Hybrid Intelligent Model for Accurate Plant Disease Detection and Agricultural Productivity Enhancement

Plant diseases are key constraints to agriculture and directly impact on crop yield and food security. early and reliable diagnosis of the disease is crucial to increase agriculture production and minimize economic loss. this thesis proposes a Hybrid intelligent model for precise plant disease detection and agricultural productivity improvement using deep learning combined with optimization.
keywords-plant disease identification,optimization algorithms, deep learning, Hybrid Intelligent model.