Solanum Tuberosum Leaf Disease Classification

Abstract: Farmers cultivating solanum tuberosum  often experience significant financial setbacks due to plant diseases that adversely affect crop health and yield. Early blight and late blight are particularly destructive diseases that, if not detected and managed early, can reduce crop productivity. Here we will use machine learning approach, Convolutional Neural Networks (CNNs), that detects and classifies these diseases, on its own, through image analysis of Solanum Tuberosum leaves. The developed system offers a rapid, accurate, and user-friendly diagnostic tool that enables farmers to take prompt action to manage and mitigate disease spread effectively. The primary goal is to reduce crop losses and enhance disease management, ultimately contributing to improved agricultural efficiency. Early disease detection through this technology allows for more effective disease control, leading to healthier crops and improved yields. The solution not only enhances crop monitoring but also provides an affordable way to protect farmers' livelihoods, fostering greater sustainability and productivity in Solanum Tuberosum farming.

Solanum Tuberosum Disease Detection, Image Processing, Neural Network

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