Works (29)

Updated: July 11th, 2024 05:00

2024 journal article

CCHA YOLO for mycelium clamp connection (CC) and hyphae Autolysis (HA) detection under microscopy imaging and web deployment

MICROCHEMICAL JOURNAL, 201.

By: L. Wu*, S. Lin*, W. Jin*, H. Weng*, J. Xu*, L. Zhang*, Y. Xu*, L. Xiang n, S. Sun*, D. Ye*

author keywords: Multi-scale object detection; Hybrid Swim Transformer; Web deployment; Clamp connection (CC); Hyphae autolysis (HA)
Sources: Web Of Science, NC State University Libraries
Added: June 11, 2024

2024 journal article

Foundation models in smart agriculture: Basics, opportunities, and challenges

COMPUTERS AND ELECTRONICS IN AGRICULTURE, 222.

author keywords: Foundation models; Smart agriculture; Language foundation models; Vision foundation models; Multimodal foundation models; Reinforcement learning foundation models
UN Sustainable Development Goal Categories
2. Zero Hunger (OpenAlex)
Sources: Web Of Science, NC State University Libraries
Added: June 24, 2024

2024 journal article

Plant-Denoising-Net (PDN): A plant point cloud denoising network based on density gradient field learning

ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING, 210, 282–299.

By: J. Wu, L. Xiang*, H. You, L. Tang & J. Gai

author keywords: Point cloud denoising; Deep learning; Phenotyping; Multi -feature fusion; Density gradient field; Gradient ascent
Sources: Web Of Science, NC State University Libraries
Added: May 28, 2024

2024 journal article

Swin-Roleaf: A new method for characterizing leaf azimuth angle in large-scale maize plants

COMPUTERS AND ELECTRONICS IN AGRICULTURE, 224.

By: W. He*, J. Gage*, R. Rellan-Alvarez* & L. Xiang*

author keywords: Circular Smooth Label; Maize azimuth angle; Oriented Bounding Boxes; Swin Transformer; YOLOv5
UN Sustainable Development Goal Categories
2. Zero Hunger (OpenAlex)
Sources: ORCID, Web Of Science, NC State University Libraries
Added: June 15, 2024

2024 journal article

Technical note: ShinyAnimalCV: open-source cloud-based web application for object detection, segmentation, and three-dimensional visualization of animals using computer vision

JOURNAL OF ANIMAL SCIENCE, 102.

author keywords: computer vision; morphological features; object detection; object segmentation; shiny application; three-dimensional visualization
Sources: Web Of Science, NC State University Libraries
Added: March 11, 2024

2023 review

A review of three-dimensional vision techniques in food and agriculture applications

[Review of ]. SMART AGRICULTURAL TECHNOLOGY, 5.

By: L. Xiang n & D. Wang*

author keywords: 3D imaging; RGB-D imaging; Stereo imaging; Deep learning; Point cloud analysis
UN Sustainable Development Goal Categories
2. Zero Hunger (OpenAlex)
Sources: Web Of Science, NC State University Libraries
Added: January 16, 2024

2023 journal article

Early Detection of Rice Blast Using a Semi-Supervised Contrastive Unpaired Translation Iterative Network Based on UAV Images

PLANTS-BASEL, 12(21).

By: S. Lin*, J. Li*, D. Huang*, Z. Cheng*, L. Xiang n, D. Ye*, H. Weng*

author keywords: rice blast; semi-supervised; soft labels; contrastive unpaired translation; unmanned aerial vehicle
TL;DR: The findings demonstrate that the proposed model can accurately identify rice blast under field-scale conditions and is higher than those of common detection models (YOLO, Y OLACT, YOLACT++, Mask R-CNN, and Faster R- CNN). (via Semantic Scholar)
UN Sustainable Development Goal Categories
2. Zero Hunger (OpenAlex)
Sources: Web Of Science, NC State University Libraries
Added: December 4, 2023

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, Vol. 2.

author keywords: convolutional neural network; field-based plant phenotyping; keypoint detection; leaf angle; stereo vision
TL;DR: The feasibility of using stereo vision to investigate the distribution of leaf angles in maize under field conditions is demonstrated and the proposed system is an efficient alternative to traditional leaf angle phenotyping and thus could accelerate breeding for improved plant architecture. (via Semantic Scholar)
UN Sustainable Development Goal Categories
2. Zero Hunger (Web of Science)
Sources: Web Of Science, ORCID, NC State University Libraries
Added: March 20, 2023

2023 article

Shinyanimalcv: Interactive Web Application for Object Detection and Three-Dimensional Visualization of Animals Using Computer Vision

Wang, J., Xiang, L., Morota, G., Wickens, C., Cushon, E., Brooks, S., & Yu, H. (2023, November 6). JOURNAL OF ANIMAL SCIENCE, Vol. 101, pp. 244–245.

