Manuscript received October 6, 2024; revised November 5, 2024; accepted December 2, 2024; published April 25, 2025.

Abstract—Farming is the main source of income, so crop development must be continuously watched and given the highest attention possible for the farmers. Precision agriculture is a farming management approach that uses multispectral satellite data to monitor, measure, and adjust to temporal and geographical variability in order to improve the sustainability of agricultural output. Sugarcane is a cash crop and is used in this study as researchers are focusing more on success in sugarcane development. Detecting dense and sparse vegetation for the individual plots of the farmers helps to understand that the plot area has favorable soil and water conditions for sugarcane growth. The sparse vegetation indicates that the area has slopes and water is not retained creating problems in the growth of the sugarcane. The Remote Sensing spectral bands are used and the vegetation indices like Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI), and Ratio Vegetation Index (RVI) are used in the sugarcane canopy. EVI works for dense canopy and RVI for sparse vegetation as shown by the research done in this paper. The Machine Learning (ML) further also helps to detect the sparse and dense vegetation and the accuracy of all the classifiers is compared for the same. The survey on different Machine Learning techniques applied to remotely sensed data of sugarcane crops is done in this research work. This study aims to monitor sugarcane crop health by detecting sparse and dense vegetation using vegetation indices, and it evaluates the performance of different ML classifiers for precision agriculture. The same plot of the farmer can be monitored each month to find the change detection and further, the cause of sparse vegetation in the particular plot can be diagnosed with the help of enhanced vegetation indices in future work. To locate healthy vegetation, RS Sensing uses the Normalised Difference Vegetation Index (NDVI) and it gets saturated at grand growth stages so the novel method is enhance vegetation indices and ratio vegetation index which can be used to monitor at grand growth stages along with ML models as shown in this research work.

Keywords—remote sensing, Geographic Information System (GIS), agriculture, machine learning, Normalized Difference Vegetation Index (NDVI)

Cite: Mansi Kambli and Bhakti Palkar, "Categorisation of Vegetation Using Machine Learning and Remote Sensing Methods," Journal of Image and Graphics, Vol. 13, No. 2, pp. 174-189, 2025.

Copyright © 2025 by the authors. This is an open access article distributed under the Creative Commons Attribution License (CC-BY-4.0), which permits use, distribution and reproduction in any medium, provided that the article is properly cited, the use is non-commercial and no modifications or adaptations are made.