Comparison Study of Corn Leaf Disease Detection based on Deep Learning YOLO-v5 and YOLO-v8

Nidya, Chitraningrum and Lies, Banowati and Dina, Herdiana and Budi, Mulyati and Indra, Sakti and Ahmad, Fudholi and Huzair, Saputra and Salman, Farishi and Kahlil, Muchtar and Agus, Andria (2023) Comparison Study of Corn Leaf Disease Detection based on Deep Learning YOLO-v5 and YOLO-v8. Journal of Engineering and Technological Sciences /JET, 56 (1). pp. 61-70. ISSN 2337-5779

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Abstract

Corn is one of the primary carbohydrate-rich food commodities in Southeast Asian countries, among which Indonesia. Corn production is highly dependent on the health of the corn plant. Infected plants will decrease corn plant productivity. Usually, corn farmers use conventional methods to control diseases in corn plants. Still, these methods are not effective and efficient because they require a long time and a lot of human labor. Deep learning-based plant disease detection has recently been used for early disease detection in agriculture. In this work, we used convolutional neural network algorithms, namely YOLO v5 and YOLO-v8, to detect infected corn leaves in the public data set called ‘Corn Leaf Infection Data set’ from the Kaggle repository. We compared the mean average precision (mAP) of mAP 50 and mAP 50-95 between YOLO-v5 and YOLO-v8. YOLO-v8 showed better accuracy at an mAP 50 of 0.965 and an mAP 50-95 of 0.727. YOLO-v8 also showed a higher detection number of 12 detections than YOLO-v5 at 11 detections. Both YOLO algorithms required about 2.49 to 3.75 hours to detect the infected corn leaves. This all-trained model could be an effective solution for early disease detection in future corn plantations. Keywords: convolutional neural network; corn leaf disease; deep learning; disease detection; YOLO models.

Item Type:	Article
Subjects:	ILMU KESEHATAN > ILMU KESEHATAN UMUM > Teknik Penyehatan Lingkungan
Depositing User:	Universitas Nurtanio
Date Deposited:	16 Jun 2025 02:39
Last Modified:	16 Jun 2025 02:39
URI:	http://repository.unnur.ac.id/id/eprint/455

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