Modern agriculture is facing tremendous challenges in its sustainability, productivity, and quality for almost ten billion people by 2050. To address these issues, we need to gain further knowledge of genetics and environment interactions (G×E), and apply those knowledge to facilitate breeding programs for cultivating new crop genotypes suitable for various production purposes and environments. These heavily rely on field based high throughput phenotyping (FB-HTP). As engineers, we are integrating various techniques (e.g. computer vision, robotics, and machine learning) to develop the state-of-the-art solutions for non-destructive, accurate, and rapid phenotyping of various crops in field conditions. Our lab developed the GPhenoVision system in 2016 and the paper presenting the system was awarded at the annual international meeting of the ASABE in 2017.

Awards

2017, Best paper award from the Information Technology, Sensors & Control Systems (ITSC) devision of the American Society of Agricultural and Biological Engineers (ASABE).

Publications

2023

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

AGI for Agriculture Journal Article

In: 2023.

Abstract | Links | BibTeX

Saeed, Farah; Sun, Shangpeng; Rodriguez-Sanchez, Javier; Snider, John; Liu, Tianming; Li, Changying

Cotton plant part 3D segmentation and architectural trait extraction using point voxel convolutional neural networks Journal Article

In: Plant Methods, vol. 19, no. 1, pp. 33, 2023, ISSN: 1746-4811.

Abstract | Links | BibTeX

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

Unoccupied aerial systems imagery for phenotyping in cotton, maize, soybean, and wheat breeding Journal Article

In: Crop Science, vol. 63, no. 4, pp. 1722-1749, 2023.

Abstract | Links | BibTeX

Tan, Chenjiao; Li, Changying; He, Dongjian; Song, Huaibo

Anchor-free deep convolutional neural network for tracking and counting cotton seedlings and flowers Journal Article

In: Computers and Electronics in Agriculture, vol. 215, pp. 108359, 2023, ISSN: 0168-1699.

Abstract | Links | BibTeX

2022

Xu, Rui; Li, Changying

A review of field-based high-throughput phenotyping systems: focusing on ground robots Journal Article

In: Plant Phenomics, vol. 2022, no. Article ID 9760269, pp. 20, 2022.

Links | BibTeX

Rodriguez-Sanchez, Javier; Li, Changying; Paterson, Andrew

Cotton yield estimation from aerial imagery using machine learning approaches Journal Article

In: Frontiers in Plant Science, vol. 13, 2022.

Links | BibTeX

Petti, Daniel; Li, Changying

Weakly-supervised learning to automatically count cotton flowers from aerial imagery Journal Article

In: Computers and Electronics in Agriculture, vol. 194, pp. 106734, 2022, ISSN: 0168-1699.

Abstract | Links | BibTeX

Xu, Rui; Li, Changying

A modular agricultural robotic system (MARS) for precision farming: Concept and implementation Journal Article

In: Journal of Field Robotics, vol. 39, no. 4, pp. 387-409, 2022.

Abstract | Links | BibTeX

2021

Sun, Shangpeng; Li, Changying; Chee, Peng W.; Paterson, Andrew H.; Meng, Cheng; Zhang, Jingyi; Ma, Ping; Robertson, Jon S.; Adhikari, Jeevan

High resolution 3D terrestrial LiDAR for cotton plant main stalk and node detection Journal Article

In: Computers and Electronics in Agriculture, vol. 187, pp. 106276, 2021, ISSN: 0168-1699.

Abstract | Links | BibTeX

Xu, Rui; Li, Changying; Bernardes, Sergio

Development and Testing of a UAV-Based Multi-Sensor System for Plant Phenotyping and Precision Agriculture Journal Article

In: Remote Sensing, vol. 13, no. 17, 2021, ISSN: 2072-4292.

Abstract | Links | BibTeX

2018

Sun, S.; Li, C.; Paterson, A. H.; Jiang, Y.; Xu, R.; Roberson, J.; Snider, J.; Chee, P.

In-field high throughput phenotyping and cotton plant growth analysis using LiDAR Journal Article

In: Frontiers in Plant Sciences, 9, 16, 2018.

Abstract | Links | BibTeX

2017

Xu, R.; Li, C.; Paterson, A. H.; Jiang, Y.; Sun, S.; Roberson, J.

Aerial Images and Convolutional Neural Network for Cotton Bloom Detection Journal Article

In: Frontiers in Plant Sciences, 8, 2235, 2017.

Abstract | Links | BibTeX

Jiang, Y.; Li, C.; Paterson, A. H.; Sun, S.; Xu, R.; Roberson, J.

Quantitative Analysis of Cotton Canopy Size in Field Conditions Using a Consumer-Grade RGB-D Camera Journal Article

In: Frontiers in Plant Sciences, 8, 2233, 2017.

Abstract | Links | BibTeX

Jiang, Y.; Li, C.; Robertson, J. S.; Sun, S.; Xu, R.; Paterson, A. H.

GPhenoVision: A Ground Mobile System with Multi-modal Imaging for Field-Based High Throughput Phenotyping of Cotton Journal Article

In: Scientific Reports, 8(1), 1213, 2017.

Abstract | Links | BibTeX

Patrick, A.; Li, C.

High Throughput Phenotyping of Blueberry Bush Morphological Traits Using Unmanned Aerial Systems Journal Article

In: Remote Sensing, 9(12), 1250, 2017.

Abstract | Links | BibTeX

Sun, S.; Li, C.; Paterson, A. H.

In-Field High-Throughput Phenotyping of Cotton Plant Height Using LiDAR Journal Article

In: Remote Sensing, 9(4), 377, 2017.

Abstract | Links | BibTeX

Patrick, A.; Pelham, S.; Culbreath, A.; Holbrook, C.; Godoy, I. J. d.; Li, C.

High Throughput Phenotyping of Tomato Spot Wilt Disease in Peanuts Using Unmanned Aerial Systems and Multispectral Imaging Journal Article

In: IEEE Instrumentation & Measurement Magazine, 20(3), 4-12, 2017.

Abstract | Links | BibTeX

2016

Jiang, Y.; Li, C.; Paterson, A. H.

High-throughput phenotyping of cotton plant height using depth images under field conditions Journal Article

In: Computers and Electronics in Agriculture, 130, 57-68, 2016.

Abstract | Links | BibTeX