Journal publications

@article{Adhikari2025,

title = {Characterizing season-long floral trajectories in cotton with low-altitude remote sensing and deep learning},

author = {Jeevan Adhikari and Daniel Petti and Deepak Vitrakoti and Wiriyanat Ployaram and Changying Li and Andrew H. Paterson},

url = {https://nph.onlinelibrary.wiley.com/doi/abs/10.1002/ppp3.10644},

doi = {https://doi.org/10.1002/ppp3.10644},

year  = {2025},

date = {2025-01-01},

journal = {PLANTS, PEOPLE, PLANET},

volume = {n/a},

number = {n/a},

abstract = {Societal Impact Statement Plant breeding is a critical tool for increasing the productivity, climate resilience, and sustainability of agriculture, but current phenotyping methods are a bottleneck due to the amount of human labor involved. Here, we demonstrate high-throughput phenotyping with an unmanned aerial vehicle (UAV) to analyze the season-long flowering pattern in cotton, subsequently mapping relevant genetic factors underpinning the trait. Season-long flowering is a complex trait, with implications for adaptation of perennials to specific environments. We believe our approach can improve the speed and efficacy of breeding for a variety of woody perennials. Summary Many perennial plants make important contributions to agroeconomies and agroecosystems but have complex architecture and/or long flowering duration that hinders measurement and selection. Iteratively tracking productivity over a long flowering/fruiting season may permit the identification of genetic factors conferring different reproductive strategies that might be successful in different environments, ranging from rapid early maturation that avoids stresses, to late maturation that utilizes the full seasonal duration to maximize productivity. In cotton, a perennial plant that is generally cultivated as an annual crop, we apply aerial imagery and deep learning methods to novel and stable genetic stocks, identifying genetic factors influencing the duration and rate of fruiting. Our phenotyping method was able to identify 24 QTLs that affect flowering behavior in cotton. A total of five of these corresponded to previously identified QTLs from other studies. While these factors may have different relationships with crop productivity and quality in different environments, their determination adds potentially important information to breeding decisions. With transfer learning of the deep learning models, this approach could be applied widely, potentially improving gains from selection in diverse perennial shrubs and trees essential to sustainable agricultural intensification.},

keywords = {cotton, deep learning, flowering time, genome mapping, High-throughput phenotyping, remote sensing, unmanned aerial vehicles},

pubstate = {published},

tppubtype = {article}

}

@article{Saeed2025,

title = {3D neural architecture search to optimize segmentation of plant parts},

author = {Farah Saeed and Chenjiao Tan and Tianming Liu and Changying Li},

url = {https://www.sciencedirect.com/science/article/pii/S2772375525000103},

doi = {https://doi.org/10.1016/j.atech.2025.100776},

issn = {2772-3755},

year  = {2025},

date = {2025-01-01},

journal = {Smart Agricultural Technology},

volume = {10},

pages = {100776},

abstract = {Accurately segmenting plant parts from imagery is vital for improving crop phenotypic traits. However, current 3D deep learning models for segmentation in point cloud data require specific network architectures that are usually manually designed, which is both tedious and suboptimal. To overcome this issue, a 3D neural architecture search (NAS) was performed in this study to optimize cotton plant part segmentation. The search space was designed using Point Voxel Convolution (PVConv) as the basic building block of the network. The NAS framework included a supernetwork with weight sharing and an evolutionary search to find optimal candidates, with three surrogate learners to predict mean IoU, latency, and memory footprint. The optimal candidate searched from the proposed method consisted of five PVConv layers with either 32 or 512 output channels, achieving mean IoU and accuracy of over 90 % and 96 %, respectively, and outperforming manually designed architectures. Additionally, the evolutionary search was updated to search for architectures satisfying memory and time constraints, with searched architectures achieving mean IoU and accuracy of >84 % and 94 %, respectively. Furthermore, a differentiable architecture search (DARTS) utilizing PVConv operation was implemented for comparison, and our method demonstrated better segmentation performance with a margin of >2 % and 1 % in mean IoU and accuracy, respectively. Overall, the proposed method can be applied to segment cotton plants with an accuracy over 94 %, while adjusting to available resource constraints.},

keywords = {3D Deep learning, 3D Neural architecture search, LiDAR, Plant part segmentation, Plant phenotyping},

pubstate = {published},

tppubtype = {article}

}

@article{Tan2025,

title = {A customized density map model and segment anything model for cotton boll number, size, and yield prediction in aerial images},

author = {Chenjiao Tan and Jin Sun and Huaibo Song and Changying Li},

url = {https://www.sciencedirect.com/science/article/pii/S0168169925001711},

doi = {https://doi.org/10.1016/j.compag.2025.110065},

issn = {0168-1699},

year  = {2025},

date = {2025-01-01},

journal = {Computers and Electronics in Agriculture},

volume = {232},

pages = {110065},

abstract = {The number of cotton bolls is an important phenotyping trait not only for breeders but also for growers. It can provide information on the physiological and genetic mechanisms of plant growth and aid decision-making in crop management. However, traditional visual inspection in the field is time-consuming and laborious. With the application of drones in the agricultural domain, there is promising potential to collect data expediently. In this paper, we integrated the improved Distribution Matching for crowd Counting (DM-Count) and Segment Anything Model (SAM) to predict cotton boll number, size, and yield in aerial images. The cotton plots were first extracted from the raw aerial images using boundaries derived from orthophotos. Then, a convolutional neural network (DM-Count) was introduced as a baseline and customized by replacing the VGG19 backbone and adding a pixel loss. The customized network was first pretrained on ground images and then fine-tuned on aerial images to predict the density map, where the number and locations of cotton bolls can be obtained. The zero-shot foundation model SAM was investigated to segment cotton bolls with the point prompts provided by customized DM-Count. The respective numbers of bolls and segmented pixels were compared for seed cotton yield estimation. The experimental results showed that the customized model obtained a mean absolute error (MAE) of 1.78 per square meter and a mean absolute percentage error (MAPE) of 4.39 % on the testing dataset, with a high correlation between the predicted boll number and ground truth (R2 = 0.91). The AP50 of SAM for cotton boll segmentation was 0.63. The segmented masks were used to delineate the boll size differences among the four genotypes, and it was found that the average boll size of Pima was 452 pixels, which was significantly smaller than Acala Maxxa, UA 48 and Tamcot Sphinx. Moreover, the yield estimation using the boll number was better than that using the pixel number, with an R2 = 0.70. Combing the boll number and the pixel number can achieve a slightly higher R2 of 0.72 for yield estimation. Overall, the customized model can count cotton bolls in aerial images accurately and estimate seed cotton yield effectively, which could significantly benefit breeders in developing genotypes with high yields, as well as help growers in yield estimation and crop management.},

keywords = {Cotton boll counting, DM-Count, Point prompts, Segmentation, UAV},

pubstate = {published},

tppubtype = {article}

}

@article{Li2025,

title = {In-field blueberry fruit phenotyping with a MARS-PhenoBot and customized BerryNet},

author = {Zhengkun Li and Rui Xu and Changying Li and Patricio Munoz and Fumiomi Takeda and Bruno Leme},

url = {https://www.sciencedirect.com/science/article/pii/S0168169925001632},

doi = {https://doi.org/10.1016/j.compag.2025.110057},

issn = {0168-1699},

year  = {2025},

date = {2025-01-01},

journal = {Computers and Electronics in Agriculture},

volume = {232},

pages = {110057},

abstract = {Accurate blueberry fruit phenotyping, including yield, fruit maturity, and cluster compactness, is crucial for optimizing crop breeding and management practices. Recent advances in machine vision and deep learning have shown promising potential to automate phenotyping and replace manual sampling. This paper presented a robotic blueberry phenotyping system, called MARS-Phenobot, that collects data in the field and measures fruit-related phenotypic traits such as fruit number, maturity, and compactness. Our workflow comprised four components: a robotic multi-view imaging system for high-throughput data collection, a vision foundation model (Segment Anything Model, SAM) for mask-free data labeling, a customized BerryNet deep learning model for detecting blueberry clusters and segmenting fruit, as well as a post-processing module for estimating yield, maturity, and cluster compactness. A customized deep learning model, BerryNet, was designed for detecting fruit clusters and segmenting individual berries by integrating low-level pyramid features, rapid partial convolutional blocks, and BiFPN feature fusion. It outperformed other networks and achieved mean average precision (mAP50) of 54.9 % in cluster detection and 85.8 % in fruit segmentation with fewer parameters and fewer computation requirements. We evaluated the phenotypic traits derived from our methods and the ground truth on 26 individual blueberry plants across 17 genotypes. The results demonstrated that both the fruit count and cluster count extracted from images were strongly correlated with the yield. Integrating multi-view fruit counts enhanced yield estimation accuracy, achieving a Mean Absolute Percentage Error (MAPE) of 23.1 % and the highest R2 value of 0.73, while maturity level estimations closely aligned with manual calculations, exhibiting a Mean Absolute Error (MAE) of approximately 5 %. Furthermore, two metrics related to fruit compactness were introduced, including cluster compactness and fruit distance, which could be useful for breeders to assess the machine and hand harvestability across genotypes. Finally, we evaluated the proposed robotic blueberry fruit phenotyping pipeline on eleven blueberry genotypes, proving the potential to distinguish the high-yield, early-maturity, and loose-clustering cultivars. Our methodology provides a promising solution for automated in-field blueberry fruit phenotyping, potentially replacing labor-intensive manual sampling. Furthermore, this approach could advance blueberry breeding programs, precision management, and mechanical/robotic harvesting.},

keywords = {Blueberry phenotyping, deep learning, Fruit compactness, Maturity, Segment Anything Model (SAM), Yield},

pubstate = {published},

tppubtype = {article}

}

@article{Muller2025,

title = {Large Language Models for Agricultural Injury Surveillance},

author = {Jacob Muller and Daniel Petti and Changying Li and Serap Gorucu and Matthew Pilz and Bryan P. Weichelt},

url = {https://www.mdpi.com/2313-576X/11/1/15},

doi = {10.3390/safety11010015},

issn = {2313-576X},

year  = {2025},

date = {2025-01-01},

journal = {Safety},

volume = {11},

number = {1},

abstract = {The traditional approach to curating and disseminating information about agricultural injuries relies heavily on manual input and review, resulting in a labor-intensive process. While the unstructured nature of the material traditionally requires human reviewers, the recent proliferation of Large Language Models (LLMs) has introduced the potential for automation. This study investigates the feasibility and implications of filling the role of a human reviewer with an LLM in analyzing information about agricultural injuries from news articles and investigation reports. Multiple language models were tested for accuracy in extracting relevant incident and victim information, and these models include OpenAI’s ChatGPT 3.5 and 4 and an open-source fine-tuned version of Llama 2. To measure accuracy, each LLM was given prompts to gather relevant data from a set of randomly selected online news articles already cataloged by human reviewers, such as the use of drugs or alcohol, time of day, or other information about the victim(s). Results showed that the fine-tuned Llama2 was the most proficient model with an average accuracy of 93% and some categories reaching 100%. ChatGPT-4 also performed well with around 90% accuracy. Additionally, we found that the fine-tuned Llama2 model was somewhat proficient in coding injuries using the OIICS classification scheme, achieving 48% accuracy when predicting the first digit. Though none of the models are perfectly accurate, the methodology and results prove that LLMs are promising in streamlining workflows in order to reduce human and financial resources and increase the efficiency of data analysis.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Jiang2025,

