Citation: Opara, I.K.; Opara, U.L.; Okolie, J.A.; Fawole, O.A. Machine Learning Application in Horticulture and Prospects for Predicting Fresh Produce Losses and Waste: A Review. Plants 2024, 13, 1200. https://doi.org/ 10.3390/plants13091200 Academic Editor: Gianluca Caruso Received: 17 February 2024 Revised: 19 April 2024 Accepted: 23 April 2024 Published: 25 April 2024 Copyright: © 2024 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/). plants Review Machine Learning Application in Horticulture and Prospects for Predicting Fresh Produce Losses and Waste: A Review Ikechukwu Kingsley Opara 1,2 , Umezuruike Linus Opara 1,3 , Jude A. Okolie 4 and Olaniyi Amos Fawole 5, * 1 SARChI Postharvest Technology Research Laboratory, Africa Institute for Postharvest Technology, Faculty of AgriSciences, Stellenbosch University, Stellenbosch 7600, South Africa; ikekings101@gmail.com (I.K.O.); opara@sun.ac.za (U.L.O.) 2 Department of Food Science, Stellenbosch University, Stellenbosch 7600, South Africa 3 UNESCO International Centre for Biotechnology, Nsukka 410001, Enugu State, Nigeria 4 Gallogly College of Engineering, University of Oklahoma, Norman, OK 73019, USA; jude.okolie@ou.edu 5 Postharvest and Agroprocessing Research Centre, Department of Botany and Plant Biotechnology, University of Johannesburg, Johannesburg 2006, South Africa * Correspondence: olaniyif@uj.ac.za; Tel.: +27-11-559-7237 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. Keywords: machine learning; models; prediction; forecast; postharvest; losses and waste; fruit; vegetables; horticulture; quantification 1. Introduction Horticultural produce is known to contain essential nutritious elements in large quantities [1–3]. These essential nutrients are vital to maintaining a healthy life and have many benefits for the human body [4]. Chronic diseases such as hypertension, heart disease, stroke, diabetes, cancer, and pulmonary disease are the leading causes of mortality [5]. Increasing cases of obesity and malnutrition are also a growing concern worldwide. Research evidence has shown that increased fruit and vegetable consumption decreases the risk of diseases [6]. Also, there is a correlation between fruit and vegetable consumption and delays in age-related disorders [6,7]. Despite the benefits obtained from the consumption of fruit and vegetables, a remarkable amount is still wasted globally throughout the food value chain for several reasons, such as pest and disease infestation, environmental stress, quality issues, and marketing aesthetic standards [8,9]. To address these challenges, artificial intelligence (AI), particularly ML, has emerged as a promising tool in preharvest and postharvest horticulture [10]. Plants 2024, 13, 1200. https://doi.org/10.3390/plants13091200 https://www.mdpi.com/journal/plants