Abstract: To boost plant growth and output, farmers need automated disease monitoring of plants rather than human monitoring. Many plant diseases have the potential to cause significant losses or possibly no harvest. Anthraconose, bacterial blight, cercospora leaf spot, and healthy leaves were the subjects of this study's focus on several alterneria alternata diseases. We apply three stages of clustering on the initial image filtering. As a result, we developed a modern technique in this study to detect diseases linked to both leaves and fruits. We overcame the shortcomings of the conventional eye monitoring method by using a digital image processing methodology for rapid and accurate plant disease identification.
Keywords: Convolutional Neural Network (CNN), Support Vector Machine (SVM), Confusion matrix
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DOI:
10.17148/IJARCCE.2023.12481
