Lirong Xiang

Field-based robotic leaf angle detection and characterization of maize plants using stereo vision and deep convolutional neural networks

Xiang, L., Gai, J., Bao, Y., Yu, J., Schnable, P. S. S., & Tang, L. (2023, February 27). JOURNAL OF FIELD ROBOTICS.

Detection and characterization of maize plant architectural traits in the field using stereo vision and deep convolutional neural networks

2022 ASABE Annual International Meeting. Presented at the 2022 ASABE Annual International Meeting, Houston, TX.

In-field soybean seed pod phenotyping on harvest stocks using 3D imaging and deep learning

2022 ASABE Annual International Meeting. Presented at the 2022 ASABE Annual International Meeting, Houston, TX.

Robotic Field-based Plant Architectural Traits Characterization Using Stereo Vision and Deep Neural Networks

Fourth International Workshop on Machine Learning for Cyber-Agricultural Systems (MLCAS2022). Presented at the Fourth International Workshop on Machine Learning for Cyber-Agricultural Systems (MLCAS2022), Ames, IA.

A Convolutional Neural Network-Based Method for Corn Stand Counting in the Field

Sensors, 21(2), 507.

AngleNet: Leaf Angle Detection and Characterization of Maize Plants in the Field Based on Stereo Vision and Deep Convolutional Neural Network

2021 ASABE Annual International Virtual Meeting. Presented at the 2021 ASABE Annual International Virtual Meeting.

Field Robotic Systems for High-Throughput Plant Phenotyping: A Review and a Case Study

Measuring Stem Diameter of Sorghum Plants in the Field Using a High-Throughput Stereo Vision System

Transactions of the ASABE, 64(6), 1999–2010.

Robotic Assay for Drought (RoAD): an automated phenotyping system for brassinosteroid and drought responses

The Plant Journal, 107(6), 1837–1853.

Using a depth camera for crop row detection and mapping for under-canopy navigation of agricultural robotic vehicle

Computers and Electronics in Agriculture, 188, 106301.

Developing a high-throughput stereo vision system for plant phenotyping

Phenome 2020. Presented at the Phenome 2020, Tucson, AZ.

Developing the Control System of an Autonomous Robot for Field-based Maize/Sorghum Plant Phenotyping

2020 ASABE Annual International Virtual Meeting. Presented at the 2020 ASABE Annual International Virtual Meeting.

PhenoBot 3.0 - an Autonomous Robot for Field-based Maize/Sorghum Plant Phenotyping

Phenome 2020. Presented at the Phenome 2020, Tucson, AZ.

PhenoStereo: a high-throughput stereo vision system for field-based plant phenotyping - with an application in sorghum stem diameter estimation

2020 ASABE Annual International Virtual Meeting, July 13-15, 2020. Presented at the 2020 ASABE Annual International Virtual Meeting, July 13-15, 2020.

Automated morphological traits extraction for sorghum plants via 3D point cloud data analysis

Computers and Electronics in Agriculture, 162, 951–961.

Robotic imaging-based methods for leaf segmentation and growth tracking in Arabidopsis

2019 ASABE Annual International Meeting. Presented at the 2019 ASABE Annual International Meeting, Boston, MA.

Automated morphological trait extraction for sorghum plants via 3D point cloud data analysis

2018 ASABE Annual International Meeting. Presented at the 2018 ASABE Annual International Meeting, Detroit, MI.

Automatic Non-Destructive Growth Measurement of Leafy Vegetables Based on Kinect

Sensors, 18(3), 806.

Comparative Analysis of Chemometrics Method on Heavy Metal Detection in Soil with Laser-Induced Breakdown Spectroscopy

Spectroscopy and Spectral Analysis, 37(12), 3871–3876. http://www.gpxygpfx.com/EN/Y2017/V37/I12/3871