Deep Learning Models for Strawberry Yield and Price Forecasting Using Satellite Images

Abstract

Forecasting crop yields and prices is crucial for both global food security and providing farmers with valuable information to avoid a price crash. This work proposes a hybrid deep learning model that uses satellite images to forecast strawberry yield along with farmers’ prices, applied in three counties in California. For tractability, a dimensionality reduction technique is applied by converting the images to histograms representing the pixel frequency. The models tested are Convolutional Neural Network (CNN), Variational AutoEncoder (VAE), CNN-Long Short-Term Memory (CNN-LSTM), Stacked AutoEncoder (SAE), and a voting ensemble of CNN-LSTM and SAE. It is found that the proposed voting ensemble of CNN-LSTM and SAE is the best at forecasting the daily strawberry yields and prices in all three counties. Based on an aggregated performance measure (AGM), the voting ensemble model outperforms the models suggested in literature with up to 70% forecasting improvement compared to the CNN model and up to 22% improvement over the CNN-LSTM model.