Works (20)

Updated: December 4th, 2023 07:06

2023 article

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.

co-author countries: United States of America 🇺🇸
author keywords: convolutional neural network; field-based plant phenotyping; keypoint detection; leaf angle; stereo vision
Source: Web Of Science
Added: March 20, 2023

2023 journal article

Synchronously Predicting Tea Polyphenol and Epigallocatechin Gallate in Tea Leaves Using Fourier Transform-Near-Infrared Spectroscopy and Machine Learning

MOLECULES, 28(14).

By: S. Ye*, H. Weng*, L. Xiang n, L. Jia* & J. Xu*

co-author countries: China 🇨🇳 United Kingdom of Great Britain and Northern Ireland 🇬🇧 United States of America 🇺🇸
author keywords: tea polyphenol; EGCG; Fourier Transform-near-infrared spectroscopy; machine learning; rapid prediction
Sources: Web Of Science, ORCID
Added: October 2, 2023

2022 conference paper

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.

By: L. Xiang, X. Liu, A. Raj & L. Tang

Event: 2022 ASABE Annual International Meeting at Houston, TX on July 17-20, 2022

Source: NC State University Libraries
Added: March 18, 2023

2022 conference paper

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.

By: X. Liu, L. Xiang, A. Raj & L. Tang

Event: 2022 ASABE Annual International Meeting at Houston, TX on July 17-20, 2022

Source: NC State University Libraries
Added: March 18, 2023

2022 conference paper

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.

By: L. Xiang, X. Liu, A. Raj, J. Yu, P. Schnable & L. Tang

Event: Fourth International Workshop on Machine Learning for Cyber-Agricultural Systems (MLCAS2022) at Ames, IA on October 10-11, 2022

Source: NC State University Libraries
Added: March 18, 2023

2021 journal article

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

Sensors, 21(2), 507.

By: L. Wang*, L. Xiang*, L. Tang* & H. Jiang*

co-author countries: China 🇨🇳 United States of America 🇺🇸
Sources: Crossref, ORCID
Added: February 26, 2023

2021 conference paper

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.

By: L. Xiang, J. Gai, Y. Bao, J. Yu, P. Schnable & L. Tang

Event: 2021 ASABE Annual International Virtual Meeting on July 13-15, 2021

Source: NC State University Libraries
Added: March 18, 2023

2021 chapter book

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

By: Y. Bao*, J. Gai*, L. Xiang* & L. Tang*

co-author countries: United States of America 🇺🇸
Sources: Crossref, ORCID
Added: February 26, 2023

2021 journal article

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

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

By: L. Xiang*, L. Tang, J. Gai & L. Wang

Sources: Crossref, ORCID
Added: February 26, 2023

2021 journal article

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

The Plant Journal, 107(6), 1837–1853.

By: L. Xiang*, T. Nolan*, Y. Bao*, M. Elmore*, T. Tuel*, J. Gai*, D. Shah*, P. Wang* ...

co-author countries: United States of America 🇺🇸
MeSH headings : Arabidopsis / drug effects; Arabidopsis / physiology; Arabidopsis Proteins / genetics; Brassinosteroids / metabolism; Droughts; Equipment Design; Image Processing, Computer-Assisted / methods; Machine Learning; Phenotype; Protein Kinases / genetics; Robotics / instrumentation; Robotics / methods; Seedlings / physiology; Soil / chemistry; Triazoles / pharmacology; Zea mays / physiology
Sources: Crossref, ORCID
Added: February 25, 2023

2021 journal article

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.

By: J. Gai*, L. Xiang* & L. Tang*

co-author countries: United States of America 🇺🇸
Sources: Crossref, ORCID
Added: February 26, 2023

2020 conference paper

Developing a high-throughput stereo vision system for plant phenotyping

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

By: L. Xiang, J. Gai & L. Tang

Event: Phenome 2020 at Tucson, AZ on February 24-27, 2020

Source: NC State University Libraries
Added: March 18, 2023

2020 conference paper

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.

By: J. Gai, T. Tuel, L. Xiang & L. Tang

Event: 2020 ASABE Annual International Virtual Meeting on July 12-15, 2020

Source: NC State University Libraries
Added: March 18, 2023

2020 conference paper

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

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

By: J. Gai, T. Tuel, L. Xiang & L. Tang

Event: Phenome 2020 at Tucson, AZ on February 24-27, 2020

Source: NC State University Libraries
Added: March 18, 2023

2020 conference paper

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.

By: L. Xiang*, L. Tang, J. Gai & L. Wang

Event: 2020 ASABE Annual International Virtual Meeting, July 13-15, 2020 on July 13-15, 2020

Sources: Crossref, ORCID
Added: February 25, 2023

2019 journal article

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

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

By: L. Xiang*, Y. Bao*, L. Tang*, D. Ortiz* & M. Salas-Fernandez*

co-author countries: United States of America 🇺🇸
Sources: Crossref, ORCID
Added: February 26, 2023

2019 conference paper

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.

By: L. Xiang, Y. Bao, T. Nolan, Y. Yin & L. Tang

Event: 2019 ASABE Annual International Meeting at Boston, MA on July 7-10, 2019

Source: NC State University Libraries
Added: March 18, 2023

2018 conference paper

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.

By: L. Xiang, Y. Bao, L. Tang & M. Salas-Fernandez

Event: 2018 ASABE Annual International Meeting at Detroit, MI on July 29 - August 1, 2018

Source: NC State University Libraries
Added: March 18, 2023

2018 journal article

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

Sensors, 18(3), 806.

By: Y. Hu*, L. Wang*, L. Xiang*, Q. Wu & H. Jiang*

co-author countries: China 🇨🇳
MeSH headings : Algorithms; Automation; Biomass; Plant Leaves; Vegetables
Sources: Crossref, ORCID
Added: February 26, 2023

2017 journal article

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

By: L. Xiang, Z. Ma, X. Zhao, F. Liu, Y. He & L. Feng

Source: NC State University Libraries
Added: March 18, 2023