Machine Learning for Quality and Stress Response Mechanisms of Horticultural Products

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

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