Paper Key : IRJ************837
Author: Yash Nimbalkar,Aditya Kadam,Hareshwar Avhad ,Sarthak Mete
Date Published: 12 Apr 2024
Abstract
Plant disease prediction by deep learning will make a definite good impact on the environment. Plant diseases significantly impact agricultural productivity, leading to substantial economic losses and food security threats. Timely and accurate disease detection is crucial for effective disease management. Traditional methods rely on visual inspection by trained experts, which can be time-consuming and subjective. In recent years, deep learning has emerged as a powerful tool for automating plant disease diagnosis. This paper provides a comprehensive review of state-of-the-art deep learning techniques applied to plant disease detection. The study begins by presenting an overview of plant diseases, their economic implications, and the challenges associated with conventional detection methods. It then delves into the fundamentals of deep learning, emphasizing convolutional neural networks (CNNs) and their suitability for image-based tasks. Various pre-processing techniques, such as data augmentation and normalization, are discussed to enhance model performance. The review highlights benchmark datasets commonly used in plant disease detection research and evaluates the performance of prominent deep learning models, including AlexNet, VGG, Inception, Res-Net, and their variants. Transfer learning techniques and their effectiveness in adapting pretrained models to specific plant disease detection tasks are also explored
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