Published Paper Details:

The health of grape plants is crucial for ensuring high-quality vineyard yields and maintaining the economic sustainability of viticulture. Effective disease detection is a pivotal aspect of modern agricultural management, as diseases such as black measles, leaf blight, and black rot can significantly impact crop production. This paper discusses research on some advanced methods in the field of grape plant disease detection by incorporating machine learning algorithms and image processing techniques. In this paper, the use of spectral imaging, neural networks, and field-based monitoring systems for early, precise, and cost-effective diagnosis of diseases is discussed and the user interface is in the regional language Kannada for better usability of farmer. By addressing the limitations of traditional manual inspection methods, this research aims to highlight innovative approaches that enhance efficiency and reduce the environmental impact of disease management practices. The findings underscore the potential of precision agriculture in revolutionizing disease control strategies in viticulture.