A Review of Key Technologies in Fruits and Vegetables Maturity Detection

JIANG Mingze; YAN Jianwei; ZOU Chuanzhu

doi:10.15933/j.cnki.1004-3268.2026.01.001

Abstract：The maturity of fruits and vegetables is closely related to both producers and consumers.It plays a crucial role in enhancing consumer experience,improving harvest timing accuracy,extending shelf life,reducing waste,and increasing yield and quality.However,traditional manual methods of detecting fruit and vegetable maturity have issues such as high labor intensity,high costs,and low efficiency.In recent years,with the rapid development of computer technology,data-driven methods for detecting fruit and vegetable maturity have made automation and intelligence in this process possible.This study begins with a comprehensive review of the commonly used data formats for fruit and vegetable maturity detection.It then discusses the research progress of maturity detection algorithms from three perspectives:traditional image processing algorithms,machine learning algorithms,and deep learning algorithms.A systematic summary of the current engineering applications of maturity detection in the agricultural field is also provided.Based on this,the study further analyzes the challenges faced by data-driven maturity detection methods in real-world applications and offers feasible solutions and research approaches to address these key issues.Finally,the study presents an analysis and outlook on the future development directions of fruit and vegetable maturity detection technologies in agriculture,aiming to provide theoretical references and practical insights for the further research and application of these technologies.