Abstract
Agricultural productivity is crucial for global economic development and growth. Crop diseases can severely risk to Nation’s food security, economic resources, environmental sustainability and agricultural output. Crop disease detection at early stage not only reduce losses for farmers but also improve crop yields. As agricultural technology and artificial intelligence advance, research into sustainable agricultural development becomes increasingly crucial. Plant diseases significantly impact the yield and quality of potatoes, and manually diagnosing these diseases can be both time-consuming and complex due to the required expertise. This paper introduces PotatoLeaf Insight System that employs U-Net technique for image segmentation followed by VGG19, for feature extraction through transfer learning. The model is trained and evaluated using a dataset of potato leaf images, covering early blight disease, late blight disease, leaf roll disease, Verticillium wilt disease, and healthy leaves. This AI driven system provide overall accuracy of 94% and useful for farmers to take necessary care of their crops in case detected with disease.
Published Version
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