Abstract

This paper presents an optimized system for plant disease detection using transfer learning techniques. Plant diseases
significantly affect crop yield and quality, making early detection making early detection crucial for sustainable agriculture. The
proposed system employs pre-trained deep learning models like ResNet, VGG, and MobileNet to classify plant leaf diseases
from images The methodology includes image collection, preprocessing, data augmentation, feature extraction, and
classification. Transfer learning enables the model to leverage knowledge from large-scale datasets, reducing training
duration and improving accuracy even with limited data. The organization is capable of real-time disease detection and can be
deployed on mobile or web platforms edge devices to assist farmers. Experimental results show that the proposed system
achieves high accuracy and efficiency compared to traditional approaches.