The Use Of Multispectral Radio-meter (msr5) Data For Wheat Crop Genotypes Identification Using Machine Learning Models

This article discusses the use of satellite remote sensing to identify crop genotypes for agricultural activities monitoring. It examines the use of multispectral radiometer data to detect the genotypes varieties of crop yield. The study used three genotypes of wheat crop (Aas-‘2011’, ‘Miraj-’08’, and ‘Punjnad-1) fields and temporal data collected using an efficient multispectral Radio Meter (MSR5 five bands). Machine learning models such as Extra Tree Classifier (ETC), Random Forest (RF), Support Vector Machine (SVM), Decision Tree (DT), Logistic Regression (LR), k Nearest Neighbor (KNN) and Artificial Neural Network (ANN) were used to analyze the data. The results showed that ANN and random forest algorithm achieved maximum accuracy of 97% and 96% on the test dataset.