Sensors and Computer Vision for Quality Assessment of Agricultural Products

Dear Colleagues,

The global demand for high-quality, safe, and sustainably produced agricultural products continues to rise, driven by population growth, evolving consumer expectations, and stringent regulatory standards. Recent advances in sensor technologies and computer vision have opened new frontiers for real-time monitoring, grading, and traceability of agricultural commodities. The integration of imaging systems, hyperspectral/multispectral sensors, deep learning models, and edge-computing platforms is transforming how we evaluate freshness, ripeness, defects, nutritional content, and authenticity of fruits, vegetables, grains, meat, and other agricultural goods.

We are pleased to invite you to contribute to a Special Issue of AgriEngineering, titled “Sensors and Computer Vision for Quality Assessment of Agricultural Products.”

This Special Issue aims to showcase cutting-edge research at the intersection of sensing technologies, computer vision, and agricultural product quality evaluation. It seeks to compile original studies and comprehensive reviews that demonstrate novel methodologies, system integrations, field or laboratory validations, and practical applications aligned with sustainable and efficient agri-food systems.

In this Special Issue, original research articles and reviews are welcome. Research areas may include (but are not limited to) the following:

• Non-destructive sensing techniques (e.g., NIR, hyperspectral, thermal, Raman, fluorescence imaging) for quality traits;
• Deep learning and machine learning approaches for image-based classification, segmentation, and defect detection;
• Real-time computer vision systems for on-line sorting and grading in processing facilities;
• Sensor fusion strategies combining multiple modalities for enhanced accuracy;
• Portable or low-cost sensing devices for smallholder or on-farm use;
• Benchmark datasets and open-source tools for agricultural product analysis;
• Case studies on specific commodities (e.g., apples, rice, coffee, dairy, poultry);
• Integration of quality assessment systems with robotics, IoT, or blockchain for traceability.

We look forward to receiving your contributions.

Dr. Yongqiang Li
Dr. Xiaofei An
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 250 words) can be sent to the Editorial Office for assessment.

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.

• computer vision
• sensors
• non-destructive quality assessment
• deep learning
• agricultural products