Paper Contents
Abstract
Agriculture is the backbone of the Indian economy. It is essential to about 65% of the population and makes a substantial contribution to the GDP. The most crucial consideration is decreased agricultural productivity as a result of illness. Disease identification is crucial to preventing a decline in the quantity and quality of agricultural goods. To identify plant diseases, a large number of studies are now underway. The detection of plant diseases can benefit several agricultural practices in addition to optimizing crop production. Agriculture-related research and advancements are advancing geometrically in the disparate field of image processing.In order to reduce agricultural output losses, this study work aims to identify and monitor sugarcane diseases such as red rot, smut, wilt, and yellow leaf early on. Images are collected, processed, segmented, features are extracted, classified, and diseases are classified in this study using contemporary image processing techniques. Once the sickness has been detected, the study team sends out alerts. Additionally, these two recommended methods were applied to the Graphical User Interface (GUI) with the use of the proper software program. Since the proposed approach decreases human work, increases productivity, and yields quicker, more precise outcomes, the community should gain from it.
Copyright
Copyright © 2025 P.V.RAGHUNATH. This is an open access article distributed under the Creative Commons Attribution License.
