AI-Driven Machine Vision Technologies in Plant Science
A special issue of Plants (ISSN 2223-7747). This special issue belongs to the section "Plant Modeling".
Deadline for manuscript submissions: 31 March 2026 | Viewed by 67
Special Issue Editors
Interests: machine learning; image processing; artificial intelligence; spectrum technology; food quality monitoring
Special Issue Information
Dear Colleagues,
The integration of artificial intelligence (AI) and machine vision technologies has revolutionized plant science, offering unprecedented opportunities for precision agriculture and sustainable forestry. These advanced tools enable automated plant phenotyping, disease detection, yield prediction, and environmental monitoring with high accuracy and efficiency. Machine learning algorithms, particularly deep learning, have enhanced image-based analysis of crops and forest ecosystems, while AI-driven decision support systems optimize resource management and reduce environmental impact. However, challenges remain in scalability, real-time processing, and model interpretability. This Special Issue seeks to compile cutting-edge research on AI and machine vision applications in plant science, highlighting innovations that bridge the gap between laboratory research and field deployment.
Scope of the Special Issue：
We invite original research articles, reviews, and case studies focusing on (but not limited to) the following topics:
- AI and deep learning for plant phenotyping (morphological trait extraction, growth monitoring);
- Machine vision-based disease and pest detection in crops and forests;
- Yield prediction and quality assessment using hyper/multispectral imaging;
- Robotics and automation for smart farming and precision forestry;
- Explainable AI (XAI) and interpretable models for agri-forestry applications;
- Edge AI and real-time vision systems for field deployment;
- Datasets and benchmarking for AI in plant science.
This Special Issue aims to foster interdisciplinary collaboration among computer scientists, agronomists, and forestry experts, promoting scalable and sustainable solutions for modern plant science.
Dr. Dayang Liu
Dr. Yi Shi
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.
Keywords
- AI and deep learning for plant phenotyping
- machine vision-based disease and pest detection
- yield prediction and quality assessment
- robotics and automation
- explainable AI (XAI) and interpretable models
- edge AI and real-time vision systems
- datasets and benchmarking
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