Plant diseases remain a significant threat to global agriculture, necessitating rapid and
accurate detection to minimize crop loss. This paper presents a lightweight, end-to-end system for plant
leaf disease detection and severity estimation, optimized for real-time field deployment. We propose a
custom Convolutional Neural Network (CNN), built using PyTorch, trained on the PlantVillage dataset
to classify leaves as healthy or diseased with a test accuracy of 92.06%. To enhance its practical relevance,
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