Lightweight AI for Agricultural Quality Assurance: Computer Vision Applications in Tea and Other High-Value Crops
A special issue of AgriEngineering (ISSN 2624-7402). This special issue belongs to the section "Computer Applications and Artificial Intelligence in Agriculture".
Deadline for manuscript submissions: 30 April 2026 | Viewed by 42
Special Issue Editors
Interests: tea processing; non-destructive measurement; deep learning; machine learning
Interests: computer vision; deep learning; object detection; agricultural robotics; agricultural automation; precision agriculture; smart farming
Special Issue Information
Dear Colleagues,
We are pleased to invite you to contribute to this Special Issue on “Lightweight AI for Agricultural Quality Assurance: Computer Vision Applications in Tea and Other High-Value Crops”. Artificial intelligence (AI) and computer vision are transforming agricultural production and quality monitoring. In tea production, subtle variations in leaf grades, bud tenderness, moisture content, and processing stages critically influence quality. At the same time, challenges such as labor-intensive management, environmental stress, and pest or disease outbreaks demand intelligent, scalable solutions. Lightweight AI models, with their efficiency and adaptability, provide a promising pathway to real-time, cost-effective assessment across both field management and processing. Beyond tea, these methods can be applied to other high-value crops to make farming more sustainable, improve product traceability, and build consumer confidence.
This Special Issue aims at presenting recent advances in lightweight AI models and computer vision techniques for agricultural quality assurance, with a particular emphasis on tea production while extending its scope to other high-value crops and foods. The focus is on efficient architectures and deployment strategies that balance accuracy, computational efficiency, and adaptability to real-world agricultural environments. This aligns with the journal’s scope covering biological and agricultural engineering, smart farming, automation and robotics in agriculture, pre- and post-harvest processing, and pest management. Contributions are encouraged that address both field-based tea plantation applications (e.g., bud detection, growth assessment, pest and disease recognition, and remote sensing or hyperspectral imaging) and processing quality control (e.g., withering, fermentation, rolling, drying, and grading).
In this Special Issue, original research articles and reviews are welcome. Research areas may include (but are not limited to) the following:
- Lightweight deep learning for tea bud detection, growth monitoring, and pest/disease diagnosis;
- Using deep learning for real-time analysis of remote sensing and hyperspectral imaging in tea plantations;
- AI-driven impurities detection, grading, and classification in fresh leaves and processed tea;
- Hyperspectral and vision-based monitoring of tea withering, fermentation, rolling, and drying;
- Cross-domain applications of lightweight AI in agricultural and food quality control;
- Sensor fusion of vision, spectroscopy, IoT, and robotics for tea production and agricultural applications;
- Edge computing, model compression, and deployment strategies in resource-limited agricultural environments;
- Benchmark datasets, evaluation frameworks, and reproducible pipelines for agricultural AI.
We look forward to receiving your contributions.
Prof. Dr. Chunwang Dong
Dr. Zhiwei Chen
Dr. Chennan Yu
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. AgriEngineering is an international peer-reviewed open access monthly 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 1600 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
- lightweight AI
- agricultural quality assurance
- computer vision
- edge computing
- sensor fusion
- tea production
- tea bud detection
- pest and disease diagnosis
- tea grading
- impurity detection
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