Crop Disease Identification Using Computer Vision And Machine Learning Techniques

Abstract

The paper examines various classification methods dedicated to the identification various illnesses of plants, emphasizing the crucial role of early detection in preserving crop quality and yield. It delves into different approaches, including image processing, machine learning, artificial
neural networks, and, more prominently, deep learning. With a focus on emerging methods for in-depth comprehension, the review provides a detailed analysis starting from traditional machine learning methods. It outlines the targeted crop diseases, the utilized models, data sources,
and performance metrics employed across studies for disease identification. The review highlights deep learning’s superior accuracy compared to traditional methods and identifies important elements that have an influence its performance. By documenting these approaches, the paper
aims to enhance accuracy and reduce response time in plant disease identification, with specific attention given to efforts in diagnosing diseases in Indian agricultural settings using authentic datasets.