Implement A Transfer Learning Model For Analyzing, Identifying, And Predicting Plant Diseases On Plant Leaf Image Dataset

Abstract

The early detection of plant diseases is vital for ensuring crop health and maximizing agricultural productivity. This study presents a comprehensive evaluation of advanced transfer learning models for automatic classification of plant diseases using leaf image datasets. Five deep learning architectures—CNN, Inception V3, ResNet, VGG16, and VGG19—were implemented and compared to identify the most effective model for generalized plant disease detection. The experimental workflow involves image preprocessing, augmentation, and feature extraction to enhance model performance. Datasets comprising 87,000+ images from Kaggle’s PlantVillage were used for training and evaluation. Each model was assessed using accuracy and loss metrics under consistent hardware and software environments. Results indicate that the VGG16 model consistently outperforms others in classification accuracy and computational efficiency, making it a robust choice for large-scale and diverse agricultural applications. This research contributes a scalable and effective solution for automated disease identification, supporting precision agriculture and reducing reliance on manual inspection.