YOLO for Early Detection and Management of Tuta absoluta-Induced Tomato Leaf Diseases

Harisu Abdullahi Shehu ^1* Aniebietabasi Ackley ¹ Mark Marvellous ² Ofem Eteng ³ Md. Haidar Sharif ⁴ Huseyin Kusetogullari ⁵

• ¹ Victoria University of Wellington, Wellington, New Zealand
• ² University of Calabar, Calabar, Cross River, Nigeria
• ³ BRALN, LTD, Port Harcourt, Nigeria
• ⁴ St. Mary's College of Maryland, St. Mary's City, Maryland, United States
• ⁵ Blekinge Institute of Technology, Karlskrona, Blekinge, Sweden

The agricultural sector faces persistent threats from plant diseases and pests, with Tuta absoluta posing a severe risk to tomato farming by causing up to 100% crop loss. Timely pest detection is essential for effective intervention, yet traditional methods remain labor-intensive and inefficient. Recent advancements in deep learning offer promising solutions, with YOLOv8 emerging as a leading real-time detection model due to its speed and accuracy, outperforming previous models in on-field deployment. This study focuses on the early detection of Tuta absoluta-induced tomato leaf diseases in Nigeria, the largest tomato producer in Sub-Saharan Africa. The first major contribution is the annotation of a dataset (TomatoEbola), which consists of 326 images and 784 annotations collected from three Nigerian farms and is now publicly available. The second key contribution is the proposal of a transfer learning-based approach to evaluate YOLOv8's performance in detecting Tuta absoluta. Experimental results highlight the model’s effectiveness, with a mean average precision of up to 0.737, outperforming other state-of-the-art methods that achieve less than 0.69, demonstrating its capability for real-world deployment. These findings suggest that AI-driven solutions like YOLOv8 could play a pivotal role in reducing agricultural losses and enhancing food security.