BLACK ROT DISEASE DETECTION IN GRAPE PLANT (VITIS VINIFERA) USING COLOUR BASED SEGMENTATION

The cultitechniques for the early and accurate detection of Black Rot disease. Our methodology involves the acquisition of high-resolution images of grape leaves and clusters. These images are then subjected to a color-based segmentation process to isolate regions of interest that exhibit symptoms of Black Rot. The results demonstrate the efficacy of the proposed approach. The integration of Color-Based Segmentation and Machine Learning enables accurate and automated identification of Black Rot disease in grape plants. This system holds promise for early disease intervention, enabling growers to take timely actions to mitigate the spread of Black Rot and prevent potential yield vation of Grape Plants (Vitis vinifera) is of significant economic importance, but the prevalence of diseases such as Black Rot poses a serious threat to grape yields and quality. In this study, we propose a novel approach to address this issue by combining Color-Based Segmentation and Machine Learning losses. In conclusion, this study bridges the gap between agricultural practices and modern technology, offering a reliable and efficient tool for Black Rot disease detection. The combination of Color-Based Segmentation and Machine Learning presents a promising avenue for sustainable grape cultivation by aiding in the preservation of grape plant health and overall yield maximization.