外文摘要：The current review examines the state of knowledge and research on machine learning (ML) applications in horticultural production and the potential for predicting fresh produce losses and waste. Recently, ML has been increasingly applied in horticulture for efficient and accurate operations. Given the health benefits of fresh produce and the need for food and nutrition security, efficient horticultural production and postharvest management are important. This review aims to assess the application of ML in preharvest and postharvest horticulture and the potential of ML in reducing postharvest losses and waste by predicting their magnitude, which is crucial for management practices and policymaking in loss and waste reduction. The review starts by assessing the application of ML in preharvest horticulture. It then presents the application of ML in postharvest handling and processing, and lastly, the prospects for its application in postharvest loss and waste quantification. The findings revealed that several ML algorithms perform satisfactorily in classification and prediction tasks. Based on that, there is a need to further investigate the suitability of more models or a combination of models with a higher potential for classification and prediction. Overall, the review suggested possible future directions for research related to the application of ML in postharvest losses and waste quantification.
外文关键词：machine learning;vegetables;horticulture;prediction;models;fruit;forecast;Postharvest;losses and waste;quantification
作者：Opara, Umezuruike Linus;Opara, Ikechukwu Kingsley;Okolie, Jude;Fawole, Olaniyi Amos
作者单位：Stellenbosch Univ;Univ Johannesburg;Univ Oklahoma;UNESCO Int Ctr Biotechnol
期刊名称：PLANTS-BASEL
期刊影响因子：0.0
出版年份：2024
出版刊次：13（9）
原文传递申请：江苏省科技资源（工程技术文献）统筹服务平台