2025
Adhikari, Jeevan; Petti, Daniel; Vitrakoti, Deepak; Ployaram, Wiriyanat; Li, Changying; Paterson, Andrew H.
Characterizing season-long floral trajectories in cotton with low-altitude remote sensing and deep learning Journal Article
In: PLANTS, PEOPLE, PLANET, vol. n/a, no. n/a, 2025.
Abstract | Links | BibTeX | Tags: cotton, deep learning, flowering time, genome mapping, High-throughput phenotyping, remote sensing, unmanned aerial vehicles
@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}
}
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.
Petti, Daniel; Li, Changying; Chee, Peng
Real-Time Multi-View Flower Counting With a Ground Mobile Robot Journal Article
In: Journal of Field Robotics, vol. 42, no. 8, pp. 1-27, 2025.
Abstract | Links | BibTeX | Tags: Computer Vision, cotton, Edge Computing, High-throughput phenotyping, mobile robot, Multi-Object Tracking, multi-view
@article{Petti2025a,
title = {Real-Time Multi-View Flower Counting With a Ground Mobile Robot},
author = {Daniel Petti and Changying Li and Peng Chee},
url = {https://onlinelibrary.wiley.com/doi/abs/10.1002/rob.70093},
doi = {https://doi.org/10.1002/rob.70093},
year = {2025},
date = {2025-01-01},
journal = {Journal of Field Robotics},
volume = {42},
number = {8},
pages = {1-27},
abstract = {ABSTRACT Although season-long cotton flowering time characterization has value to breeders and growers, a manual data collection process is too laborious to be practical in most cases. In recent years, several fully automated flower counting approaches have been proposed. However, such approaches are typically designed to run offline and require a significant amount of computation. Furthermore, little thought has gone into developing convenient interfaces and integrations so that a layperson can use such systems without extensive training. The goal of this study is to develop a flower tracking system that is deployable on a ground robot and can operate in real time. A previous GCNNMatch++ approach was modified to increase the inference speed. Additionally, data from multiple cameras were fused to avoid canopy occlusions, and three-dimensional flower locations were extracted by integrating GPS data from the robot. It is shown that the approach significantly outperforms UAV-based counting and single-camera counting while running at above 40 FPS on an edge device, achieving a counting error of 15. Overall, it is believed that the highly integrated, automated, and simplified flower counting solution makes significant strides toward a practical commercial cotton phenotyping platform.},
keywords = {Computer Vision, cotton, Edge Computing, High-throughput phenotyping, mobile robot, Multi-Object Tracking, multi-view},
pubstate = {published},
tppubtype = {article}
}
ABSTRACT Although season-long cotton flowering time characterization has value to breeders and growers, a manual data collection process is too laborious to be practical in most cases. In recent years, several fully automated flower counting approaches have been proposed. However, such approaches are typically designed to run offline and require a significant amount of computation. Furthermore, little thought has gone into developing convenient interfaces and integrations so that a layperson can use such systems without extensive training. The goal of this study is to develop a flower tracking system that is deployable on a ground robot and can operate in real time. A previous GCNNMatch++ approach was modified to increase the inference speed. Additionally, data from multiple cameras were fused to avoid canopy occlusions, and three-dimensional flower locations were extracted by integrating GPS data from the robot. It is shown that the approach significantly outperforms UAV-based counting and single-camera counting while running at above 40 FPS on an edge device, achieving a counting error of 15. Overall, it is believed that the highly integrated, automated, and simplified flower counting solution makes significant strides toward a practical commercial cotton phenotyping platform.