Data-Driven Prediction of Nitrogen Stress and Crop Yield in a Maize -Wheat Cropping System

Abstract

The study has attempted to determine leaf-N content in a cropping system consisting of Wheat and Maize using plant sensors like GreenSeeker and SPAD meter (chlorophyll meter) and use machine learning models so that crop yields based on inputs can be predicted of data on nitrogen doses, NDVI values, SPAD values, and leaf N content. The data recorded from field experiments on wheat and maize crop variable fertilized by N (wheat: 0, 30, 60, 90, 120, 150, 180 and 240 kg N/ha; maize: 40, 80, 120, 160, 200, 240 and 300 kg N/ha) were used. A pair plot analysis showed a positive association between leaf N and NDVI, between leaf N and SPAD values, and between NDVI and SPAD values; all these parameters had a positive correlation with N- application rates. For yield prediction, the model calibrated for wheat could validate yield prediction for maize as well. Random Forest and Support Vector Machines showed reproducible performance (consistent)  on both datasets (maize and wheat), with accuracies of 85.71% and 100%, respectively, on the original dataset.