title = {Apple tree architectural trait phenotyping with organ-level instance segmentation from point cloud},

author = {Lizhi Jiang and Changying Li and Longsheng Fu},

url = {https://www.sciencedirect.com/science/article/pii/S0168169924010998},

doi = {https://doi.org/10.1016/j.compag.2024.109708},

issn = {0168-1699},

year  = {2025},

date = {2025-01-01},

journal = {Computers and Electronics in Agriculture},

volume = {229},

pages = {109708},

abstract = {Three-dimensional (3D) plant phenotyping techniques measure organ-level traits effectively and provide detailed plant growth information to breeders. In apple tree breeding, architectural traits can determine photosynthesis efficiency and characterize the developmental stages of trees. The overall goal of this study was to develop a deep learning-based organ-level instance segmentation method to quantify the 3D architectural traits of apple trees. This study utilized PointNeXt for the semantic segmentation of apple tree point clouds, classifying them into trunks and branches, and benchmarked its performance against several competitive models, including PointNet, PointNet++, and Point Transformer V2 (PTv2). A cylinder-based constraint method was introduced to refine the semantic segmentation results. Next, the branches were identified with the density-based spatial clustering of applications with noise (DBSCAN) algorithm. The type of 3D skeleton vertices determined whether a cluster represented a single branch or multiple branches. If multiple, a graph-based technique further separated them. This study also directly applied the instance segmentation model SoftGroup++ to the apple tree point clouds and analyzed the segmentation results on the apple tree dataset. Finally, seven architectural traits of apple trees were extracted, including height, volume, and crown width of the tree, as well as height and diameter for the trunk, and length and count for the branches. The experimental results showed that the post-processed mIoU values for PointNet, PointNet++, PTv2, and PointNeXt were 0.8495, 0.8535, 0.9500, and 0.9481, respectively. The final instance segmentation results based on SoftGroup++ and PointNeXt achieved mAP_50 of 0.815 and 0.842, respectively. For traits such as tree height, trunk length and diameter, branch length, and branch count, the method based on PointNeXt achieved R2 values of 0.987, 0.788, 0.877, 0.796, and 0.934, with mean absolute percentage errors of 0.86 %, 2.17 %, 5.93 %, 10.24 %, and 13.55 %, respectively. The segmentation results of PTv2 and SoftGroup++ were also used to extract the phenotypic traits of apple trees, achieving results comparable to those of PointNeXt. The proposed method demonstrates a cost-effective and accurate approach for extracting the architectural traits of apple trees, which will benefit apple breeding programs as well as the precision management of apple orchards.},

keywords = {3D segmentation, Apple tree, Plant phenotyping, Point Transformer V2, PointNeXt, SoftGroup++},

pubstate = {published},

tppubtype = {article}

}

@article{Liu,

title = {Digital Twin/MARS-CycleGAN: Enhancing Sim-to-Real Crop/Row Detection for MARS Phenotyping Robot Using Synthetic Images},

author = {David Liu and Zhengkun Li and Zihao Wu and Changying Li},

url = {https://onlinelibrary.wiley.com/doi/abs/10.1002/rob.22473},

doi = {https://doi.org/10.1002/rob.22473},

year  = {2024},

date = {2024-12-10},

journal = {Journal of Field Robotics},

volume = {n/a},

number = {n/a},

abstract = {ABSTRACT Robotic crop phenotyping has emerged as a key technology for assessing crops' phenotypic traits at scale, which is essential for developing new crop varieties with the aim of increasing productivity and adapting to the changing climate. However, developing and deploying crop phenotyping robots faces many challenges, such as complex and variable crop shapes that complicate robotic object detection, dynamic and unstructured environments that confound robotic control, and real-time computing and managing big data that challenge robotic hardware/software. This work specifically addresses the first challenge by proposing a novel Digital Twin(DT)/MARS-CycleGAN model for image augmentation to improve our Modular Agricultural Robotic System (MARS)'s crop object detection from complex and variable backgrounds. The core idea is that in addition to the cycle consistency losses in the CycleGAN model, we designed and enforced a new DT/MARS loss in the deep learning model to penalize the inconsistency between real crop images captured by MARS and synthesized images generated by DT/MARS-CycleGAN. Therefore, the synthesized crop images closely mimic real images in terms of realism, and they are employed to fine-tune object detectors such as YOLOv8. Extensive experiments demonstrate that the new DT/MARS-CycleGAN framework significantly boosts crop/row detection performance for MARS, contributing to the field of robotic crop phenotyping. We release our code and data to the research community (https://github.com/UGA-BSAIL/DT-MARS-CycleGAN).},

keywords = {digital twin, field-based robotic phenotyping, object detection, sim-to-real transfer, zero-shot},

pubstate = {published},

tppubtype = {article}

}

@article{TAN2024233,

title = {Three-view cotton flower counting through multi-object tracking and RGB-D imagery},

author = {Chenjiao Tan and Jin Sun and Andrew H. Paterson and Huaibo Song and Changying Li},

url = {https://www.sciencedirect.com/science/article/pii/S1537511024001880},

doi = {https://doi.org/10.1016/j.biosystemseng.2024.08.010},

issn = {1537-5110},

year  = {2024},

date = {2024-01-01},

journal = {Biosystems Engineering},

volume = {246},

pages = {233-247},

abstract = {Monitoring the number of cotton flowers can provide important information for breeders to assess the flowering time and the productivity of genotypes because flowering marks the transition from vegetative growth to reproductive growth and impacts the final yield. Traditional manual counting methods are time-consuming and impractical for large-scale fields. To count cotton flowers efficiently and accurately, a multi-view multi-object tracking approach was proposed by using both RGB and depth images collected by three RGB-D cameras fixed on a ground robotic platform. The tracking-by-detection algorithm was employed to track flowers from three views simultaneously and remove duplicated counting from single views. Specifically, an object detection model (YOLOv8) was trained to detect flowers in RGB images and a deep learning-based optical flow model Recurrent All-pairs Field Transforms (RAFT) was used to estimate motion between two adjacent frames. The intersection over union and distance costs were employed to associate flowers in the tracking algorithm. Additionally, tracked flowers were segmented in RGB images and the depth of each flower was obtained from the corresponding depth image. Those flowers tracked with known depth from two side views were then projected onto the middle image coordinate using camera calibration parameters. Finally, a constrained hierarchy clustering algorithm clustered all flowers in the middle image coordinate to remove duplicated counting from three views. The results showed that the mean average precision of trained YOLOv8x was 96.4%. The counting results of the developed method were highly correlated with those counted manually with a coefficient of determination of 0.92. Besides, the mean absolute percentage error of all 25 testing videos was 6.22%. The predicted cumulative flower number of Pima cotton flowers is higher than that of Acala Maxxa, which is consistent with what breeders have observed. Furthermore, the developed method can also obtain the flower number distributions of different genotypes without laborious manual counting in the field. Overall, the three-view approach provides an efficient and effective approach to count cotton flowers from multiple views. By collecting the video data continuously, this method is beneficial for breeders to dissect genetic mechanisms of flowering time with unprecedented spatial and temporal resolution, also providing a means to discern genetic differences in fecundity, the number of flowers that result in harvestable bolls. The code and datasets used in this paper can be accessed on GitHub: https://github.com/UGA-BSAIL/Multi-view_flower_counting.},

keywords = {Depth camera, Multiple views, Optical flow, Phenotyping, YOLO},

pubstate = {published},

tppubtype = {article}

}

@article{PETTI2024109294,

title = {Graph Neural Networks for lightweight plant organ tracking},

author = {Daniel Petti and Ronghang Zhu and Sheng Li and Changying Li},

url = {https://www.sciencedirect.com/science/article/pii/S0168169924006859},

doi = {https://doi.org/10.1016/j.compag.2024.109294},

issn = {0168-1699},

year  = {2024},

date = {2024-01-01},

journal = {Computers and Electronics in Agriculture},

volume = {225},

pages = {109294},

abstract = {Many specific problems within the domain of high throughput phenotyping require the accurate localization of plant organs. To track and count plant organs, we propose GCNNMatch++, a Graph Convolutional Neural Network (GCNN) that is capable of online tracking objects from videos. Based upon the GCNNMatch tracker with an improved CensNet GNN, our end-to-end tracking approach achieves fast inference. In order to adapt this approach to flower counting, we collected a large, high-quality dataset of cotton flower videos by leveraging our custom-built MARS-X robotic platform. Specifically, our system can count cotton flowers in the field with 80% accuracy, achieving a Higher-Order Tracking Accuracy (HOTA) of 51.09 and outperforming more generic tracking methods. Without any optimization (such as employing TensorRT), our association model runs in 44 ms on a central processing unit (CPU). On appropriate hardware, our model holds promise for achieving real-time counting performance when coupled with a fast detector. Overall, our approach is useful in counting cotton flowers and other relevant plant organs for both breeding programs and yield estimation.},

keywords = {Convolutional Neural Network, Graph Neural Network, High-throughput phenotyping, Machine vision, Multi-Object Tracking},

pubstate = {published},

tppubtype = {article}

}

@article{10.3389/fpls.2024.1436120,

title = {Cotton morphological traits tracking through spatiotemporal registration of terrestrial laser scanning time-series data},

author = {Javier Rodriguez-Sanchez and John L. Snider and Kyle Johnsen and Changying Li},

url = {https://www.frontiersin.org/journals/plant-science/articles/10.3389/fpls.2024.1436120},

doi = {10.3389/fpls.2024.1436120},

issn = {1664-462X},

year  = {2024},

date = {2024-01-01},

urldate = {2024-01-01},

journal = {Frontiers in Plant Science},

volume = {15},

abstract = {<p>Understanding the complex interactions between genotype-environment dynamics is fundamental for optimizing crop improvement. However, traditional phenotyping methods limit assessments to the end of the growing season, restricting continuous crop monitoring. To address this limitation, we developed a methodology for spatiotemporal registration of time-series 3D point cloud data, enabling field phenotyping over time for accurate crop growth tracking. Leveraging multi-scan terrestrial laser scanning (TLS), we captured high-resolution 3D LiDAR data in a cotton breeding field across various stages of the growing season to generate four-dimensional (4D) crop models, seamlessly integrating spatial and temporal dimensions. Our registration procedure involved an initial pairwise terrain-based matching for rough alignment, followed by a bird’s-eye view adjustment for fine registration. Point clouds collected throughout nine sessions across the growing season were successfully registered both spatially and temporally, with average registration errors of approximately 3 cm. We used the generated 4D models to monitor canopy height (CH) and volume (CV) for eleven cotton genotypes over two months. The consistent height reference established via our spatiotemporal registration process enabled precise estimations of CH (<italic>R</italic>^{2} = 0.95},

