The Analysis of Architectural YOLOv5 Convolutional Neural Networks for Detecting Apple Leaf Diseases - Erkamim, Moh.; Subarkah, Muhammad Zidni; Soelistijono, R. (Green Engineering Society, 2025)
Apple cultivation is crucial to agricultural economies, particularly in regions with sub-tropical climates, such as Indonesia, where apple farming is expanding rapidly. However, managing diseases and pests is essential for maintaining optimal crop yields, as they can significantly reduce production. Among the common diseases affecting apple trees are Scab, Black Rot, and Cedar Apple Rust, which primarily impact leaves and threaten the total health of the plant. Therefore, this research aimed to develop an effective model for detecting apple leaf diseases using the architectural YOLOv5 Convolutional Neural Networks (CNNs). The analysis was conducted between November 2022 and February 2023 at the Smart City Information System (SIKC) laboratory, including 120 apple leaf samples collected from Tawangmangu. Additionally, secondary data containing 30 images for each disease category, consisting of Healthy, Scab, Black Rot, and Cedar Apple Rust, were used as a benchmark. The performance of YOLOv5 was evaluated based on several metrics, including Precision, Recall, mAP@0.5, and mAP@0.5:0.95. The results showed that Cedar Apple Rust was the most prevalent disease identified among the samples. YOLOv5 performed exceptionally well in detecting disease symptoms, achieving a Precision score of 0.810, Recall of 0.981, mAP@0.5 of 0.950, and mAP@0.5:0.95 of 0.765 on the test dataset. These results showed that the proposed model was highly accurate and reliable for the early detection of apple leaf diseases, offering significant potential for improving disease management strategies and increasing the efficiency of apple production.