Computer Vision Techniques for Plant Phenomics Applications

Dear Colleagues,

Plant phenomics, the study of plant traits and their responses to environmental conditions, is critical for advancing agricultural productivity, sustainability, and food security. With the rapid development of computer vision technologies, there is a transformative opportunity to enhance the precision, scalability, and efficiency of phenotyping processes. Computer vision techniques, such as image segmentation, feature extraction, and deep learning, have shown great promise in analyzing plant traits from high-throughput imaging data, enabling non-invasive and real-time monitoring of plant growth, stress responses, and yield potential.

However, adapting computer vision to plant phenomics presents unique challenges, including handling diverse plant morphologies, varying environmental conditions, and integrating multi-modal data (e.g., RGB, hyperspectral, and 3D imaging). This Special Issue aims to bring together cutting-edge research that addresses these challenges, fostering interdisciplinary collaboration between the computer vision and plant science communities. We invite contributions covering a wide range of topics, including, but not limited to, the following:

• Advanced Image Processing Techniques: Novel algorithms for plant segmentation, trait extraction, and morphological analysis.
• Deep Learning for Phenotyping: Applications of convolutional neural networks (CNNs), transformers, and other deep learning models in plant trait identification and classification.
• Multi-Modal Data Integration: Combining RGB, hyperspectral, thermal, or 3D imaging for comprehensive phenotyping.
• High-Throughput Phenotyping Systems: Automated platforms for large-scale plant imaging and analysis.
• Robustness to Environmental Variability: Methods to handle varying lighting, backgrounds, and field conditions in outdoor phenotyping.
• Real-Time Monitoring and Analysis: Computer vision solutions for dynamic, in situ plant monitoring.
• Applications in Precision Agriculture: Use cases in crop breeding, stress detection, yield prediction, and resource optimization.
• Open-Source Tools and Datasets: Development of publicly available software and benchmark datasets for plant phenomics research.

Dr. Xijian Fan
Dr. Weiliang Wen
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Plants is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2700 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

• computer vision
• deep learning
• UAV remote sensing
• multisource fusion
• plant phenomics
• phenotypic traits extraction