Journal publications

@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{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{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{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{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}

}