Abstract

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 Valparaiso 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.