Corn Yield Prediction using Spatial-Temporal Data and Deep Learning

As the global population grows rapidly, ensuring food security has become a challenge. Climate change on the other hand has led to increased frequency and intensity of weather conditions, posing significant risks to agriculture production. An accurate yield prediction system plays a crucial role in addressing the challenge by enabling effective resource allocation, optimizing agriculture practices, and reducing risk. By accurately estimating end-of-season yield in advance, farmers can take timely proactive measures for risk mitigation and yield improvement. Current approaches suffer from imprecision, an incapacity to capture intricate nonlinear connections and the challenge of accounting for spatial-temporal variations. The current work proposes an end-to-end framework using 3 dimensional CNN models and compares different strategies to improve prediction performance. The data was collected from farms located in Ottawa, Ontario, where predominantly corn (measured in bushels per acre, bu/ac) is cultivated from the 2021 growing season divided into Early, Mid, and Later stages. Two CNN models (a) 2D CNN and (b) 3D CNN were tested, where the widely used 2D CNN model was used as a baseline. The findings demonstrated that the 3D CNN model, which also incorporates temporal features(crop changes over time), outperformed the 2D CNN model, which exclusively focuses on spatial characteristics. Overall, the 3D CNN model with stacked Early and growing season images was able to achieve a Mean Absolute Percentage Error of 15.18% and a Root Mean Square Error of 17.63 bu/ac.