Enhancing Potato Disease Classification with Inception V3-Based Deep Learning Model

Potato cultivation is a cornerstone of global foods production, however it is always threatened with the aid of the usage of a range of illnesses that can notably limit crop yield and quality. Traditional methods of disease detection are normally time-consuming and error-prone, prompting the adoption of deep gaining information of strategies for automated illness classification. In this research, we suggest a sturdy and right reply for potato disease classification, leveraging the electrical energy of InceptionV3, a present day convolutional neural neighborhood (CNN) architecture. Our model is trained to classify potato vegetation into three classes: Early Blight, Late Blight, and Healthy, specifically based totally on leaf conditions. Extensive experimentation on a dataset of potato leaf portraits demonstrates the efficacy of our approach, with a classification accuracy of 96.38%. Our discover out about contributes to the growing physique of search for in the challenge of plant disease detection, presenting an computerized and surroundings pleasant reply to embellish crop administration practices. This search for has the attainable to revolutionize disorder prognosis in agriculture, making positive properly timed intervention and sustainable ingredients production.