keywords = {agricultural robot, LiDAR, phenotyping robot, robotics},

pubstate = {published},

tppubtype = {article}

}

@article{https://doi.org/10.1002/aisy.202300233,

title = {Intelligent Soft Robotic Grippers for Agricultural and Food Product Handling: A Brief Review with a Focus on Design and Control},

author = {Yuxuan Liu and Jixin Hou and Changying Li and Xianqiao Wang},

url = {https://onlinelibrary.wiley.com/doi/abs/10.1002/aisy.202300233},

doi = {https://doi.org/10.1002/aisy.202300233},

year  = {2023},

date = {2023-10-08},

journal = {Advanced Intelligent Systems},

volume = {n/a},

number = {n/a},

pages = {2300233},

abstract = {Advances in material sciences, control algorithms, and manufacturing techniques have facilitated rapid progress in soft grippers, propelling their adoption in various fields. In this review article, a comprehensive overview of the design and control aspects of intelligent soft robotic grippers tailored specifically for agricultural product handling is provided. Soft grippers have emerged as a promising solution for handling delicate and fragile objects. In this article, the recent progress in various gripper design, including fluidic and mechanical grippers, is elucidated and the role of advanced control approaches in enabling intelligent functions, such as object classification and grasping condition evaluation, is explored. Moreover, the challenges and opportunities pertaining to implementation of soft grippers in the agricultural industry are thoroughly discussed. While most demonstrations of soft grippers and their control strategies remain at the experimental stage, in this article, it is aimed to provide insights into the potential applications of soft grippers in agricultural product handling, thereby inspiring future research in this field.},

keywords = {agricultural robots, end-effectors, grasping, grippers, harvesting robots, pick and place, sensors},

pubstate = {published},

tppubtype = {article}

}

@article{lu2023agi,

title = {AGI for Agriculture},

author = {Guoyu Lu and Sheng Li and Gengchen Mai and Jin Sun and Dajiang Zhu and Lilong Chai and Haijian Sun and Xianqiao Wang and Haixing Dai and Ninghao Liu and Rui Xu and Daniel Petti and Changying Li and Tianming Liu and Changying Li},

url = {https://arxiv.org/abs/2304.06136},

year  = {2023},

date = {2023-04-12},

urldate = {2023-01-01},

abstract = {Artificial General Intelligence (AGI) is poised to revolutionize a variety of sectors, including healthcare, finance, transportation, and education. Within healthcare, AGI is being utilized to analyze clinical medical notes, recognize patterns in patient data, and aid in patient management. Agriculture is another critical sector that impacts the lives of individuals worldwide. It serves as a foundation for providing food, fiber, and fuel, yet faces several challenges, such as climate change, soil degradation, water scarcity, and food security. AGI has the potential to tackle these issues by enhancing crop yields, reducing waste, and promoting sustainable farming practices. It can also help farmers make informed decisions by leveraging real-time data, leading to more efficient and effective farm management. This paper delves into the potential future applications of AGI in agriculture, such as agriculture image processing, natural language processing (NLP), robotics, knowledge graphs, and infrastructure, and their impact on precision livestock and precision crops. By leveraging the power of AGI, these emerging technologies can provide farmers with actionable insights, allowing for optimized decision-making and increased productivity. The transformative potential of AGI in agriculture is vast, and this paper aims to highlight its potential to revolutionize the industry. },

keywords = {3D reconstruction, AGI, Deep convolutional neural network, deep learning, High-throughput phenotyping, object detection, phenotyping robot, robotics},

pubstate = {published},

tppubtype = {article}

}

@article{Tan2023a,

title = {Anchor-free deep convolutional neural network for tracking and counting cotton seedlings and flowers},

author = {Chenjiao Tan and Changying Li and Dongjian He and Huaibo Song},

url = {https://www.sciencedirect.com/science/article/pii/S0168169923007470},

doi = {https://doi.org/10.1016/j.compag.2023.108359},

issn = {0168-1699},

year  = {2023},

date = {2023-01-01},

urldate = {2023-01-01},

journal = {Computers and Electronics in Agriculture},

volume = {215},

pages = {108359},

abstract = {Accurate counting of plants and their organs in natural environments is essential for breeders and growers. For breeders, counting plants during the seedling stage aids in selecting genotypes with superior emergence rates, while for growers, it informs decisions about potential replanting. Meanwhile, counting specific plant organs, such as flowers, forecasts yields for different genotypes, offering insights into production levels. The overall goal of this study was to investigate a deep convolutional neural network-based tracking method, CenterTrack, for cotton seedling and flower counting from video frames. The network is extended from a customized CenterNet, which is an anchor-free object detector. CenterTrack predicts the detections of the current frame and displacements of detections between the previous frame and the current frame, which are used to associate the same object in consecutive frames. The modified CenterNet detector achieved high accuracy on both seedling and flower datasets with an overall AP50 of 0.962. The video tracking hyperparameters were optimized for each dataset using orthogonal tests. Experimental results showed that seedling and flower counts with optimized hyperparameters highly correlated with those of manual counts (R2 = 0.98 andR2 = 0.95) and the mean relative errors of 75 cotton seedling testing videos and 50 flower testing videos were 5.5 % and 10.8 %, respectively. An average counting speed of 20.4 frames per second was achieved with an input resolution of 1920 × 1080 pixels for both seedling and flower videos. The anchor-free deep convolution neural network-based tracking method provides automatic tracking and counting in video frames, which will significantly benefit plant breeding and crop management.},

keywords = {Anchor free, CNN, Counting, Deep convolutional network, High-throughput phenotyping, object detection, Plant and plant organ, Tracking},

pubstate = {published},

tppubtype = {article}

}

@article{KAUR2023107868,

title = {Variation in thermotolerance of photosystem II energy trapping, intersystem electron transport, and photosystem I electron acceptor reduction for diverse cotton genotypes},

author = {Navneet Kaur and John L. Snider and Andrew H. Paterson and Timothy L. Grey and Changying Li and Gurpreet Virk and Ved Parkash},

url = {https://www.sciencedirect.com/science/article/pii/S0981942823003790},

doi = {https://doi.org/10.1016/j.plaphy.2023.107868},

issn = {0981-9428},

year  = {2023},

date = {2023-01-01},

urldate = {2023-01-01},

journal = {Plant Physiology and Biochemistry},

volume = {201},

pages = {107868},

abstract = {Cotton breeding programs have focused on agronomically-desirable traits. Without targeted selection for tolerance to high temperature extremes, cotton will likely be more vulnerable to environment-induced yield loss. Recently-developed methods that couple chlorophyll fluorescence induction measurements with temperature response experiments could be used to identify genotypic variation in photosynthetic thermotolerance of specific photosynthetic processes for field-grown plants. It was hypothesized that diverse cotton genotypes would differ significantly in photosynthetic thermotolerance, specific thylakoid processes would exhibit differential sensitivities to high temperature, and that the most heat tolerant process would exhibit substantial genotypic variation in thermotolerance plasticity. A two-year field experiment was conducted at Tifton and Athens, Georgia, USA. Experiments included 10 genotypes in 2020 and 11 in 2021. Photosynthetic thermotolerance for field-collected leaf samples was assessed by determining the high temperature threshold resulting in a 15% decline in photosynthetic efficiency (T15) for energy trapping by photosystem II (ΦPo), intersystem electron transport (ΦEo), and photosystem I end electron acceptor reduction (ΦRo). Significant genotypic variation in photosynthetic thermotolerance was observed, but the response was dependent on location and photosynthetic parameter assessed. ΦEo was substantially more heat sensitive than ΦPo or ΦRo. Significant genotypic variation in thermotolerance plasticity of ΦEo was also observed. Identifying the weakest link in photosynthetic tolerance to high temperature will facilitate future selection efforts by focusing on the most heat-susceptible processes. Given the genotypic differences in environmental plasticity observed here, future research should evaluate genotypic variation in acclimation potential in controlled environments.},

keywords = {Photosynthesis, Thermotolerance, Thylakoid reactions},

pubstate = {published},

tppubtype = {article}

}

@article{https://doi.org/10.1002/csc2.21028,

title = {Unoccupied aerial systems imagery for phenotyping in cotton, maize, soybean, and wheat breeding},

author = {Andrew W. Herr and Alper Adak and Matthew E. Carroll and Dinakaran Elango and Soumyashree Kar and Changying Li and Sarah E. Jones and Arron H. Carter and Seth C. Murray and Andrew Paterson and Sindhuja Sankaran and Arti Singh and Asheesh K. Singh},

url = {https://acsess.onlinelibrary.wiley.com/doi/abs/10.1002/csc2.21028},

doi = {https://doi.org/10.1002/csc2.21028},

year  = {2023},

date = {2023-01-01},

urldate = {2023-01-01},

journal = {Crop Science},

volume = {63},

number = {4},

pages = {1722-1749},

abstract = {Abstract High-throughput phenotyping (HTP) with unoccupied aerial systems (UAS), consisting of unoccupied aerial vehicles (UAV; or drones) and sensor(s), is an increasingly promising tool for plant breeders and researchers. Enthusiasm and opportunities from this technology for plant breeding are similar to the emergence of genomic tools ∼30 years ago, and genomic selection more recently. Unlike genomic tools, HTP provides a variety of strategies in implementation and utilization that generate big data on the dynamic nature of plant growth formed by temporal interactions between growth and environment. This review lays out strategies deployed across four major staple crop species: cotton (Gossypium hirsutum L.), maize (Zea mays L.), soybean (Glycine max L.), and wheat (Triticum aestivum L.). Each crop highlighted in this review demonstrates how UAS-collected data are employed to automate and improve estimation or prediction of objective phenotypic traits. Each crop section includes four major topics: (a) phenotyping of routine traits, (b) phenotyping of previously infeasible traits, (c) sample cases of UAS application in breeding, and (d) implementation of phenotypic and phenomic prediction and selection. While phenotyping of routine agronomic and productivity traits brings advantages in time and resource optimization, the most potentially beneficial application of UAS data is in collecting traits that were previously difficult or impossible to quantify, improving selection efficiency of important phenotypes. In brief, UAS sensor technology can be used for measuring abiotic stress, biotic stress, crop growth and development, as well as productivity. These applications and the potential implementation of machine learning strategies allow for improved prediction, selection, and efficiency within breeding programs, making UAS HTP a potentially indispensable asset.},

