A Comprehensive Study on Crop Disease Detection Using Machine Learning and Deep Learning Models

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Crop diseases can significantly affect agricultural productivity, resulting in huge economic losses for farmers and food shortages for the population. To address this challenge, several researchers have explored the use of machine learning and deep learning algorithms for crop disease detection. These algorithms use image processing techniques to identify and classify diseases in crops and have shown promising results in accurately detecting and diagnosing crop diseases. In particular, deep learning models, especially convolutional neural networks (CNNs), have demonstrated high accuracy in crop disease detection. Researchers have also developed various mobile and web applications based on these models, which can help farmers identify and manage crop diseases in real time. However, there is still a need for more research to improve the accuracy and effectiveness of these models and to ensure their scalability and accessibility for use in the field. Overall, the application of machine learning and deep learning algorithms for crop disease detection holds great potential for addressing the challenges of crop disease management and improving agricultural productivity. This paper studied various pieces of research to enhance and conclude the best algorithm with high accuracy, precision, and recall for the detection of crop diseases.