A deep learning multi-layer perceptron and remote sensing approach for soil health based crop yield estimation

Abstract

In recent years, Deep Learning Multi-Layer Perceptron (DLMLP) neural networks have shown remarkable success in addressing crop yield forecast related problems. The methodologies used so far for crop yield forecast with remotely sensed data were focused upon vegetation indices generated from optical data. The prediction of crop yield in an accurate manner by developing robust machine learning models based on soil health parameters is crucial since it helps keep a track of soil health as well as its impact on overall yield. This study aims to utilize remotely sensed Microwave satellite data from Sentinel-1 and optical data from Sentinel-2, and field data to estimate three important soil health parameters- Soil Moisture, Soil Salinity, and Soil Organic Carbon (SOC). The study has been carried out in the Rupnagar district of Punjab in India. The estimated soil health parameters, SAR backscatter, and optical remote sensing satellite data parameters were utilized to estimate wheat crop yield. The soil health based DLMLP model performed best in crop yield estimation and gave R2 values of 0.723 and 0.684 in the training and testing phases, respectively, and Mean Absolute Error (MAE) of 0.98 and Root Mean Square Error (RMSE) value of 1.24 for the 2019–20 season. The DLMLP test R2 was 42.2% more than the Ordinary Least Squares Regressor (OLS), while the MAE and RMSE were 37.97% and 38.61% less than the OLS regressor for wheat crop yield estimation. The soil health-based DLMLP model gave satisfactory yield estimation accuracy in the absence of validation of soil health parameter values for the preceding years-2015–16 till 2018–19 wheat seasons. This study's novel feature is that it estimates soil health parameters for the early stages of wheat crop growth when soil lies mostly exposed and utilises them for crop yield prediction.