Infield High-Throughput Plant Phenotyping

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

Sun, S., C. Li, A.H. Paterson, Y. Jiang, R. Xu, J. Roberson, J. Snider, and P. Chee. In-field high throughput phenotyping and cotton plant growth analysis using LiDAR. Frontiers in Plant Sciences. doi: 10.3389/fpls.2018.00016.

Xu, R., C. Li, A.H. Paterson, Y. Jiang, S. Sun, J. Roberson. Cotton bloom detection using aerial images and convolutional neural network. Frontiers in Plant Sciences. doi: 10.3389/fpls.2017.02235.

Jiang, Y., C. Li, A.H. Paterson, S. Sun, R. Xu, and J. Roberson. Quantitative analysis of cotton canopy size in field conditions using a consumer-grade RGB-D camera. Frontiers in Plant Sciences. doi: 10.3389/fpls.2017.02233.

Jiang, Y., C. Li, A.H. Paterson, J. Roberson, S. Sun, and R. Xu. GPhenoVision: A ground mobile system with multi-modal imaging for field-based high throughput phenotyping of cotton. Scientific Reports. doi: 10.1038/s41598-018-19142-2.

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

Sun, S., C. Li, and A. H. Paterson. 2017. In-Field High-Throughput Phenotyping of Cotton Plant Height Using LiDAR. Remote Sensing, 9, 377; doi:10.3390/rs9040377.

Patrick, A., S. Pelham, A. Culbreath, C. Holbrook, I.J.d. Godoy, and C. Li. 2017. High Throughput Phenotyping of Tomato Spot Wilt Disease in Peanuts Using Unmanned Aerial Systems and Multispectral Imaging. IEEE Instrumentation & Measurement Magazine. June 1-10.

Jiang, Y., C. Li., and A. Paterson. 2016. High-throughput phenotyping of cotton plant height using depth images under field conditions. Computers and Electronics in Agriculture. 130 (2016): 57-68.

 

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