Potato Leaf Disease Identification Using Deep Learning: Implementation

Abstract— Agricultural productivity is increasingly threatened by foliar pathologies, which can devastate potato yields if left unchecked. This paper proposes an integrated Potato Leaf Disease Monitoring System that transitions from traditional manual inspection to automated, AI-driven diagnostics. By utilizing state-of-the-art Convolutional Neural Networks (CNNs), specifically VGG16 and EfficientNetB0, the system classifies digital leaf images into categories of Early Blight, Late Blight, or Healthy. Beyond simple identification, the proposed framework integrates a web-based interface and IoT compatibility to provide real-time intervention strategies and pesticide recommendations. Experimental results demonstrate a classification accuracy of up to 98.94%, offering a scalable and cost-effective tool for precision agriculture and global food security. Keywords— Precision Agriculture, Deep Learning, CNN, EfficientNetB0, Crop Pathology, Image Processing.