Journal publications

@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{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{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}

}