By: J. Wang*, L. Xiang n, G. Morota*, C. Wickens*, E. Cushon*, S. Brooks*, H. Yu*

author keywords: computer vision; precision livestock farming; R shiny
TL;DR: The newly developed ShinyAnimalCV is developed, which is a Shiny-based interactive animal computer vision web application that offers a user-friendly graphical user interface for object detection and three-dimensional visualization and could facilitate the application of computer vision in the animal science community. (via Semantic Scholar)
Sources: Web Of Science, NC State University Libraries
Added: December 18, 2023

2023 article

Spectroscopic determination of chlorophyll content in sugarcane leaves for drought stress detection

Gai, J., Wang, J., Xie, S., Xiang, L., & Wang, Z. (2023, November 13). PRECISION AGRICULTURE, Vol. 11.

By: J. Gai*, J. Wang*, S. Xie*, L. Xiang n & Z. Wang*

author keywords: VIS/NIR spectroscopy; Sugarcane; Leaf chlorophyll content; Machine learning; Drought stress
UN Sustainable Development Goal Categories
6. Clean Water and Sanitation (OpenAlex)
Sources: Web Of Science, NC State University Libraries
Added: December 11, 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*

author keywords: tea polyphenol; EGCG; Fourier Transform-near-infrared spectroscopy; machine learning; rapid prediction
TL;DR: The results demonstrate a potential of FT-NIR spectroscopy combined with machine learning for the rapid screening of genotypes with high tea polyphenol and EGCG content in tea leaves. (via Semantic Scholar)
UN Sustainable Development Goal Categories
2. Zero Hunger (Web of Science)
Sources: Web Of Science, NC State University Libraries
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*

TL;DR: An automated, robust, and high-throughput method for corn stand counting based on color images extracted from video clips that is accurate and reliable for stand counting, achieving an accuracy of over 98% at growth stages V2 and V3. (via Semantic Scholar)
Sources: Crossref, NC State University Libraries
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*

TL;DR: This chapter presents an updated review of the infield ground-based robotic HTPP systems developed so far and presents a vision sensor PhenoStereo to show the promising potential of integrating conventional stereo imaging with the state-of-the-art visual perception techniques for plant organ phenotyping applications. (via Semantic Scholar)
Sources: Crossref, NC State University Libraries
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

UN Sustainable Development Goal Categories
7. Affordable and Clean Energy (OpenAlex)
Sources: Crossref, NC State University Libraries
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* ...

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, NC State University Libraries
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*

Sources: Crossref, NC State University Libraries
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

TL;DR: This research demonstrated that with proper customization stereo vision can be a highly desirable sensing method for field-based plant phenotyping using high-fidelity 3D models reconstructed from stereoscopic images. (via Semantic Scholar)
UN Sustainable Development Goal Categories
7. Affordable and Clean Energy (OpenAlex)
Sources: Crossref, NC State University Libraries
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.

TL;DR: Stem volume was a promising predictor of shoot fresh weight and shoot dry weight, and the total leaf area was strongly correlated to shoot biomass at early stages, which revealed high correlations between the manual measurements and the estimated values generated by the system. (via Semantic Scholar)
Sources: Crossref, NC State University Libraries
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*

MeSH headings : Algorithms; Automation; Biomass; Plant Leaves; Vegetables
TL;DR: A Kinect-based automatic system for non-destructive growth measurement of leafy vegetables using a turntable to acquire multi-view point clouds of the measured plant and a series of suitable algorithms were applied to obtain a fine 3D reconstruction. (via Semantic Scholar)
UN Sustainable Development Goal Categories
2. Zero Hunger (OpenAlex)
Sources: Crossref, NC State University Libraries
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

Employment

Updated: March 31st, 2024 23:15

2022 - present

North Carolina State University Raleigh, US
Assistant Professor Biological and Agricultural Engineering

Education

Updated: April 19th, 2024 12:14

2017 - 2022

Iowa State University Ames, US

2013 - 2017

Zhejiang University Hangzhou, CN

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