Robotics (both ground and aerial) and machine learning (e.g. deep learning) are expected to dramatically change our work and life. Plant sciences and in particular agriculture is one of the most important fields where the two technologies would have a significant impact. Agricultural robots can assist (or replace) humans to work in harsh field conditions and regions with limited labor. We are developing custom robots and robotic networks with machine learning capabilities for various agricultural tasks such as phenotyping, production management (e.g. weeding and pruning), and harvesting. With the advent of the big data era, machine learning techniques will help transform the way we observe and understand plants and crops. Our lab has developed a technique to use images from the unmanned aerial systems and convolutional neural networks to count cotton flowers.



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


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

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

Towards real-time tracking and counting of seedlings with a one-stage detector and optical flow Journal Article

In: Computers and Electronics in Agriculture, vol. 193, pp. 106683, 2022, ISSN: 0168-1699.

Abstract | Links | BibTeX

Adke, Shrinidhi; Li, Changying; Rasheed, Khaled M.; Maier, Frederick W.

Supervised and Weakly Supervised Deep Learning for Segmentation and Counting of Cotton Bolls Using Proximal Imagery Journal Article

In: Sensors, vol. 22, no. 10, 2022, ISSN: 1424-8220.

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

Ni, Xueping; Li, Changying; Jiang, Huanyu; Takeda, Fumiomi

Three-dimensional photogrammetry with deep learning instance segmentation to extract berry fruit harvestability traits Journal Article

In: ISPRS Journal of Photogrammetry and Remote Sensing, vol. 171, pp. 297-309, 2021, ISSN: 0924-2716.

Abstract | Links | BibTeX


Adke, S.; Mogel, K. H. Von; Jiang, Y.; Li, C.

Instance Segmentation to Estimate Consumption of Corn Ears by Wild Animals for GMO Preference Tests Journal Article

In: Frontiers in Artificial Intelligence, vol. 3, no. 119, 2020.

Links | BibTeX

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

DeepFlower: a deep learning-based approach to characterize flowering patterns of cotton plants in the field Journal Article

In: Plant Methods, vol. 16, no. 156, 2020.

Links | BibTeX

Iqbal, Jawad; Xu, Rui; Halloran, Hunter; Li, Changying

Development of a Multi-Purpose Autonomous Differential Drive Mobile Robot for Plant Phenotyping and Soil Sensing Journal Article

In: Electronics, vol. 9, no. 9, pp. 1550, 2020.

Links | BibTeX

Ni, X.; Li, C.; Jiang, H.; Takeda., F.

Deep learning image segmentation and extraction of blueberry fruit traits associated with harvestability and yield Journal Article

In: Horticulture Research, vol. 7, no. 1, pp. 1-14, 2020.

Links | BibTeX

Iqbal, Jawad; Xu, Rui; Sun, Shangpeng; Li, Changying

Simulation of an autonomous mobile robot for LiDAR-based in-field phenotyping and navigation Journal Article

In: Robotics, vol. 9, no. 2, pp. 46, 2020.


Jiang, Yu; Li, Changying

Convolutional neural networks for image-based high throughput plant phenotyping: A review Journal Article

In: Plant Phenomics, vol. 2020, no. 4152816, 2020.

Links | BibTeX

Zhang, M.; Jiang, Y.; Li, C.; Yang, F.

Fully convolutional networks for blueberry bruising and calyx segmentation using hyperspectral transmittance imaging Journal Article

In: Biosystems Engineering, vol. 192, pp. 159-175, 2020.

Links | BibTeX


Jiang, Y.; Li, C.; Paterson, A.; Robertson, J.

DeepSeedling: Deep convolutional network and Kalman filter for plant seedling detection and counting in the field Journal Article

In: Plant Methods, vol. 15, no. 1, pp. 141, 2019.

Links | BibTeX

Xu, R.; Li, C.; Paterson, A. H.

Multispectral imaging and unmanned aerial systems for cotton plant phenotyping Journal Article

In: PLoS One, no. 0205083, 2019.

Abstract | Links | BibTeX


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

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

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


Li, C.; Heinemann, P.

ANN integrated electronic nose system for apple quality evaluation Journal Article

In: Transactions of the ASABE, 50(6), 2285-2294, 2007.

Abstract | Links | BibTeX

Li, C.; Heinemann, P.; Sherry, R.

Neural network and Bayesian network fusion models to fuse electronic nose and surface acoustic wave sensor data for apple defect detection Journal Article

In: Sensors and Actuators B: Chemical, 125(1), 301-310, 2007.

Abstract | Links | BibTeX


Li, C.; Heinemann, P.; Reed, P.

Using genetic algorithms (GAs) and CMA evolutionary strategy to optimize electronic nose sensor selection Journal Article

In: Transactions of the ASABE, 51(1), 321-330, 2006.

Abstract | Links | BibTeX