The growing demand for agricultural goods necessitates technological improvement, using Geographic Information Systems (GIS) and remote sensing methods to study farmland suitability and environmental factors. These methods are used to accurately predict land usage and agricultural yield. Brazil and India are the two countries that produce the most sugarcane globally, and remote sensing data can be combined with image processing, data mining, GIS, and GPS to recover crop data. Remote sensing can efficiently identify small changes in crops, such as the dual polarisation traits of sugarcane crops. The study aims to find machine learning techniques in addition to quantitative measurement indices like NDVI, which track vegetation. Satellite imagery data can help farmers identify dense and sparse vegetation for sugarcane growth, assessing the plot area's soil and water availability. Ratio vegetation indices are useful for sparse vegetation, while enhanced indices are better for dense canopy. The best month for harvesting and cutting sugarcane can be determined from vegetation indices. However, ground truth data is not easily available in India, necessitating the use of satellite imagery data for monitoring sugarcane growth.https://doi.org/10.1007/s11042-025-20950-8 is the url for the paper published