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Abstract：
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.
摘要：
当前的评论研究了园艺生产中机器学习（ML）应用的知识和研究状况，以及预测新鲜农产品损失和浪费的潜力。最近,ML已越来越多地应用于园艺中，以实现高效和准确的操作。鉴于新鲜农产品的健康益处以及对食品和营养安全的需求，高效的园艺生产和采后管理很重要。这篇综述旨在评估ML在收获前和收获后园艺中的应用，以及ML通过预测其大小在减少收获后损失和浪费方面的潜力。这对于减少损失和浪费的管理实践和政策制定至关重要。该综述从评估ML在收获前园艺中的应用开始。然后介绍了ML在采后处理和处理中的应用，最后，在采后损失和废物定量中的应用前景。研究结果表明，几种ML算法在分类和预测任务中的表现令人满意。基于此,需要进一步研究更多模型或具有更高分类和预测潜力的模型组合的适用性。总的来说,该综述提出了与ML在采后损失和废物定量中的应用相关的未来研究方向。
