Abstract: Monitoring plant health and finding plant diseases are essential for sustainable agriculture. Disease identification and preventative strategies are a significant challenge because of the rapid rise in a variety of diseases and the low level of knowledge of these ailments. Early discovery gives more time to implement the proper preventative measures. Since arecanut plants are very vulnerable to a variety of pests and diseases, the suggested method is used to identify arecanut leaf diseases and divide them into four groups: healthy, diseased leaves with yellow spots, leaf blight, and yellow leaves. The project main goal is to create a YOLO model that analyses leaf images and can be used to find plant diseases.
Keywords: YOLO, Dataset, Epochs, Pixels.
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DOI:
10.17148/IJARCCE.2023.126106
[1] Shayana G G, Priyanshu Das Roy, Sathwik K Shetty, Prajay Jaykar Poojary, Vidya Dudhanikar, "DETECTION OF DISEASES IN ARECANUT LEAVES USING YOLOv8," International Journal of Advanced Research in Computer and Communication Engineering (IJARCCE), DOI: 10.17148/IJARCCE.2023.126106
