Development of an Integrated System for Forecasting Fruit Crop Yields Based on Multimodal Data and an Ensemble of Machine Learning Models

Abstract

The article presents a comprehensive system for forecasting orchard yields based on multimodal remote monitoring data. It combines convolutional neural networks for detecting flowers, ovaries, and fruits with an ensemble of linear and nonlinear models (multivariate regression, MLP, LSTM) for yield estimation. LASSO regression and SHAP analysis are used to interpret the results. The developed Python software enables full data processing, visualization, and saving of forecasts. The model achieves a determination coefficient of R2>0.85 and RMSE

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