Works (25)
Updated: April 4th, 2024 14:42
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
academic.oup.com (publisher PDF)
Sources: Web Of Science, ORCID
Added: March 11, 2024
2023 review
A review of three-dimensional vision techniques in food and agriculture applications
[Review of ]. SMART AGRICULTURAL TECHNOLOGY, 5.
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, ORCID
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).
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, ORCID
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.
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)
Source: Web Of Science
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.
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, ORCID
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.
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, ORCID
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).
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, 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.
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.
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.
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.
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, 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.
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
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, 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.
UN Sustainable Development Goal Categories
7. Affordable and Clean Energy
(OpenAlex)
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.
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.
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.
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.
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.
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.
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, 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.
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, 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.
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.
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.
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, 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
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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