keywords = {agricultural robot, High-throughput phenotyping, review},

pubstate = {published},

tppubtype = {article}

}

@article{Saeed2023,

title = {Cotton plant part 3D segmentation and architectural trait extraction using point voxel convolutional neural networks},

author = {Farah Saeed and Shangpeng Sun and Javier Rodriguez-Sanchez and John Snider and Tianming Liu and Changying Li},

url = {https://doi.org/10.1186/s13007-023-00996-1},

doi = {10.1186/s13007-023-00996-1},

issn = {1746-4811},

year  = {2023},

date = {2023-01-01},

urldate = {2023-01-01},

journal = {Plant Methods},

volume = {19},

number = {1},

pages = {33},

abstract = {Plant architecture can influence crop yield and quality. Manual extraction of architectural traits is, however, time-consuming, tedious, and error prone. The trait estimation from 3D data addresses occlusion issues with the availability of depth information while deep learning approaches enable learning features without manual design. The goal of this study was to develop a data processing workflow by leveraging 3D deep learning models and a novel 3D data annotation tool to segment cotton plant parts and derive important architectural traits.},

keywords = {deep learning, High-throughput phenotyping, LiDAR, machine learning},

pubstate = {published},

tppubtype = {article}

}

@article{nokey,

title = {A review of field-based high-throughput phenotyping systems: focusing on ground robots},

author = {Rui Xu and Changying Li},

url = {https://spj.sciencemag.org/journals/plantphenomics/2022/9760269/},

doi = {https://doi.org/10.34133/2022/9760269.},

year  = {2022},

date = {2022-06-18},

urldate = {2022-06-18},

journal = {Plant Phenomics},

volume = {2022},

number = {Article ID 9760269},

pages = {20},

keywords = {agricultural robot, High-throughput phenotyping, phenotyping robot, review, robotics},

pubstate = {published},

tppubtype = {article}

}

@article{nokey,

title = {Cotton yield estimation from aerial imagery using machine learning approaches},

author = {Javier Rodriguez-Sanchez and Changying Li and Andrew Paterson},

url = {https://www.frontiersin.org/articles/10.3389/fpls.2022.870181/full},

year  = {2022},

date = {2022-04-01},

urldate = {2022-04-01},

journal = {Frontiers in Plant Science},

volume = {13},

keywords = {High-throughput phenotyping, machine learning},

pubstate = {published},

tppubtype = {article}

}

@article{nokey,

title = {Dairy cow lameness detection using a back curvature feature},

author = {Bo Jiang and Huaibo Song and Han Wang and Changying Li},

url = {https://www.sciencedirect.com/science/article/pii/S0168169922000461},

doi = {https://doi.org/10.1016/j.compag.2022.106729},

year  = {2022},

date = {2022-02-01},

urldate = {2022-02-01},

journal = {Computers and Electronics in Agriculture},

volume = {194},

number = {106729},

issue = {106729},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Adke2022,

title = {Supervised and Weakly Supervised Deep Learning for Segmentation and Counting of Cotton Bolls Using Proximal Imagery},

author = {Shrinidhi Adke and Changying Li and Khaled M. Rasheed and Frederick W. Maier},

url = {https://www.mdpi.com/1424-8220/22/10/3688},

doi = {10.3390/s22103688},

issn = {1424-8220},

year  = {2022},

date = {2022-01-01},

urldate = {2022-01-01},

journal = {Sensors},

volume = {22},

number = {10},

abstract = {The total boll count from a plant is one of the most important phenotypic traits for cotton breeding and is also an important factor for growers to estimate the final yield. With the recent advances in deep learning, many supervised learning approaches have been implemented to perform phenotypic trait measurement from images for various crops, but few studies have been conducted to count cotton bolls from field images. Supervised learning models require a vast number of annotated images for training, which has become a bottleneck for machine learning model development. The goal of this study is to develop both fully supervised and weakly supervised deep learning models to segment and count cotton bolls from proximal imagery. A total of 290 RGB images of cotton plants from both potted (indoor and outdoor) and in-field settings were taken by consumer-grade cameras and the raw images were divided into 4350 image tiles for further model training and testing. Two supervised models (Mask R-CNN and S-Count) and two weakly supervised approaches (WS-Count and CountSeg) were compared in terms of boll count accuracy and annotation costs. The results revealed that the weakly supervised counting approaches performed well with RMSE values of 1.826 and 1.284 for WS-Count and CountSeg, respectively, whereas the fully supervised models achieve RMSE values of 1.181 and 1.175 for S-Count and Mask R-CNN, respectively, when the number of bolls in an image patch is less than 10. In terms of data annotation costs, the weakly supervised approaches were at least 10 times more cost efficient than the supervised approach for boll counting. In the future, the deep learning models developed in this study can be extended to other plant organs, such as main stalks, nodes, and primary and secondary branches. Both the supervised and weakly supervised deep learning models for boll counting with low-cost RGB images can be used by cotton breeders, physiologists, and growers alike to improve crop breeding and yield estimation.},

keywords = {deep learning, machine learning},

pubstate = {published},

tppubtype = {article}

}

@article{TAN2022106683,

title = {Towards real-time tracking and counting of seedlings with a one-stage detector and optical flow},

author = {Chenjiao Tan and Changying Li and Dongjian He and Huaibo Song},

url = {https://www.sciencedirect.com/science/article/pii/S0168169921007006},

doi = {https://doi.org/10.1016/j.compag.2021.106683},

issn = {0168-1699},

year  = {2022},

date = {2022-01-01},

urldate = {2022-01-01},

journal = {Computers and Electronics in Agriculture},

volume = {193},

pages = {106683},

abstract = {The population of crop seedlings is important for breeders and growers to evaluate the emergence rate of different cultivars and the necessity of replanting, but manual counting of plant seedlings is time-consuming and tedious. Building upon our prior work, we advanced the cotton seedling tracking method by incorporating a one-stage object detection deep neural network and optical flow to improve tracking speed and counting accuracy. Videos of cotton seedlings were captured using consumer-grade video cameras from the top view. You Only Look Once Version 4 (YOLOv4), a one-stage object detection network, was trained to detect cotton seedlings in each frame and to generate bounding boxes. To associate the same seedlings between adjacent frames, an optical flow-based tracking method was adopted to estimate camera motions. By comparing the positions of bounding boxes predicted by optical flow and detected by the YOLOv4 network in the same frame, the number of cotton seedlings was updated. The trained YOLOv4 model achieved high accuracy under conditions of occlusions, blurry images, complex backgrounds, and extreme illuminations. The F1 score of the final detection model was 0.98 and the average precision was 99.12%. Important tracking metrics were compared to evaluate the tracking performance. The Multiple-Object Tracking Accuracy (MOTA) and ID switch of the proposed tracking method were 72.8% and 0.1%, respectively. Counting results showed that the relative error of all testing videos was 3.13%. Compared with the Kalman filter and particle filter-based methods, our optical flow-based method generated fewer errors on testing videos because of higher accuracy of motion estimation. Compared with our previous work, the RMSE of the optical flow-based method decreased by 0.54 and the counting speed increased from 2.5 to 10.8 frames per second. The counting speed can reach 16.6 frames per second if the input resolution was reduced to 1280 × 720 pixels with an only 0.45% reduction in counting accuracy. The proposed method provides an automatic and near real-time tracking approach for counting of multiple cotton seedlings in video frames with improved speed and accuracy, which will benefit plant breeding and precision crop management.},

keywords = {Cotton seedling, Counting, Deep convolutional neural network, deep learning, machine learning, object detection, Optical flow},

pubstate = {published},

tppubtype = {article}

}

@article{https://doi.org/10.1002/rob.22056,

title = {A modular agricultural robotic system (MARS) for precision farming: Concept and implementation},

author = {Rui Xu and Changying Li},

url = {https://onlinelibrary.wiley.com/doi/abs/10.1002/rob.22056},

doi = {https://doi.org/10.1002/rob.22056},

year  = {2022},

date = {2022-01-01},

journal = {Journal of Field Robotics},

volume = {39},

number = {4},

pages = {387-409},

abstract = {Abstract Increasing global population, climate change, and shortage of labor pose significant challenges for meeting the global food and fiber demand, and agricultural robots offer a promising solution to these challenges. This paper presents a new robotic system architecture and the resulting modular agricultural robotic system (MARS) that is an autonomous, multi-purpose, and affordable robotic platform for in-field plant high throughput phenotyping and precision farming. There are five essential hardware modules (wheel module, connection module, robot controller, robot frame, and power module) and three optional hardware modules (actuation module, sensing module, and smart attachment). Various combinations of the hardware modules can create different robot configurations for specific agricultural tasks. The software was designed using the Robot Operating System (ROS) with three modules: control module, navigation module, and vision module. A robot localization method using dual Global Navigation Satellite System antennas was developed. Two line-following algorithms were implemented as the local planner for the ROS navigation stack. Based on the MARS design concept, two MARS designs were implemented: a low-cost, lightweight robotic system named MARS mini and a heavy-duty robot named MARS X. The autonomous navigation of both MARS X and mini was evaluated at different traveling speeds and payload levels, confirming satisfactory performances. The MARS X was further tested for its performance and navigation accuracy in a crop field, achieving a high accuracy over a 537 m long path with only 15% of the path having an error larger than 0.05 m. The MARS mini and MARS X were shown to be useful for plant phenotyping in two field tests. The modular design makes the robots easily adaptable to different agricultural tasks and the low-cost feature makes it affordable for researchers and growers.},

keywords = {agricultural robot, High-throughput phenotyping, phenotyping robot},

pubstate = {published},

tppubtype = {article}

}

@article{Petti2022,

title = {Weakly-supervised learning to automatically count cotton flowers from aerial imagery},

author = {Daniel Petti and Changying Li},

url = {https://www.sciencedirect.com/science/article/pii/S0168169922000515},

doi = {https://doi.org/10.1016/j.compag.2022.106734},

issn = {0168-1699},

year  = {2022},

date = {2022-01-01},

urldate = {2022-01-01},

journal = {Computers and Electronics in Agriculture},

volume = {194},

pages = {106734},

abstract = {Counting plant flowers is a common task with applications for estimating crop yields and selecting favorable genotypes. Typically, this requires a laborious manual process, rendering it impractical to obtain accurate flower counts throughout the growing season. The model proposed in this study uses weak supervision, based on Convolutional Neural Networks (CNNs), which automates such a counting task for cotton flowers using imagery collected from an unmanned aerial vehicle (UAV). Furthermore, the model is trained using Multiple Instance Learning (MIL) in order to reduce the required amount of annotated data. MIL is a binary classification task in which any image with at least one flower falls into the positive class, and all others are negative. In the process, a novel loss function was developed that is designed to improve the performance of image-processing models that use MIL. The model is trained on a large dataset of cotton plant imagery which was collected over several years and will be made publicly available. Additionally, an active-learning-based approach is employed in order to generate the annotations for the dataset while minimizing the required amount of human intervention. Despite having minimal supervision, the model still demonstrates good performance on the testing dataset. Multiple models were tested with different numbers of parameters and input sizes, achieving a minimum average absolute count error of 2.43. Overall, this study demonstrates that a weakly-supervised model is a promising method for solving the flower counting problem while minimizing the human labeling effort.},

