The paper presents a model for predicting corn yield using deep neural networks (DNNR). The model considers the interaction between weather and soil variables, challenging the assumption that there is no such interaction. A new metric, the average of absolute root squared error (ARSE), is proposed to evaluate the model’s performance. The model outperforms other methods like the random forest regressor (RFR) and the extreme gradient boosting regressor (XGBR) when tested on unforeseen data. The goal of the model is to empower smallholder farmers by providing them with a mobile application decision support system.
Publication date: 9 Jan 2024
Project Page: Not provided
Paper: https://arxiv.org/pdf/2401.03768
