DeepFoliage: A Unified Approach for Plant Disease Detection using Deep Learning Techniques

Plant diseases pose a serious danger to crop productivity and quality, making the agricultural sector one of the main obstacles to maintaining food security. The word “deep foliage” was coined to combine the terms “deep” (referring to deep learning) and “foliage”, which refers to leaves or other plant life. In particular, illnesses that impact plant leaves are the main focus of this work, which aims to show how deep learning techniques can be used for plant disease analysis and diagnosis. The proposed method combines two algorithms that were trained on a varied dataset of plant pictures, including different crops and disease presentations, to create a hybrid deep learning technique. The proposed method combines two algorithms that were trained on a varied dataset of plant pictures, including different crops and disease presentations, to create a hybrid deep learning technique. With the ability to recognize and classify possible diseases in plant foliage with accuracy, the deep learning model picks up complex patterns and features. “Deep Foliage” enables prompt intervention to lessen the impact of illnesses on agricultural productivity by offering rapid and precise diagnosis. By lowering the reliance on antiquated, labor-intensive disease detection techniques, the project seeks to promote sustainable agricultural practices.