Predicting crop yield response to management and environmental variables is a crucial step towards nutrient management optimization. With the increase in the amount of data generated by agricultural machinery, more sophisticated models are necessary to get full advantage of such data. In this work, we propose a Convolutional Neural Network (CNN) to capture relevant spatial structures of different attributes and combine them to model yield response to nutrient and seed rate management. Nine on-farm experiments on corn fields are used to construct a suitable dataset to train and test the CNN model. Four architectures combining input attributes at different stages in the network are evaluated and compared to the most commonly used predictive models. Results show a reduction in the test dataset RMSE up to 68% when compared to multiple linear regression and up to 29% when compared to a random forest. We also demonstrate that higher variability associated with the spatial structure of the data takes the most advantage of this framework.
ASJC Scopus subject areas
- Agronomy and Crop Science
- Computer Science Applications