An Intelligent Approach to Grape Leaf Disease Diagnosis Through Machine Learning

This study underscores the pressing necessity for early and accurate diagnosis of grape leaf diseases, advocating for an automated solution that harnesses the synergistic power of image processing and machine learning. Employing methodologies such as Naïve Bayes, Gradient Boosting, and Random Forest, the primary objective is to proficiently classify grape leaves as either healthy or afflicted by diseases. The model casts its focus on prevalent grape leaf maladies, including Grapes Healthy Leaf, Grape Black Rot,Grape Black Measles, Grape Isariopsis leaf. The process commences with the meticulous preparation of grape leaf images, followed by the extraction of crucial features, which serve as the foundation for the application of machine learning techniques. The viability of the grape crop sector hinges on the efficacy of this innovative approach, which has the potential to significantly curtail the spread of diseases in grape fields while enhancing the precision of disease identification. This study serves as a compelling demonstration of the formidable effectiveness of machine learning algorithms in the detection of grape leaf diseases, thus ushering in the prospects of automation and improved decision-making within the realm of agriculture.