Design and Evaluation of a Transfer Learning Model for Ailment Recognition in Plant Images

The agriculture sector is still working to mitigate the detrimental effects of plant diseases on crop production and food security. Accurate and prompt disease diagnosis is essential to preventing extensive crop damage. Deep learning, a subfield of machine learning, has generated a great deal of interest and has astounding potential for automating the identification of plant diseases from images. This article presents a comprehensive overview of the advancements, strategies, and potential applications of CNN for the diagnosis of plant diseases.. The paper covers important facets of the deep learning-based methodology, such as dataset curation and collection, picture preprocessing methods, architectural decisions, and training methods. The effectiveness of CNN models for detecting plant diseases is discussed in this article, with a special emphasis on convolutional neural networks (CNNs) as playing a key role.