Advances in Crop Classification Using Satellite Imagery: A Comprehensive Review

Classifying crop is essential for monitoring agricultural landscapes, managing re sources and assessing crop health. Crop classification is revolutionized by recent developments in satellite imagery and machine learning, with various datasets like multispectral, hyper-spectral, and SAR. Various machine learning and deep learning algorithms, such as CNNs, ANNs, and ensemble methods, play a pivotal role in the automation of crop classification. Ongoing research in this field aims to address the limitations like the availability of ground truth data, cloud cover, and seasonal variability. Future possibilities in this field include integration of multi-sensor data, enhancing temporal and spatial resolution, and enabling real-time monitoring for precision agriculture and sustainable land management. This review paper is a valuable resource for researchers in agriculture, remote sensing, and machine learning. This paper also offers insight into the cutting-edge techniques and the transformative potential of crop classification in agriculture and global food security.