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Achieving sustainability in agriculture requires resolving multiple optimization challenges. Among these, the harvest scheduling problem stands out. This paper explores scheduling tea harvests. The problem is to devise a harvest timetable that accounts for farmer quotas while respecting the constraints of both the collection center and the processing plant. The tea harvest is a cooperative effort between the grower and the factory. Factory administration is focused on resource utilization. Consequently, the process must incorporate the factory’s capacity, the collection centers’ capacities, and the frequency of transport trips. From the grower’s standpoint, it is observed that farming is often not their sole profession. On harvest days, growers are frequently absent from their main occupations. Integrating their preferred harvest days into the schedule is critical to the longevity of agricultural practices. Two separate mathematical formulations are introduced to tackle this problem. The initial formulation reduces the weekly transport frequency of factory vehicles, operating within the bounds of factory and collection center capacities. The subsequent formulation both curtails transport frequency and endeavors to satisfy grower preferences to the greatest extent feasible. A practical implementation was conducted in an area encompassing 12 collection centers, 988 growers, and 3392 decares of tea plantations. The outcomes demonstrate that the alignment rate with grower harvest preferences could be increased from 52% to 97% without altering the factory’s transport frequency. This finding reveals that accommodating grower preferences on harvest day imposes no negative consequences on the factory. Instead, it was determined that this practice bolsters both sustainability and incentive within agriculture. Additionally, the findings confirm the models’ effectiveness in resolving the problem.

Machine learning is increasingly applied across agri-food systems to support prediction, classification, optimisation, monitoring, and decision-making. These applications now extend from farm-level production to processing, logistics, consumption, and food-waste management. As data-intensive agriculture expands, machine learning is often presented as an enabling technology for sustainable agri-food transitions. Despite this promise, the sustainability evidence base remains fragmented. Many studies report improvements in model accuracy, prediction performance, or operational decision support, but they do not always specify how these technical gains translate into environmental, economic, or social outcomes. This creates uncertainty about whether machine learning contributes to sustainability transitions directly, indirectly, or only under particular implementation conditions. This systematic review examines how machine learning contributes to sustainable agri-food transitions through environmental, economic, and social impact pathways. It focuses on the relationship between machine learning methods, agri-food applications, and sustainability-relevant outcomes rather than treating model performance as an end in itself. The review asks where evidence is strongest, where it remains indirect, and how different pathways vary across production, processing, distribution, consumption, waste reduction, and governance. The review finds that environmental and economic pathways are more developed than social and equity pathways. Evidence is strongest where machine learning supports precision input management, yield forecasting, irrigation decision support, operational efficiency, supply-chain analytics, and food-waste reduction. However, stronger longitudinal evaluation, causal assessment, life-cycle integration, participatory design, and equity-sensitive governance are needed before machine learning can be judged as a reliable driver of sustainable agri-food transitions.