Field-scale corn yield prediction using UAV multispectral data and explainable machine learning models

Authors: CHANDAN, KUMAR - Mississippi State University; JAGMAN, DHILLON - Mississippi State University; Huang, Yanbo; Reddy, Krishna

Submitted to: Computers and Electronics in Agriculture
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 1/16/2025
Publication Date: 1/24/2025
Citation: Chandan, K., Jagman, D., Huang, Y., Reddy, K.N. 2025. Field-scale corn yield prediction using UAV multispectral data and explainable machine learning models. Computers and Electronics in Agriculture. 231 (2025) 109990. https://doi.org/10.1016/j.compag.2025.109990.
DOI: https://doi.org/10.1016/j.compag.2025.109990

Interpretive Summary: Accurate and reliable corn (Zea mays L.) yield prediction at the field-scale is essential for optimizing corn production management practices for closing yield gaps. Remote sensing data integrated with Machine Learning (ML) models have been widely used in crop yield prediction. However, conventional ML models are often criticized for their ‘black-box’ nature, impeding transparency into their internal processes and the rationale behind their predictions. To address this limitation, scientists from USDA-ARS, Crop Production Systems Research Unit, Stoneville, Mississippi; USDA-ARS, Genetics and Sustainable Agriculture Research Unit, Starkville, Mississippi; and Mississippi State University, Mississippi State, Mississippi have developed explainable ML models to predict corn yield using high-resolution multispectral data captured by Unmanned Aerial Vehicle. Among thirty variables derived from multispectral data, Simplified Canopy Chlorophyll Content Index and Transformed Chlorophyll Absorption Reflectance Index were found to be the most suitable inputs. To accurately predict corn yield, we evaluated the Generalized Linear Model (GLM), K-Nearest Neighbor (KNN), Principal Component Regression (PCR), Random Forest (RF), Support Vector Machine (SVM), and Bayesian Regularized Neural Networks (BRNN). Among individual models, SVM outperformed others. This study demonstrated the application of UAV-derived multispectral data integrated with explainable ML models to achieve accurate corn yield prediction at the farm scale.

Technical Abstract: Accurate and reliable corn (Zea mays L.) yield prediction at the field-scale is essential for optimizing corn production management practices for closing yield gaps. Remote sensing data integrated with Machine Learning (ML) models have been widely used in crop yield prediction. However, conventional ML models are often criticized for their ‘black-box’ nature, impeding transparency into their internal processes and the rationale behind their predictions. To address this limitation, this study develops explainable ML models to predict corn yield using high-resolution multispectral data captured by Unmanned Aerial Vehicle. Among thirty variables derived from multispectral data, Simplified Canopy Chlorophyll Content Index and Transformed Chlorophyll Absorption Reflectance Index were found to be the most suitable inputs. To accurately predict corn yield, we evaluated the Generalized Linear Model (GLM), K-Nearest Neighbor (KNN), Principal Component Regression (PCR), Random Forest (RF), Support Vector Machine (SVM), and Bayesian Regularized Neural Networks (BRNN). We further evaluated various ensemble models combined using GLM and RF. Among individual models, SVM outperformed others with R² = 0.67 and RMSE = 1.56 Mg ha-1. Ensemble models combined using RF produced significantly higher prediction accuracy (R² = 0.90 and RMSE = 0.86 Mg ha-1) than those combined using GLM. Explanation tools provided valuable insights into three key aspects of ML modeling: (a) the contribution of variables to model performance, (b) the influence of variables on model predictions, and (c) the distribution of residuals. These insights offer a comprehensive understanding of how ML models accurately predict corn yield, fostering trust in ML models, and promoting their adoption in precision agriculture.