(Study Program of Electrical Engineering, University of Bengkulu, Indonesia)(2) Ruvita Faurina
(Study Program of Informatics, University of Bengkulu, Indonesia)(3) Sahrial Ihsani Ishak
(Study Program of Informatics, University of Bengkulu, Indonesia)(4) Ferzha Putra Utama
(Study Program of Informatics, University of Bengkulu, Indonesia)(5) Kusmea Dinata Dinata
(Agricultural Technology Study Center, Agriculture Department, Province of Bengkulu, Indonesia)(6) Novalio Daratha
(Study Program of Electrical Engineering, Indonesia)*corresponding author
Abstract.This research developed a pest detection model for Indonesian red chili pepper based on fine-tuned YOLOv5. Indonesian red chili pepper is the third largest vegetable commodity produced in Indonesia. Pest attacks disrupt the quantity and quality of crop yields. To control pests effectively, it is necessary to detect the type of pest correctly. A viable solution is to leverage computer vision and deep learning technologies. However, no previous studies have developed a pest detection model for Indonesian red chili pepper based on this technology. YOLOv5 is a variant of the YOLO object detection algorithm, which has major advantages in terms of computation cost and execution speed. The dataset comprises 4,994 image files collected from a chili plantation in Bengkulu province, Indonesia, covering 4 different classes and a total of 10,683 pests. The image is 1216 x1216 px with the smallest, largest, and average object dimensions of 2%, 35%, and 4% of the image dimensions. The training model used is fine-tuning YOLOv5s with variations of patience as an early stop parameter of 100, 200, and 300. The evaluation of the trained model is based on train loss, validation loss, and mAP@0.5:0.95, the best-trained model is the 445th epoch on patience 100 with the best confidence value of 0.321 and the highest TF1 of 0.74. From the best-trained model testing on the test dataset, the mAP@0.5 performance for all classes is 81.3%. The model not only detected large pests but was also able to detect objects that were small in size compared to the image size. The best-trained model's best mAP@0.5 performance and speed are 82.6% and 20 ms/image, or 50 fps on NVIDIA P100 GPU.
KeywordsDeep learning; Yolo; Indonesia red chili paper; Pest detection
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DOIhttps://doi.org/10.26555/ijain.v9i3.864 |
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