Beyond Boundaries: Exploring Innovative Approaches to Satellite-Based Crop Classification With Machine Learning

This research explores the technologies, new methodologies using remote sensing methods.High resolution , Multi-spectral satellite imagery to classify the accurate different crop type through the satellite images.This is productive approach for land use management and precision agriculture.These, study utilize the encyclopedic dataset, showcasing the models flexibility in real world cases.For the crop classification multiple algorithms have been used.In this work machine learning methods , algorithms are applied on high resolution images to achieve the accurate prediction of crop classification.To achieve accurate predictions selected agricultural region having accurate ground truth data.The models are used in this researches are (SVM)Support Vector Machine,(CNN)Convlutional Neural Network,(DT)Decision Tree, (RF)Random Forest,(DL)Deep Learning,(KNN)K-Nearest Neighbour are applied on crop classification.This survey giving comprehensive overview of crop classification techniques and their flexiblity to establish efficiency, accurate prediction and useful for landscape management , monitoring the different crops.