keywords = {Active learning, deep learning, High-throughput phenotyping, machine learning, Multiple-instance learning, Object counting},

pubstate = {published},

tppubtype = {article}

}

@article{NI2021297,

title = {Three-dimensional photogrammetry with deep learning instance segmentation to extract berry fruit harvestability traits},

author = {Xueping Ni and Changying Li and Huanyu Jiang and Fumiomi Takeda},

url = {https://www.sciencedirect.com/science/article/pii/S0924271620303178},

doi = {https://doi.org/10.1016/j.isprsjprs.2020.11.010},

issn = {0924-2716},

year  = {2021},

date = {2021-01-01},

urldate = {2021-01-01},

journal = {ISPRS Journal of Photogrammetry and Remote Sensing},

volume = {171},

pages = {297-309},

abstract = {Fruit cluster characteristics such as compactness, maturity, berry number, and berry size, are important phenotypic traits associated with harvestability and yield of blueberry genotypes and can be used to monitor berry development and improve crop management. The goal of this study was to develop a complete framework of 3D segmentation for individual blueberries as they develop in clusters and to extract blueberry cluster traits. To achieve this goal, an image-capturing system was developed to capture blueberry images to facilitate 3D reconstruction and a 2D-3D projection-based photogrammetric pipeline was proposed to extract berry cluster traits. The reconstruction was performed for four southern highbush blueberry cultivars (‘Emerald’, ‘Farthing’, ‘Meadowlark’ and ‘Star’) with 10 cluster samples for each cultivar based on photogrammetry. A minimum bounding box was created to surround a 3D blueberry cluster to calculate compactness as the ratio of berry volume and minimum bounding box volume. Mask R-CNN was used to segment individual blueberries with the maturity property from 2D images and the instance masks were projected onto 3D point clouds to establish 2D-3D correspondences. The developed trait extraction algorithm was used to segment individual 3D blueberries to obtain berry number, individual berry volume, and berry maturity. Berry maturity was used to calculate cluster maturity as the ratio of the mature berry (blue colored fruit) number and the total berry (blue, reddish, and green colored fruit) number comprising the cluster. The accuracy of determining the fruit number in a cluster is 97.3%. The linear regression for cluster maturity has a R2 of 0.908 with a RMSE of 0.068. The cluster berry volume has a RMSE of 2.92 cm3 compared with the ground truth, indicating that the individual berry volume has an error of less than 0.292 cm3 for clusters with a berry number greater than 10. The statistical analyses of the traits for the four cultivars reveals that, in the middle of April, ‘Emerald’ and ‘Farthing’ were more compact than ‘Meadowlark’ and ‘Star’, and the mature berry volume of ‘Farthing’ was greater than ‘Emerald’ and ‘Meadowlark’, while ‘Star’ had the smallest mature berry size. This study develops an effective method based on 3D photogrammetry and 2D instance segmentation that can determine blueberry cluster traits accurately from a large number of samples and can be used for fruit development monitoring, yield estimation, and harvest time prediction.},

keywords = {2D-3D projection, 3D reconstruction, Blueberry traits, deep learning, machine learning, mask R-CNN},

pubstate = {published},

tppubtype = {article}

}

@article{Adke2021,

title = {Instance Segmentation to Estimate Consumption of Corn Ears by Wild Animals for GMO Preference Tests},

author = {Shrinidhi Adke and Karl Haro von Mogel and Yu Jiang and Changying Li},

url = {https://www.frontiersin.org/article/10.3389/frai.2020.593622},

doi = {10.3389/frai.2020.593622},

issn = {2624-8212},

year  = {2021},

date = {2021-01-01},

journal = {Frontiers in Artificial Intelligence},

volume = {3},

abstract = {The Genetically Modified (GMO) Corn Experiment was performed to test the hypothesis that wild animals prefer Non-GMO corn and avoid eating GMO corn, which resulted in the collection of complex image data of consumed corn ears. This study develops a deep learning-based image processing pipeline that aims to estimate the consumption of corn by identifying corn and its bare cob from these images, which will aid in testing the hypothesis in the GMO Corn Experiment. Ablation uses mask regional convolutional neural network (Mask R-CNN) for instance segmentation. Based on image data annotation, two approaches for segmentation were discussed: identifying whole corn ears and bare cob parts with and without corn kernels. The Mask R-CNN model was trained for both approaches and segmentation results were compared. Out of the two, the latter approach, i.e., without the kernel, was chosen to estimate the corn consumption because of its superior segmentation performance and estimation accuracy. Ablation experiments were performed with the latter approach to obtain the best model with the available data. The estimation results of these models were included and compared with manually labeled test data with R^{2} = 0.99 which showed that use of the Mask R-CNN model to estimate corn consumption provides highly accurate results, thus, allowing it to be used further on all collected data and help test the hypothesis of the GMO Corn Experiment. These approaches may also be applied to other plant phenotyping tasks (e.g., yield estimation and plant stress quantification) that require instance segmentation.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Virk2021,

title = {Cotton Emergence and Yield Response to Planter Depth and Downforce Settings in Different Soil Moisture Conditions},

author = {Simerjeet Virk and Wesley Porter and John Snider and Glen Rains and Changying Li and Yangxuan Liu},

url = {https://www.mdpi.com/2624-7402/3/2/22},

doi = {10.3390/agriengineering3020022},

issn = {2624-7402},

year  = {2021},

date = {2021-01-01},

journal = {AgriEngineering},

volume = {3},

number = {2},

pages = {323--338},

abstract = {US cotton producers are motivated to optimize planter performance to ensure timely and uniform stand establishment early in the season, especially when planting in sub-optimal field conditions. Field studies were conducted in 2017, 2018 and 2019 to evaluate the effect of seeding depth and planter downforce on crop emergence and yield in cotton planted in different soil moisture conditions. Field conditions representative of dry, normal and wet soil moisture conditions were attained by applying 0, 1.27 and 2.54 cm of irrigation within the same field. Two cotton cultivars (representing a small-seeded and a large-seeded cultivar, 9259–10,582 and 11,244–14,330 seeds kg−1, respectively), were planted at seeding depths of 1.3, 2.5 and 3.8 cm with each seeding depth paired with three different planter downforces of 0, 445 and 890 N in each block. Cotton was planted in plots that measured 3.66 m (four-rows) wide by 10.67 m long. Results indicated that crop emergence was affected by the seeding depth across most field conditions and higher crop emergence was observed in the large-seeded cultivar at 1.3 and 3.8 cm seeding depths in dry and wet field conditions, respectively. Lint yield was also higher for the large-seeded cultivar at the 3.8 cm seeding depth across all field conditions in 2017, and in dry field conditions in 2018. Planter downforce effect on crop emergence varied among the cultivars where the large-seeded cultivar exhibited higher crop emergence than the small-seeded cultivar at 445 and 890 N downforce. Planter downforce of 445 N yielded greater than the 0 and 890 N treatment in dry field conditions in 2017. The study results suggest that matching planter depth and downforce settings for prevalent soil moisture conditions at planting along with appropriate cultivar selection can help in achieving optimal emergence and yield in cotton.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{SUN2021106276,

title = {High resolution 3D terrestrial LiDAR for cotton plant main stalk and node detection},

author = {Shangpeng Sun and Changying Li and Peng W. Chee and Andrew H. Paterson and Cheng Meng and Jingyi Zhang and Ping Ma and Jon S. Robertson and Jeevan Adhikari},

url = {https://www.sciencedirect.com/science/article/pii/S0168169921002933},

doi = {https://doi.org/10.1016/j.compag.2021.106276},

issn = {0168-1699},

year  = {2021},

date = {2021-01-01},

urldate = {2021-01-01},

journal = {Computers and Electronics in Agriculture},

volume = {187},

pages = {106276},

abstract = {Dense three-dimensional point clouds provide opportunities to retrieve detailed characteristics of plant organ-level phenotypic traits, which are helpful to better understand plant architecture leading to its improvements via new plant breeding approaches. In this study, a high-resolution terrestrial LiDAR was used to acquire point clouds of plants under field conditions, and a data processing pipeline was developed to detect plant main stalks and nodes, and then to extract two phenotypic traits including node number and main stalk length. The proposed method mainly consisted of three steps: first, extract skeletons from original point clouds using a Laplacian-based contraction algorithm; second, identify the main stalk by converting a plant skeleton point cloud to a graph; and third, detect nodes by finding the intersection between the main stalk and branches. Main stalk length was calculated by accumulating the distance between two adjacent points from the lowest to the highest point of the main stalk. Experimental results based on 26 plants showed that the proposed method could accurately measure plant main stalk length and detect nodes; the average R2 and mean absolute percentage error were 0.94 and 4.3% for the main stalk length measurements and 0.7 and 5.1% for node counting, respectively, for point numbers between 80,000 and 150,000 for each plant. Three-dimensional point cloud-based high throughput phenotyping may expedite breeding technologies to improve crop production.},

keywords = {High-throughput phenotyping, LiDAR},

pubstate = {published},

tppubtype = {article}

}

@article{Xu2021,

title = {Development and Testing of a UAV-Based Multi-Sensor System for Plant Phenotyping and Precision Agriculture},

author = {Rui Xu and Changying Li and Sergio Bernardes},

url = {https://www.mdpi.com/2072-4292/13/17/3517},

doi = {10.3390/rs13173517},

issn = {2072-4292},

year  = {2021},

date = {2021-01-01},

urldate = {2021-01-01},

journal = {Remote Sensing},

volume = {13},

number = {17},

abstract = {Unmanned aerial vehicles have been used widely in plant phenotyping and precision agriculture. Several critical challenges remain, however, such as the lack of cross-platform data acquisition software system, sensor calibration protocols, and data processing methods. This paper developed an unmanned aerial system that integrates three cameras (RGB, multispectral, and thermal) and a LiDAR sensor. Data acquisition software supporting data recording and visualization was implemented to run on the Robot Operating System. The design of the multi-sensor unmanned aerial system was open sourced. A data processing pipeline was proposed to preprocess the raw data and to extract phenotypic traits at the plot level, including morphological traits (canopy height, canopy cover, and canopy volume), canopy vegetation index, and canopy temperature. Protocols for both field and laboratory calibrations were developed for the RGB, multispectral, and thermal cameras. The system was validated using ground data collected in a cotton field. Temperatures derived from thermal images had a mean absolute error of 1.02 °C, and canopy NDVI had a mean relative error of 6.6% compared to ground measurements. The observed error for maximum canopy height was 0.1 m. The results show that the system can be useful for plant breeding and precision crop management.},

