AI-Enhanced Deep Learning for Leaf Disease Classification and Pesticide Recommendation: An Ample Review and Framework

In plant health forecasting and investigation, leaf images research using deep learning serves a primary function. Nevertheless, the challenges and gaps are perceived during field deployments. Mainly, the limitation in samples, field images, analysis, and pesticide suggestions. This ample review produces state of the art models, analyzes the available datasets, parameters, converses the real-time problems, and also recommends new research directions in the field of targeted pesticide recommendation systems. One of the prime proposal explores a Generative AI-enhanced Deep Learning (DL) framework for an effective and precise leaf disease classification and pesticide recommendation. The initial task starts with preprocessing inputs to smoothen the dimensions and quality. A generative approach Variational Autoencoder (VAE), increases image resolution by eliminating noise and emphasizing diseaseprone sites. The processed images are then analysed using a Convolutional Neural Network (CNN) powered deep learning model (example: ResNet or EfficientNet) for feature extraction and classification, and for detecting diseases with high precision. The detected disease initiates a targeted pesticide recommendation module that suggests optimal treatments based on the diagnosis. This approach integrates advanced AI and zone-specific knowledge, guaranteeing accurate disease detection and fitted pesticide advice. The outcome of this research aims to optimize agricultural productivity, reduce chemical over use, and upgrade sustainable farming practices.