Machine Learning for Quality and Stress Response Mechanisms of Horticultural Products
A Special Issue of Horticulturae (ISSN 2311-7524) belonging to the section "Postharvest Biology, Quality, Safety, and Technology".
Deadline for manuscript submissions: 30 June 2027 | Viewed by 29
Editors
Interests: hyperspectral imaging; postharvest quality; stress response mechanism
Special Issues, Collections and Topics in MDPI journals
Interests: resistance; application of mulch films; cultivation; molecular physiology
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
Horticultural products frequently suffer from quality decline, nutritional loss and biological stress damage during postharvest handling, which impairs their stress adaptability, shortens shelf life and limits commercial benefits. Traditional detection and evaluation methods are destructive, labor-intensive and inefficient, failing to achieve real-time, dynamic monitoring of product quality and stress response. As an advanced artificial intelligence technology, machine learning has emerged as a critical technique for intelligent analysis and precise regulation of horticultural product postharvest performance.
This Special Issue focuses on innovative machine learning model construction and algorithm optimization targeting horticultural product quality variation and stress response mechanisms. It welcomes original studies on feature extraction, predictive modeling, intelligent classification and model optimization for produce freshness assessment, stress response identification, quality degradation prediction and shelf-life evaluation, as well as studies dealing with AI in improving quality and longer shelf life. It aims to provide a novel theoretical basis and technical references for intelligent and efficient postharvest quality control and stress regulation of horticultural products.
Dr. Longguo Wu
Prof. Dr. Yan Yan
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-anonymized peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Horticulturae 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 2400 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
- machine learning
- horticultural products
- postharvest quality
- stress response mechanism
- quality prediction
- intelligent detection
- algorithm optimization
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