Prediction of Strawberry Yield and Farm Price Utilizing Deep Learning

Abstract

The currently deployed prediction models for strawberry fresh produce (FP) are based on either conventional machine learning (ML) or on simple deep learning (DL) models that are mostly applied for yield prediction. In this paper, we propose more comprehensive DL models that are applied for the first time to predict strawberry yield. The strawberry price is predicted as well directly from weather input parameters and yield. The strawberry price prediction is achieved using compound DL models such as Convolutional Long Short-Term Memory Recurrent Neural Network (CNN-LSTM). It is found that by adding attention, the performance of the compound models usually improves. After utilizing an aggregated performance measure to find the best model, the Attention-CNN-LSTM model proved to be the best compared to the rest of the deployed conventional ML models as well as the compound and simple DL models. The aggregated measure shows that this model is capable of precisely predicting strawberry prices five weeks ahead while maintaining the lowest prediction error and the highest model correlation.