Disease detection in solanaceous crops using one-stage detectors

Ahmad Radzi, Syafeeza and Khan, Asar and Amsan, Azureen Naja and Abdul Hamid, Norihan and Abd Razak, Norazlina and Rahaman, Shahid (2025) Disease detection in solanaceous crops using one-stage detectors. Journal of Advanced Research Design, 134 (1). pp. 1-13. ISSN 2289-7984

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Abstract

Agriculture plays a crucial role in sustaining and ensuring the continuous food supply. Crop disease may cause the negative impact on agriculture due to the decrease of yield production. Machine vision technology such as object detection can overcome the issue of early disease detection with more efficient way compared to the conventional method such as manual observation. This study utilizes two one-stage detectors namely YOLOv8 and SSDLite-MobilenetV3 to analyze the efficiency and accuracy of both models to perform crops disease detection. A total of 23 species of plants dataset are used taken from PlantVillage dataset. The datasets are divided into 70:20:10 ratio which results in total of 9,936 for training, 1,414 for validation, and 510 for testing images are used. The result shows that YOLOv8 has better performance with 86% accuracy compared to 82% for SSDLite-MobilenetV3. YOLOv8 also surpassed SSDLite-MobilenetV3 in terms of inference time by 76.6% faster with 8.2ms and 35.5ms respectively.

Item Type:	Article
Uncontrolled Keywords:	Agriculture, Crop disease, deep learning, Object detection, One-stage detector
Divisions:	Faculty Of Electronics And Computer Technology And Engineering
Depositing User:	Norfaradilla Idayu Ab. Ghafar
Date Deposited:	21 Jul 2026 04:03
Last Modified:	21 Jul 2026 04:03
URI:	http://eprints.utem.edu.my/id/eprint/30196
Statistic Details:	View Download Statistic

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