Shehu Harisu Abdullahi, Ackley Aniebietabasi, Mark Marvellous, Eteng Ofem Ebriba, Sharif Md Haidar, Kusetogullari Huseyin
School of Engineering and Computer Science, Victoria University of Wellington, Wellington, New Zealand.
School of Architecture and Design, Victoria University of Wellington, Wellington, New Zealand.
Front Plant Sci. 2025 May 20;16:1524630. doi: 10.3389/fpls.2025.1524630. eCollection 2025.
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 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 different 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.
农业部门面临着来自植物病虫害的持续威胁，番茄潜叶蛾对番茄种植构成严重风险，可导致高达100%的作物损失。及时检测害虫对于有效干预至关重要，但传统方法仍然劳动强度大且效率低下。深度学习的最新进展提供了有前景的解决方案，由于其速度和准确性，YOLOv8成为领先的实时检测模型，在现场部署中优于以前的模型。本研究聚焦于撒哈拉以南非洲地区番茄潜叶蛾引发的番茄叶部病害的早期检测。第一个主要贡献是标注了一个数据集（TomatoEbola），该数据集由从三个不同农场收集的326张图像和784个标注组成，现已公开可用。第二个关键贡献是提出了一种基于迁移学习的方法来评估YOLOv8在检测番茄潜叶蛾方面的性能。实验结果突出了该模型的有效性，平均精度高达0.737，优于其他最先进方法（其精度低于0.69），证明了其在实际应用中的能力。这些发现表明，像YOLOv8这样的人工智能驱动的解决方案在减少农业损失和加强粮食安全方面可以发挥关键作用。