keywords = {agricultural robot, High-throughput phenotyping, robotics},

pubstate = {published},

tppubtype = {article}

}

@article{adke2020instane,

title = {Instance Segmentation to Estimate Consumption of Corn Ears by Wild Animals for GMO Preference Tests },

author = {S. Adke and K.H. Von Mogel and Y. Jiang and C. Li},

url = {https://www.frontiersin.org/articles/10.3389/frai.2020.593622/abstract},

year  = {2020},

date = {2020-12-30},

urldate = {2020-12-30},

journal = {Frontiers in Artificial Intelligence},

volume = {3},

number = {119},

keywords = {deep learning, machine learning, mask R-CNN},

pubstate = {published},

tppubtype = {article}

}

@article{jiang2020deepflower,

title = {DeepFlower: a deep learning-based approach to characterize flowering patterns of cotton plants in the field},

author = {Y. Jiang and C. Li and R. Xu and S. Sun and J. S. Robertson and A.H. Paterson},

url = {https://plantmethods.biomedcentral.com/articles/10.1186/s13007-020-00698-y

https://doi.org/10.1186/s13007-020-00698-y},

year  = {2020},

date = {2020-12-07},

urldate = {2020-12-07},

journal = {Plant Methods},

volume = {16},

number = {156},

keywords = {deep learning, machine learning},

pubstate = {published},

tppubtype = {article}

}

@article{iqbal2020maria,

title = {Development of a Multi-Purpose Autonomous Differential Drive Mobile Robot for Plant Phenotyping and Soil Sensing},

author = {Jawad Iqbal and Rui Xu and Hunter Halloran and Changying Li },

url = {https://www.mdpi.com/2079-9292/9/9/1550},

year  = {2020},

date = {2020-09-15},

urldate = {2020-09-15},

journal = {Electronics},

volume = {9},

number = {9},

pages = {1550},

keywords = {agricultural robot, mobile, phenotyping robot, robotics},

pubstate = {published},

tppubtype = {article}

}

@article{Ni2020,

title = {Deep learning image segmentation and extraction of blueberry fruit traits associated with harvestability and yield},

author = {Ni, X. and C. Li and H. Jiang and F. Takeda. },

url = {https://www.nature.com/articles/s41438-020-0323-3},

year  = {2020},

date = {2020-07-01},

urldate = {2020-07-01},

journal = {Horticulture Research},

volume = {7},

number = {1},

pages = {1-14},

keywords = {deep learning, machine learning},

pubstate = {published},

tppubtype = {article}

}

@article{Iqbal2020,

title = {Simulation of an autonomous mobile robot for LiDAR-based in-field phenotyping and navigation},

author = {Jawad Iqbal and Rui Xu and Shangpeng Sun and Changying Li},

year  = {2020},

date = {2020-06-15},

journal = {Robotics},

volume = {9},

number = {2},

pages = {46},

keywords = {LiDAR, robotics, simulation},

pubstate = {published},

tppubtype = {article}

}

@article{Yu2020,

title = {Convolutional neural networks for image-based high throughput plant phenotyping: A review},

author = {Yu Jiang and Changying Li},

url = {https://spj.sciencemag.org/journals/plantphenomics/2020/4152816/},

doi = {https://doi.org/10.34133/2020/4152816.},

year  = {2020},

date = {2020-02-20},

urldate = {2020-02-20},

journal = {Plant Phenomics},

volume = {2020},

number = {4152816},

keywords = {CNN, deep learning, machine learning, review},

pubstate = {published},

tppubtype = {article}

}

@article{Jiang2020,

title = {Ground based hyperspectral imaging to characterize canopy-level photosynthetic activities},

author = {Jiang, Y. and Snider, J. L. and Li, C. and Rains, G. C. and Paterson, A. H. },

year  = {2020},

date = {2020-02-01},

journal = {Remote Sensing},

volume = {12},

number = {2},

pages = {315},

keywords = {hyperspectral, SIF},

pubstate = {published},

tppubtype = {article}

}

@article{Zhang2019b,

title = {Fully convolutional networks for blueberry bruising and calyx segmentation using hyperspectral transmittance imaging },

author = {M. Zhang and Y. Jiang and C. Li and F. Yang},

url = {https://www.sciencedirect.com/science/article/pii/S1537511020300301?dgcid=author},

doi = {https://doi.org/10.1016/j.biosystemseng.2020.01.018},

year  = {2020},

date = {2020-01-27},

urldate = {2020-01-27},

journal = {Biosystems Engineering},

volume = {192},

pages = {159-175},

keywords = {deep learning, hyperspectral, machine learning},

pubstate = {published},

tppubtype = {article}

}

@article{RN27,

title = {Measurement of optical properties of fruits and vegetables: A review},

author = {Renfu Lu and Robbe Van Beers and Wouter Saeys and Changying Li and Haiyan Cen},

url = {https://www.sciencedirect.com/science/article/pii/S0925521419300870},

issn = {0925-5214},

year  = {2020},

date = {2020-01-01},

journal = {Postharvest Biology and Technology},

volume = {159},

pages = {111003},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Sun2019,

title = {Three-dimensional photogrammetric mapping of cotton bolls in situ based on point cloud segmentation and clustering},

author = {S. Sun and C. Li and P. Chee and A. Patersona and Y. Jiang and R. Xu and J. Robertson and J. Adhikari and T. Shehzad},

url = {https://www.sciencedirect.com/science/article/abs/pii/S0924271619302990?via%3Dihub},

year  = {2020},

date = {2020-01-01},

journal = {ISPRS Journal of Photogrammetry and Remote Sensing},

volume = {160 },

pages = {195-207},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Jiang2019,

title = {DeepSeedling: Deep convolutional network and Kalman filter for plant seedling detection and counting in the field },

author = { Y. Jiang and C. Li and A. Paterson and J. Robertson},

url = {https://plantmethods.biomedcentral.com/articles/10.1186/s13007-019-0528-3#citeas},

doi = {https://doi.org/10.1186/s13007-019-0528-3},

year  = {2019},

date = {2019-11-23},

urldate = {2019-11-23},

journal = {Plant Methods},

volume = {15},

number = {1},

pages = {141},

keywords = {deep learning, machine learning},

pubstate = {published},

tppubtype = {article}

}

@article{Sun2019b,

title = {Image processing algorithms for infield single cotton boll counting and yield prediction},

author = { S. Sun and C. Li and A. Paterson and P. Chee},

url = {http://sensinglab.engr.uga.edu//srv/htdocs/wp-content/uploads/2019/11/Image-processing-algorithms-for-infield-single-cotton-boll-counting-and-yield-prediction-1.pdf},

year  = {2019},

date = {2019-09-12},

journal = {Computers and Electronics in Agriculture},

abstract = {Sun, S., Li, C., Paterson, A. H., Chee, P. W., & Robertson, J. S. (2019). Image processing algorithms for infield single cotton boll counting and yield prediction. Computers and Electronics in Agriculture, 166, 104976.

Cotton boll number is an important component of fiber yield, arguably the most important phenotypic trait to plant breeders and growers alike. In addition, boll number provides a better understanding on the physiological and genetic mechanisms of crop growth and development, facilitating timely decisions on crop management to maximize profit. Traditional in-field cotton boll number counting by visual inspection is time consuming and labor-intensive. In this work, we presented novel image processing algorithms for automatic single cotton boll recognition and counting under natural illumination in the field. A digital camera mounted on a robot platform was used to acquire images with a 45° downward angle on three different days before harvest. A double thresholding with region growth algorithm combining color and spatial features was applied to segment bolls from background, and three geometric-feature-based algorithms were developed to estimate boll number. Line features detected by linear Hough Transform and the minimum boundary distance between two regions were used to merge disjointed regions split by branches and burrs, respectively. The area and the elongation ratio between major and minor axes were used to separate bolls overlapping in clusters. A total of 210 images captured

under sunny and cloudy illumination conditions on three days were used to validate the performance of

the cotton boll recognition method, with an F1 score of around 0.98; whereas, the best accuracy for boll counting was around 84.6%. At the whole plot level, fifteen plots were used to build a linear regression model between the estimated boll number and the overall fiber yield with a R2 value of 0.53. The performance was evaluated by another ten plots with a mean absolute percentage error of 8.92% and a root mean square error of 99 g. The methodology developed in this study provides a means to estimate cotton boll number from color images under field conditions and would be helpful to predict crop yield and understand genetic mechanisms of crop growth.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Jiang2018,

title = {3D point cloud data to quantitatively characterize size and shape of shrub crops},

author = {Y. Jiang and C. Li and F. Takeda and E.A. Kramer and H. Ashrafi and J. Hunter},

url = {https://www.nature.com/articles/s41438-019-0123-9

http://sensinglab.engr.uga.edu//srv/htdocs/wp-content/uploads/2019/11/3D-point-cloud-data-to-quantitatively-characterize-size-and-shape-of-shrub-crops-1.pdf},

year  = {2019},

date = {2019-04-06},

journal = {Horticulture Research},

volume = {6},

number = {43},

abstract = {Jiang, Y., Li, C., Takeda, F., Kramer, E. A., Ashrafi, H., & Hunter, J. (2019). 3D point cloud data to quantitatively characterize size and shape of shrub crops. Horticulture research, 6(1), 43.

This paper demonstrates the application of aerial multispectral images in cotton plant phenotyping. Four phenotypic traits (plant height, canopy cover, vegetation index, and flower) were measured from multispectral images captured by a multispectral camera on an unmanned aerial system. Data were collected on eight different days from two fields. Ortho-mosaic and digital elevation models (DEM) were constructed from the raw images using the structure from motion (SfM) algorithm. A data processing pipeline was developed to calculate plant height using the ortho-mosaic and DEM. Six ground calibration targets (GCTs) were used to correct the error of the calculated plant height caused by the georeferencing error of the DEM. Plant heights were measured manually to validate the heights predicted from the imaging method. The error in estimation of the maximum height of each plot ranged from -40.4 to 13.5 cm among six datasets, all of which showed strong linear relationships with the manual measurement (R2 > 0.89). Plot canopy was separated from the soil based on the DEM and normalized differential vegetation index (NDVI). Canopy cover and mean canopy NDVI were calculated to show canopy growth over time and the correlation between the two indices was investigated. The spectral responses of the ground, leaves, cotton flower, and ground shade were analyzed and detection of cotton flowers was satisfactory using a support vector machine (SVM). This study demonstrated the potential of using aerial multispectral images for high throughput phenotyping of important cotton phenotypic traits in the field.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Zhang201901,

title = {Optical properties of blueberry flesh and skin and Monte Carlo multi-layered simulation of light interaction with fruit tissues},

author = {M. Zhang and C. Li and F. Yang },

url = {http://sensinglab.engr.uga.edu//srv/htdocs/wp-content/uploads/2019/11/Optical-properties-of-blueberry-flesh-and-skin-and-Monte-Carlo-multi-layered-simulation-of-light-interaction-with-fruit-tissues.pdf},

doi = {https://doi.org/10.1016/j.postharvbio.2018.12.006},

year  = {2019},

date = {2019-03-31},

journal = {Postharvest Biology and Technology},

volume = {150},

pages = {28-41},

abstract = {Zhang, M., Li, C., & Yang, F. (2019). Optical properties of blueberry flesh and skin and Monte Carlo multi-layered simulation of light interaction with fruit tissues. Postharvest Biology and Technology, 150, 28-41.

