A new AI-based system for early and accurate vineyard yield forecasting

Abstract

Vineyard yield forecasting is a key issue for vintage scheduling and optimization of winemaking operations. High errors in yield forecasting can be found in the wine industry, mainly due to the high spatial variability in vineyards, strong dependency on historical yield data, insufficient use of agroclimatic data and inadequate sampling methods. Today, errors can reach values within the range of 20%-30% per block. Thus, improved methodologies for early and accurate vineyard yield forecasting are needed. We proposed a new system for vineyard yield forecasting that integrates: systematic cluster counting, sampling and weight measurement; key agroclimatic parameters; vineyards spatial variability and the use of forecasting models based on artificial intelligence (AI). We carried out trials in high yield Cabernet Sauvignon (CS) vineyards located in Maule Valley (Chile), during seasons 2019 and 2020. We covered 13 blocks (66 ha) and two trellis systems (pergola and free-cordon). We characterized the spatial variability of blocks using Sentinel 2 images and NDVI analysis. We defined sampling units based on NDVI levels and we counted and sampled grape clusters and measured their weights during fruit-set and veraison. Key agroclimatic data were taken from public databases and we collected yield historical data from 2017 onwards. We trained and applied machine-learning models based on MARS, Random Forest and SVR algorithms. For the 2020 trial, in veraison, we obtained an average error of 7.6% per block against a 10.1% given by the traditional method (error is 23.5% for all the CS grapes of the company). Time dedicated to counting and sampling was significantly lower. As a result, we obtained a cost-efficient, early and accurate new system for vineyard yield forecasting.