A YOLOv10-based Approach for Banana Leaf Disease Detection
Received: 25 March 2025 | Revised: 16 April 2025 | Accepted: 23 April 2025 | Online: 4 June 2025
Corresponding author: Rakan Alanazi
Abstract
Leaf disease detection plays a crucial role in modern agricultural management, enabling early intervention to minimize crop losses. This paper explores the application of the YOLOv10 model for detecting and classifying banana leaf conditions with high accuracy. A publicly available dataset of 938 images was used, categorized into five classes, namely Black-Sigatoka, Healthy-Leaf, Panama-Disease, Potassium-Deficiency, and Yellow-Sigatoka. The model achieved a mean Average Precision (mAP@0.5) of 88.85%, a precision of 91.22%, and a recall of 85.06%, demonstrating strong detection capabilities. These findings highlight the effectiveness of YOLOv10 in advancing automated disease detection, providing a reliable tool for precision agriculture. The model’s ability to accurately classify multiple leaf conditions can aid farmers in proactive disease management, ultimately enhancing crop health and sustainability.
Keywords:
object detection, deep learning, YOLOv10Downloads
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