A new method based on machine learning to forecast fruit yield using spectrometric data: analysis in a fruit supply chain context

• Published in: Precision agriculture Vol. 24; no. 1; pp. 326 - 352
• Main Authors: Gómez-Lagos, Javier E., González-Araya, Marcela C., Ortega Blu, Rodrigo, Acosta Espejo, Luis G.
• Format: Journal Article
• Language: English
• Published: New York Springer US 01.02.2023; Springer Nature B.V
• Subjects: Agricultural production; Agriculture; Artificial neural networks; Atmospheric Sciences; Biomedical and Life Sciences; Case studies; Chemistry and Earth Sciences; Citrus fruits; Clustering; Cold storage; Computer Science; Crop production; Crop yield; Decisions; Forecasting techniques; Fruits; Geographic information systems; Learning algorithms; Life Sciences; Literature reviews; Machine learning; Methods; Neural networks; Normalized difference vegetative index; Orchards; Physics; Remote Sensing/Photogrammetry; Scientific imaging; Soil Science & Conservation; Spectrometry; Statistics for Engineering; Supply chains; Variables; Vegetation; Fruit yield forecast; Artificial neural network; Spatial Fuzzy c-Means algorithm; Normalized Difference Vegetation Index; Machine learning
• ISSN: 1385-2256; 1573-1618
• DOI: 10.1007/s11119-022-09947-7

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.