One of the major issues of fresh blueberry production is the excessive bruising caused by mechanical impact during harvesting and packaging, which substantially lowers fruit quality and therefore compromises consumer satisfaction as well as the profitability for growers. Spectroscopy and imaging techniques such as hyperspectral imaging have great potential to detect and quantify internal bruises in fruit. It is important to measure the fundamental optical properties of blueberry tissues to better employ spectroscopy or imaging techniques. The aim of this study was to obtain the absorption coefficient (μa), reduced scattering coefficient (μ′ s), and scattering anisotropy (g) of blueberry flesh and skin in the spectral regions of 500–800 nm and 930–1400 nm and investigate the light propagation model of blueberries using Monte Carlo multi-layered (MCML) simulation. The total reflectance, total transmittance, and collimated transmittance of blueberry flesh and skin with three treatments (non-bruised, 30-min bruised, and 24-h bruised) were collected using a single integrating spherebased spectroscopic system. Using the collected spectra, the inverse adding-doubling (IAD) method was applied to calculate μa, μ′

s , and g. Results indicated that the differences between bruised (30 min and 24 h) and nonbruised flesh samples for both μ′ s and g were significant from 930 nm to 1400 nm. Microscope images revealed that the differences were caused by the damaged and ruptured cellular structure of bruised flesh. Although μa, μ′ s , and g showed significant differences between non-bruised and bruised (30 min and 24 h) flesh in the spectral region of 400–700 nm, the MCML simulation results showed that this spectral region is not effective in detecting bruises due to strong absorption and backward scattering of the blueberry skin. In contrast, the absorption effect of the skin in the near infrared range (930–1400 nm) was small, allowing light to penetrate and interact with the flesh. Therefore, the near infrared spectral region is an effective spectral range for inspecting bruised blueberries using either reflectance or transmittance method. This study reported the optical properties of blueberry skin and flesh with varying degree of bruising for the first time and simulated photon interaction with fruit tissues for bruising detection using MCML. These findings would provide guidance to develop non-destructive sensing methods for blueberry internal bruising detection.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Xu201901,

title = {Multispectral imaging and unmanned aerial systems for cotton plant phenotyping},

author = {R. Xu and C. Li and A.H. Paterson},

url = {http://sensinglab.engr.uga.edu//srv/htdocs/wp-content/uploads/2019/11/Multispectral-imaging-and-unmanned-aerial-systems-for-cotton-plant-phenotyping.pdf},

doi = {https://doi.org/10.1371/journal.pone.0205083},

year  = {2019},

date = {2019-02-27},

urldate = {2019-02-27},

journal = {PLoS One},

number = {0205083},

abstract = {Xu, R., Li, C., & Paterson, A. H. (2019). Multispectral imaging and unmanned aerial systems for cotton plant phenotyping. PloS one, 14(2), e0205083.

Size and shape are important properties of shrub crops such as blueberries, and they can be particularly useful for evaluating bush architecture suited to mechanical harvesting. The overall goal of this study was to develop a 3D imaging approach to measure size-related traits and bush shape that are relevant to mechanical harvesting. 3D point clouds were acquired for 367 bushes from five genotype groups. Point cloud data were preprocessed to obtain clean bush points for characterizing bush architecture, including bush morphology (height, width, and volume), crown size, and shape descriptors (path curve λ and five shape indices). One-dimensional traits (height, width, and crown size) had high correlations (R2 = 0.88–0.95) between proposed method and manual measurements, whereas bush volume showed relatively lower correlations (R2 = 0.78–0.85). These correlations suggested that the present approach was accurate in measuring one-dimensional size traits and acceptable in estimating three-dimensional bush volume. Statistical results demonstrated that the five genotype groups were statistically different in crown size and bush shape. The differences matched with human evaluation regarding optimal bush architecture for mechanical harvesting. In particular, a visualization tool could be generated using crown size and path curve λ, which showed great potential of determining bush architecture suitable for mechanical harvesting quickly. Therefore, the processing pipeline of 3D point cloud data presented in this study is an effective tool for blueberry breeding programs (in particular for mechanical harvesting) and farm management.},

keywords = {agricultural robot, robotics},

pubstate = {published},

tppubtype = {article}

}

@article{Fan201801,

title = {Data fusion of two hyperspectral imaging systems with complementary spectral sensing ranges for blueberry bruising detection},

author = {S. Fan and C. Li and W. Huang and L. Chen},

url = {http://sensinglab.engr.uga.edu//srv/htdocs/wp-content/uploads/2019/11/Data-fusion-of-two-hyperspectral-imaging-systems-with-complementary-spectral-sensing-ranges-for-blueberry-bruising-detection.pdf},

doi = {https://doi.org/10.3390/s18124463},

year  = {2018},

date = {2018-12-17},

urldate = {2018-12-17},

journal = {Sensors},

volume = {18},

number = {12},

pages = {4463},

abstract = {Fan, S., Li, C., Huang, W., & Chen, L. (2018). Data fusion of two hyperspectral imaging systems with complementary spectral sensing ranges for blueberry bruising detection. Sensors, 18(12), 4463.

Currently, the detection of blueberry internal bruising focuses mostly on single hyperspectral imaging (HSI) systems. Attempts to fuse different HSI systems with complementary spectral ranges are still lacking. A push broom based HSI system and a liquid crystal tunable filter (LCTF) based HSI system with different sensing ranges and detectors were investigated to jointly detect blueberry internal bruising in the lab. The mean reflectance spectrum of each berry sample was extracted from the data obtained by two HSI systems respectively. The spectral data from the two spectroscopic techniques were analyzed separately using feature selection method, partial least squares-discriminant analysis (PLS-DA), and support vector machine (SVM), and then fused with three data fusion strategies at the data level, feature level, and decision level. The three data fusion strategies achieved better classification results than using each HSI system alone. The decision level fusion integrating classification results from the two instruments with selected relevant features achieved more promising results, suggesting that the two HSI systems with complementary spectral ranges, combined with feature selection and data fusion strategies, could be used synergistically to improve blueberry internal bruising detection. This study was the first step in demonstrating the feasibility of the fusion of two HSI systems with complementary spectral ranges for detecting blueberry bruising, which could lead to a multispectral imaging system with a few selected wavelengths and an appropriate detector for bruising detection on the packing line.},

keywords = {hyperspectral},

pubstate = {published},

tppubtype = {article}

}

@article{Ozturk201801,

title = {Dielectric properties, heating rate, and heating uniformity of various seasoning spices and their mixtures with radio frequency heating},

author = {S. Ozturk and F. Kong and R. K. Singh and J. D. Kuzy and C. Li and S. Trabelsi},

url = {http://sensinglab.engr.uga.edu//srv/htdocs/wp-content/uploads/2019/11/Dielectric-properties-heating-rate-and-heating-uniformity-of-various-seasoning-spices-and-their-mixtures-with-radio-frequency-heating.pdf},

doi = {https://doi.org/10.1016/j.jfoodeng.2018.02.011},

year  = {2018},

date = {2018-07-01},

journal = {Journal of Food Engineering},

volume = {228},

pages = {128-141},

abstract = {Ozturk, S., Kong, F., Singh, R. K., Kuzy, J. D., Li, C., & Trabelsi, S. (2018). Dielectric properties, heating rate, and heating uniformity of various seasoning spices and their mixtures with radio frequency heating. Journal of food engineering, 228, 128-141.

Low moisture foods, including seasoning spices, have been associated with a number of multi-state outbreaks of salmonellosis in the past decade. The long-term objective of this study was to develop an effective in-package pasteurization treatment for seasoning mixtures based on radio frequency (RF) heating. Seasoning spices obtained from grocery stores included red, white, and black pepper; cumin; curry powder; and garlic powder with moisture contents ranging from 3.1 to 12.3% (wet basis). The dielectric properties (DP) of the seasoning spices and their mixtures as influenced by frequency, moisture, temperature, mixing fraction and salt content were determined using a precision LCR meter and liquid test fixture at frequency ranging from 1 to 30 MHz. The RF heating rates of each spice and their mixtures were evaluated using a 27.12-MHz, 6-kW pilot scale RF system with 105 mm gap between electrodes. To evaluate the effect of mixing on heating uniformity, a sample (50 g) was placed into a polystyrene plastic cylindrical container and heated to 70 
C, and surface images were taken by an infrared camera. The results showed that the relationship among moisture content, temperature and DP of white pepper can be explained by a second-order model at 13.56 and 27.12 MHz. The DP and heating rates of spice mixtures ranged between the highest and lowest values of their respective individual spices. Increase in salt content resulted in a decrease in heating rate, which resulted a better heating uniformity with smaller uniformity index (UI). The RF heating rate of samples ranged from 2.97 to 23.61 (
C min1). The highest heating rate in samples was in a correspondence to the worst heating uniformity, and highest average temperature on the sample surface. The most uniform heat distrubition on top surface was obtained for garlic powder as 0.012 (UI) at 70 
C. The information obtained from this study is important to develop an effective RF heating strategy for pathogen control in seasoning mixture.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Sun2018,

title = {In-field high throughput phenotyping and cotton plant growth analysis using LiDAR},

author = {S. Sun and C. Li and A.H. Paterson and Y. Jiang and R. Xu and J. Roberson and J. Snider and P. Chee},

url = {http://sensinglab.engr.uga.edu//srv/htdocs/wp-content/uploads/2019/11/In-Field-High-Throughput-Phenotyping-of-Cotton-Plant-Height-Using-LiDAR.pdf},

doi = {10.3389/fpls.2018.00016},

year  = {2018},

date = {2018-01-30},

urldate = {2018-01-30},

journal = {Frontiers in Plant Sciences, 9, 16},

abstract = {Sun, S., Li, C., Paterson, A. H., Jiang, Y., Xu, R., Robertson, J. S., ... & Chee, P. W. (2018). In-field high throughput phenotyping and cotton plant growth analysis using LiDAR. Frontiers in Plant Science, 9, 16.

