The fruit supply chain (FSC) involves different stages that need to be planned at least two months in advance. Therefore, having a good fruit yield forecast with anticipation allows making timely correct decisions for providing the resources, transport, and cold storage contracts, among others. Therefore, fruit yield over or underestimation could cause important inefficiencies with regards to FSC. Because of its relevance, a method based on machine learning (ML) techniques that uses spectrometric vegetation data is proposed. This method, known as Spectrometry Based Method for Fruit Production Forecast (SBM-Fruit), allows exploring the georeferenced Normalized Difference Vegetation Index (NDVI), collected in different phenological stages, aiming to capture spatial and temporal dependency in the fruit yield forecast. In the first step of SBM-Fruit, several clusters are obtained in a clustering process using the georeferenced NDVI in all phenological stages as input, while, in the second step, two validation functions are used for determining the best clustering. Finally, in the third step, the predictor variables of the best clustering are incorporated into an artificial neural network (ANN) for predicting the fruit yield. The SBM-Fruit was applied to forecast table grape yield of an orchard located in the Valparaíso Region, Chile. The results show fruit yield estimations with mean errors around 0.013 percent for every spatial zone of the orchard, forecasted at least two months in advance. The use of the SBM-Fruit would allow FSC stakeholders to make better decisions, improving the coordination of the FSC stages, and reducing costs and fruit losses.

水果供应链 (FSC) 涉及不同的阶段，需要至少提前两个月进行计划。因此，具有预期的良好水果产量预测可以及时做出正确的决策，以提供资源、运输和冷藏合同等。因此，水果产量高估或低估可能会导致 FSC 效率低下。由于其相关性，提出了一种基于机器学习 (ML) 技术的方法，该方法使用光谱植被数据。这种方法被称为基于光谱的水果产量预测方法 (SBM-Fruit)，它允许探索在不同物候阶段收集的地理参考归一化植被指数 (NDVI)，旨在捕捉水果产量预测中的空间和时间依赖性。SBM-Fruit的第一步，使用所有物候阶段的地理参考 NDVI 作为输入，在聚类过程中获得了几个聚类，而在第二步中，使用两个验证函数来确定最佳聚类。最后，在第三步中，将最佳聚类的预测变量纳入人工神经网络（ANN）中，用于预测果实产量。SBM-Fruit 用于预测位于智利瓦尔帕莱索地区的果园的鲜食葡萄产量。结果显示，至少提前两个月预测的果园每个空间区域的水果产量估计平均误差约为 0.013%。SBM-Fruit 的使用将使 FSC 利益相关者能够做出更好的决策，改善 FSC 阶段的协调，并减少成本和水果损失。