Integration of high resolution remotely sensed data and machine learning techniques for spatial prediction of soil properties and corn yield

Abstract

Widespread adoption of precision agriculture requires timely acquisition of low-cost, high quality soil and crop yield maps. Integration of remotely sensed data and machine learning algorithms offers cost-and time-effective approach for spatial prediction of soil properties and crop yield compared to conventional approaches. The objectives of this study were to: (i) evaluate the role of remotely sensed images; (ii) compare the performance of various machine learning algorithms; and (iii) identify the importance of remotely sensed image-derived variables, in spatial prediction of soil properties and corn yield. This study integrated field based data on five soil properties (i.e., soil organic matter (SOM), cation exchange capacity (CEC), magnesium (Mg), potassium (K), and pH) and yield monitor based corn yield data with multispectral aerial images and topographic data, both collected in 2013, from seven fields at the Molly Caren Farm near London, Ohio. Digital elevation model data, at a resolution of 1 m, was used to derive topographic properties of the fields. Multispectral images collected at bare-soil conditions, at a resolution 0.30 m, were used to derive soil and vegetation indices. Models developed for prediction of soil properties and corn yield using linear regression (LM) and five machine learning algorithms (i.e., Random Forest (RF); Neural Network (NN); Support Vector Machine (SVM) with radial and linear kernel functions; Gradient Boosting Model (GBM); and Cubist (CU)) were evaluated in terms of coefficient of determination (R2) and root mean square error (RMSE). Machine learning algorithms were found to outperform LM algorithm for most of the times with a higher R2 and lower RMSE. Based on models for seven fields, on average, NN provided the highest accuracy for SOM (R2 = 0.64, RMSE = 0.44) and CEC (R2 = 0.67, RMSE = 2.35); SVM for K (R2 = 0.21, RMSE = 0.49) and Mg (R2 = 0.22, RMSE = 4.57); and GBM for pH (R2 = 0.15, RMSE = 0.62). For corn yield, RF consistently outperformed other models and provided higher accuracy (R2 = 0.53, RMSE = 0.97). Soil and vegetation indices based on bare-soil imagery played a more significant role in demonstrating in-field variability of corn yield and soil properties than topographic variables. The accuracy of the models developed for prediction of soil properties and corn yield observed in this study suggested that the approach of integrating remotely sensed data and machine learning algorithms are promising for mapping soil properties and corn yield at a local scale, which can be useful in locating areas of potential concerns and implementing site-specific farming practices.