A LiDAR-based high-throughput phenotyping (HTP) system was developed for cotton plant phenotyping in the field. The HTP system consists of a 2D LiDAR and an RTK-GPS mounted on a high clearance tractor. The LiDAR scanned three rows of cotton plots simultaneously from the top and the RTK-GPS was used to provide the spatial coordinates of the point cloud during data collection. Configuration parameters of the system were optimized to ensure the best data quality. A height profile for each plot was extracted from the dense three dimensional point clouds; then the maximum height and height distribution of each plot were derived. In lab tests, single plants were scanned by LiDAR using 0.5◦ angular resolution and results showed an R 2 value of 1.00 (RMSE = 3.46 mm) in comparison to manual measurements. In field tests using the same angular resolution; the LiDAR-based HTP system achieved average R2 values of 0.98 (RMSE = 65 mm) for cotton plot height estimation; compared to manual measurements. This HTP system is particularly useful for large field application because it provides highly accurate measurements; and the efficiency is greatly improved compared to similar studies using the side view scan.},

keywords = {High-throughput phenotyping},

pubstate = {published},

tppubtype = {article}

}

@article{Jiang2018,

title = {Quantitative Analysis of Cotton Canopy Size in Field Conditions Using a Consumer-Grade RGB-D Camera},

author = {Y. Jiang and C. Li and A.H. Paterson and S. Sun and R. Xu and J. Roberson},

url = {http://sensinglab.engr.uga.edu//srv/htdocs/wp-content/uploads/2019/11/Quantitative-Analysis-of-Cotton-Canopy-Size-in-Field-Conditions-Using-a-Consumer-Grade-RGB-D-Camera.pdf},

doi = {10.3389/fpls.2017.02233},

year  = {2017},

date = {2017-12-19},

urldate = {2017-12-19},

journal = {Frontiers in Plant Sciences, 8, 2233},

abstract = {Jiang, Y., Li, C., Paterson, A. H., Sun, S., Xu, R., & Robertson, J. (2018). Quantitative analysis of cotton canopy size in field conditions using a consumer-grade RGB-D camera. Frontiers in plant science, 8, 2233.

Plant canopy structure can strongly affect crop functions such as yield and stress tolerance, and canopy size is an important aspect of canopy structure. Manual assessment of canopy size is laborious and imprecise, and cannot measure multi-dimensional traits such as projected leaf area and canopy volume. Field-based high throughput phenotyping systems with imaging capabilities can rapidly acquire data about plants in field conditions, making it possible to quantify and monitor plant canopy development. The goal of this study was to develop a 3D imaging approach to quantitatively analyze cotton canopy development in field conditions. A cotton field was planted with 128 plots, including four genotypes of 32 plots each. The field was scanned by GPhenoVision (a customized field-based high throughput phenotyping system) to acquire color and depth images with GPS information in 2016 covering two growth stages: canopy development, and flowering and boll development. A data processing pipeline was developed, consisting of three steps: plot point cloud reconstruction, plant canopy segmentation, and trait extraction. Plot point clouds were reconstructed using color and depth images with GPS information. In colorized point clouds, vegetation was segmented from the background using an excess-green (ExG) color filter, and cotton canopies were further separated from weeds based on height, size, and position information. Static morphological traits were extracted on each day, including univariate traits (maximum and mean canopy height and width, projected canopy area, and concave and convex volumes) and a multivariate trait (cumulative height profile). Growth rates were calculated for univariate static traits, quantifying canopy growth and development. Linear regressions were performed between the traits and fiber yield to identify the best traits and measurement time for yield prediction. The results showed that fiber yield was correlated with static traits after the canopy development stage (R2 = 0.35–0.71) and growth rates in early canopy development stages (R2 = 0.29–0.52). Multi-dimensional traits (e.g., projected canopy area and volume) outperformed one-dimensional traits, and the multivariate trait (cumulative height profile) outperformed univariate traits. The proposed approach would be useful for identification of quantitative trait loci (QTLs) controlling canopy size in genetics/genomics studies or for fiber yield prediction in breeding programs and production environments.},

keywords = {High-throughput phenotyping},

pubstate = {published},

tppubtype = {article}

}

@article{Xu2018,

title = {Aerial Images and Convolutional Neural Network for Cotton Bloom Detection},

author = {R. Xu and C. Li and A.H. Paterson and Y. Jiang and S. Sun and J. Roberson},

url = {http://sensinglab.engr.uga.edu//srv/htdocs/wp-content/uploads/2019/11/Aerial-Images-and-Convolutional-Neural-Network-for-Cotton-Bloom-Detection.pdf},

doi = {10.3389/fpls.2017.02235},

year  = {2017},

date = {2017-12-19},

urldate = {2017-12-19},

journal = {Frontiers in Plant Sciences, 8, 2235},

abstract = {Xu, R., Li, C., Paterson, A. H., Jiang, Y., Sun, S., & Robertson, J. S. (2018). Aerial images and convolutional neural network for cotton bloom detection. Frontiers in Plant Science, 8, 2235.

Monitoring flower development can provide useful information for production management, estimating yield and selecting specific genotypes of crops. The main goal of this study was to develop a methodology to detect and count cotton flowers, or blooms, using color images acquired by an unmanned aerial system. The aerial images were collected from two test fields in 4 days. A convolutional neural network (CNN) was designed and trained to detect cotton blooms in raw images, and their 3D locations were calculated using the dense point cloud constructed from the aerial images with the structure from motion method. The quality of the dense point cloud was analyzed and plots with poor quality were excluded from data analysis. A constrained clustering algorithm was developed to register the same bloom detected from different images based on the 3D location of the bloom. The accuracy and incompleteness of the dense point cloud were analyzed because they affected the accuracy of the 3D location of the blooms and thus the accuracy of the bloom registration result. The constrained clustering algorithm was validated using simulated data, showing good efficiency and accuracy. The bloom count from the proposed method was comparable with the number counted manually with an error of −4 to 3 blooms for the field with a single plant per plot. However, more plots were underestimated in the field with multiple plants per plot due to hidden blooms that were not captured by the aerial images. The proposed methodology provides a high-throughput method to continuously monitor the flowering progress of cotton.},

keywords = {deep learning, High-throughput phenotyping, machine learning},

pubstate = {published},

tppubtype = {article}

}

@article{Patrick2017,

title = {High Throughput Phenotyping of Blueberry Bush Morphological Traits Using Unmanned Aerial Systems},

author = {A. Patrick and C. Li



},

url = {http://sensinglab.engr.uga.edu//srv/htdocs/wp-content/uploads/2019/11/High-Throughput-Phenotyping-of-Blueberry-Bush-Morphological-Traits-Using-Unmanned-Aerial-Systems.pdf},

doi = {10.3390/rs9121250},

year  = {2017},

date = {2017-11-30},

urldate = {2017-11-30},

journal = {Remote Sensing, 9(12), 1250},

abstract = {Patrick, A., & Li, C. (2017). High throughput phenotyping of blueberry bush morphological traits using unmanned aerial systems. Remote Sensing, 9(12), 1250.

Phenotyping morphological traits of blueberry bushes in the field is important for selecting genotypes that are easily harvested by mechanical harvesters. Morphological data can also be used to assess the effects of crop treatments such as plant growth regulators, fertilizers, and environmental conditions. This paper investigates the feasibility and accuracy of an inexpensive unmanned aerial system in determining the morphological characteristics of blueberry bushes. Color images collected by a quadcopter are processed into three-dimensional point clouds via structure from motion algorithms. Bush height, extents, canopy area, and volume, in addition to crown diameter and width, are derived and referenced to ground truth. In an experimental farm, twenty-five bushes were imaged by a quadcopter. Height and width dimensions achieved a mean absolute error of 9.85 cm before and 5.82 cm after systematic under-estimation correction. Strong correlation was found between manual and image derived bush volumes and their traditional growth indices. Hedgerows of three Southern Highbush varieties were imaged at a commercial farm to extract five morphological features (base angle, blockiness, crown percent height, crown ratio, and vegetation ratio) associated with cultivation and machine harvestability. The bushes were found to be partially separable by multivariate analysis. The methodology developed from this study is not only valuable for plant breeders to screen genotypes with bush morphological traits that are suitable for machine harvest, but can also aid producers in crop management such as pruning and plot layout organization.},

keywords = {agricultural robot, High-throughput phenotyping, phenotyping robot, robotics},

pubstate = {published},

tppubtype = {article}

}

@article{Jiang2018b,

title = {GPhenoVision: A Ground Mobile System with Multi-modal Imaging for Field-Based High Throughput Phenotyping of Cotton},

author = {Y. Jiang and C. Li and J. S. Robertson and S. Sun and R. Xu and A.H. Paterson },

url = {http://sensinglab.engr.uga.edu//srv/htdocs/wp-content/uploads/2019/11/GPhenoVision-A-Ground-Mobile-System-with-Multi-modal-Imaging-for-Field-Based-High-Throughput-Phenotyping-of-Cotton.pdf},

doi = {10.1038/s41598-018-19142-2},

year  = {2017},

date = {2017-11-30},

urldate = {2017-11-30},

journal = {Scientific Reports, 8(1), 1213},

abstract = {Jiang, Y., Li, C., Robertson, J. S., Sun, S., Xu, R., & Paterson, A. H. (2018). Gphenovision: a ground mobile system with multi-modal imaging for field-based high throughput phenotyping of cotton. Scientific reports, 8(1), 1213.

Imaging sensors can extend phenotyping capability, but they require a system to handle high-volume data. The overall goal of this study was to develop and evaluate a field-based high throughput phenotyping system accommodating high-resolution imagers. The system consisted of a high-clearance tractor and sensing and electrical systems. The sensing system was based on a distributed structure, integrating environmental sensors, real-time kinematic GPS, and multiple imaging sensors including RGB-D, thermal, and hyperspectral cameras. Custom software was developed with a multilayered architecture for system control and data collection. The system was evaluated by scanning a cotton field with 23 genotypes for quantification of canopy growth and development. A data processing pipeline was developed to extract phenotypes at the canopy level, including height, width, projected leaf area, and volume from RGB-D data and temperature from thermal images. Growth rates of morphological traits were accordingly calculated. The traits had strong correlations (r = 0.54–0.74) with fiber yield and good broad sense heritability (H2 = 0.27–0.72), suggesting the potential for conducting quantitative genetic analysis and contributing to yield prediction models. The developed system is a useful tool for a wide range of breeding/genetic, agronomic/physiological, and economic studies.},

keywords = {High-throughput phenotyping},

pubstate = {published},

tppubtype = {article}

}