Deep Learning Approach for Forecasting Apple Yield using Soil Parameters

Mohita Chaudhary, Lobna Nassar, Fakhri Karray

Abstract: Procuring apple yield prior to harvest is essential since it helps in estimating the apple production and prices. A compound Deep Learning (DL) model, SeriesNet with Gated Recurrent Unit (GRU) and Attention (Att-SeriesNet-GRU), is used in this work to predict the apple yield for 15 counties across 6 different Crop Reporting Districts (CRD) in California. The DL model is trained using static soil parameters, which remain constant over years per county and dynamic parameters, which change daily or monthly for a specific county as input, and the corresponding annual apple yield for that county as output. If the training is done based on a single county data then the static parameters won’t add information to the DL model since they remain constant over years per county. Therefore, considering different counties across California is decided to study the effect of considering the static soil parameters along with the dynamic ones. The county level annual apple yield forecast using both static and dynamic parameters together gives promising results. Experimenting with the test set as input shows that adding the static parameters together with the dynamic ones gives an improvement of around 34% in the value of Aggregated Measure (AGM) over the case of using the dynamic parameters alone for yield forecasting. It is also found that training the DL model with augmented training set improves the AGM value by around 12%.