Algorithmic Approaches to Plant Disease Detection: A Comparative Analysis

Beijing Academy, 100018 Beijing, China

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Abstract

Every year, plant diseases cause considerable economic damage to agriculture, and the identification of plant diseases in the past relied on experts to determine the diseases through checking abnormal features of plants manually, which is time-consuming and labor-intensive. With the development of machine learning, models based on machine learning algorithms can now to identify diseases quickly using plant images. There are some algorithms used in identifying diseases, such as Convolutional Neural Networks (CNN), vision transformer (ViT), and K-means clustering. This study reviews the work already done by researchers. In turn, this work summarizes the advantages and limitations of some past studies and compares the popular algorithm CNN and other new algorithms ‘performances through difference dimensions. By concluding the techniques used in different studies to enhance accuracy and ability of generalization thorough image preprocessing, selection of datasets, and use of new algorithms, and this work explores the potential and advantages of various algorithms to inform the development of improved models. It identifies key research gaps and offers recommendations for future studies.