Detection Model Development for Chili Leaves Disease Based on YOLOv8

Chili plants are the main horticultural commodity in Indonesia, with dominant production in the agricultural sector. Data from the Central Statistics Agency (BPS) from 2021 to 2023 shows that chili is consistently in the top five vegetable productions. However, production is often hampered by diseases caused by pathogenic microorganisms, so disease identification is very important. The current method performed by chili farmers is based on visual inspection of chili leaves, which is considered subjective from one individual to another. This study produces primary data in the form of images of chili leaves in five classes, namely healthy leaves, curly leaves, yellow virus leaves, spotted leaves, and whitefly leaves which have been published in Roboflow. Through six times of dataset training of the deep learning model using YoloV8, the best model was obtained with an Average Precision (AP) value at Intersection over Union 0.5 and 0.5-0.95 of 85.1% and 67.3%, respectively. This model is optimal for applications that require fast and accurate results.