Abstract
The field of plant disease classification has recently been seen to be a vast area of research. Recent years have witnessed a growing interest in the application of artificial intelligence (AI) and machine learning (ML) techniques for various tasks. Among these techniques, deep learning (DL) algorithms have received significant attention and demonstrated remarkable results. This review article aims to give a comprehensive overview of the current advancements in the field of plant disease classification using AI and ML, with a focus on DL approaches. The paper will cover key literature in the field, including recent advances and challenges, and will discuss the most commonly used algorithms, such as Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), and Transfer Learning (TL). Additionally, the review will highlight the various applications of AI in plant disease classification, including the use of images, genomic data, and environmental data. The paper will also provide insights into the limitations and opportunities of AI-based plant disease classification, as well as future directions for research in this field. The goal of this paper is to provide a comprehensive overview of the field and to serve as a useful resource for researchers and practitioners in the area of plant disease classification using AI and ML.
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