Revolutionizing Crop Disease Management With Deep Learning Classifiers For Rice Leaf Images

Abstract

Crop disease management has been a crucial aspect of agriculturefor centuries, with farmers traditionally relying on visual inspection and expert knowledge to identify diseases.As agriculture advanced, techniqueslikechemicaltreatmentsandresistantcropvarietieswere developed. The advent of digital tools and data processing in recent decades allowed for more precise agricultural practices, but the process of detecting crop diseases still required substantial human expertise and time.Theobjectiveis to develop an automated system that utilizes deep learning classifiers to accurately detect andclassify rice leaf diseases from images, thereby providing a faster, more efficient, and scalable approach to crop disease management. Visual inspection by farmers or experts. Consultation with agricultural extension services. Use of reference books or charts for disease identification. The proposed system leverages deep learning to analyze rice leaf images and automatically detect variousdiseases. It involves collecting a large dataset of rice leaf images, preprocessing them for consistency, and training a deep learning model to distinguish between healthy and diseased leaves. The system then classifies the types of diseases present, providing actionable insights that can be used by farmers to make informed decisions. Additionally, a user-friendly interface is developed for easy interaction, allowing farmers to upload images and receivereal-time results.
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