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.

Papers

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.

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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.

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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.

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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.

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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.

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2021

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.

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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.

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2020

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.

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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.

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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.

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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.

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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.

BibTeX

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.

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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.

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2019

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.

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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

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

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.

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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.

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2007

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.

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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.

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2006

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