In-field high throughput phenotyping and cotton plant growth analysis using LiDAR S Sun, C Li, AH Paterson, Y Jiang, R Xu, JS Robertson, JL Snider, ... Frontiers in plant science 9, 16, 2018 | 140 | 2018 |
Aerial images and convolutional neural network for cotton bloom detection R Xu, C Li, AH Paterson, Y Jiang, S Sun, JS Robertson Frontiers in plant science 8, 2235, 2018 | 101 | 2018 |
Three-dimensional photogrammetric mapping of cotton bolls in situ based on point cloud segmentation and clustering S Sun, C Li, PW Chee, AH Paterson, Y Jiang, R Xu, JS Robertson, ... ISPRS Journal of Photogrammetry and Remote Sensing 160, 195-207, 2020 | 90 | 2020 |
Multispectral imaging and unmanned aerial systems for cotton plant phenotyping R Xu, C Li, AH Paterson PloS one 14 (2), e0205083, 2019 | 88 | 2019 |
Simulation of an autonomous mobile robot for LiDAR-based in-field phenotyping and navigation J Iqbal, R Xu, S Sun, C Li Robotics 9 (2), 46, 2020 | 87 | 2020 |
GPhenoVision: A ground mobile system with multi-modal imaging for field-based high throughput phenotyping of cotton Y Jiang, C Li, JS Robertson, S Sun, R Xu, AH Paterson Scientific reports 8 (1), 1213, 2018 | 80 | 2018 |
Measure of mechanical impacts in commercial blueberry packing lines and potential damage to blueberry fruit R Xu, F Takeda, G Krewer, C Li Postharvest Biology and Technology 110, 103-113, 2015 | 64 | 2015 |
Development of a multi-purpose autonomous differential drive mobile robot for plant phenotyping and soil sensing J Iqbal, R Xu, H Halloran, C Li Electronics 9 (9), 1550, 2020 | 46 | 2020 |
DeepFlower: a deep learning-based approach to characterize flowering patterns of cotton plants in the field Y Jiang, C Li, R Xu, S Sun, JS Robertson, AH Paterson Plant methods 16, 1-17, 2020 | 44 | 2020 |
Quantitative analysis of cotton canopy size in field conditions using a consumer-grade RGB-D camera Y Jiang, C Li, AH Paterson, S Sun, R Xu, J Robertson Frontiers in plant science 8, 2233, 2018 | 44 | 2018 |
Applying new technologies to transform blueberry harvesting F Takeda, WQ Yang, C Li, A Freivalds, K Sung, R Xu, B Hu, J Williamson, ... Agronomy 7 (2), 33, 2017 | 44 | 2017 |
A review of high-throughput field phenotyping systems: focusing on ground robots R Xu, C Li Plant Phenomics, 2022 | 36 | 2022 |
A modular agricultural robotic system (MARS) for precision farming: Concept and implementation R Xu, C Li Journal of Field Robotics 39 (4), 387-409, 2022 | 24 | 2022 |
Development and testing of a UAV-based multi-sensor system for plant phenotyping and precision agriculture R Xu, C Li, S Bernardes Remote Sensing 13 (17), 3517, 2021 | 19 | 2021 |
Development of an autonomous ground robot for field high throughput phenotyping R Xu, C Li, JM Velni IFAC-PapersOnLine 51 (17), 70-74, 2018 | 18 | 2018 |
Agi for agriculture G Lu, S Li, G Mai, J Sun, D Zhu, L Chai, H Sun, X Wang, H Dai, N Liu, ... arXiv preprint arXiv:2304.06136, 2023 | 15 | 2023 |
Development of the second generation berry impact recording device (BIRD II) R Xu, C Li Sensors 15 (2), 3688-3705, 2015 | 13 | 2015 |
Effect of cinnamon on starch hydrolysis of rice pudding: Comparing static and dynamic in vitro digestion models Y Li, R Xu, H Xiu, J Feng, HJ Park, H Prabhakar, F Kong Food Research International 161, 111813, 2022 | 6 | 2022 |
Cotton flower detection using aerial color images R Xu, A Paterson, C Li 2017 ASABE Annual International Meeting, 1, 2017 | 2 | 2017 |
Simulation of an In-field Phenotyping Robot: System Design, Vision-based Navigation and Field Mapping Z Li, R Xu, C Li, L Fu 2022 ASABE Annual International Meeting, 1, 2022 | 1 | 2022 |