改进的胶囊-YOLO模型可精准识别番茄病害，助力早期防治。
Development of an Improved Capsule-Yolo Network for Automatic Tomato Plant Disease Early Detection and Diagnosis
- 融合胶囊网络与YOLO框架，增强重叠叶片分割能力。
- 病害识别准确率99.31%，各项指标优于现有方法2.9%以上。
- 配套简易界面，适合农户上传图片自助诊断用药。
尼日利亚拥有肥沃土壤，支持大规模番茄种植，但病原体导致的疾病严重威胁番茄健康，常引发减产甚至物种灭绝，加剧粮食不安全。这些病害通常在叶片和果实上呈现明显视觉特征，易于通过图像识别。本研究提出一种改进的胶囊-YOLO网络架构，基于YOLO框架实现复杂背景下重叠与遮挡番茄叶片的自动分割。该模型在病害症状识别中表现优异：准确率99.31%、召回率98.78%、精确率99.09%，F1分数达98.93%，较现有先进方法分别提升2.91%、1.84%、5.64%和4.12%。同时开发了用户友好的交互界面，支持农户上传受病植株图像，实现早期病害检测，并提供诊断与治疗建议。该系统有望显著提升农业产量，强化粮食安全。
原文摘要 · Abstract (English)
Like many countries, Nigeria is naturally endowed with fertile agricultural soil that supports large-scale tomato production. However, the prevalence of disease causing pathogens poses a significant threat to tomato health, often leading to reduced yields and, in severe cases, the extinction of certain species. These diseases jeopardise both the quality and quantity of tomato harvests, contributing to food insecurity. Fortunately, tomato diseases can often be visually identified through distinct forms, appearances, or textures, typically first visible on leaves and fruits. This study presents an enhanced Capsule-YOLO network architecture designed to automatically segment overlapping and occluded tomato leaf images from complex backgrounds using the YOLO framework. It identifies disease symptoms with impressive performance metrics: 99.31% accuracy, 98.78% recall, and 99.09% precision, and a 98.93% F1-score representing improvements of 2.91%, 1.84%, 5.64%, and 4.12% over existing state-of-the-art methods. Additionally, a user-friendly interface was developed to allow farmers and users to upload images of affected tomato plants and detect early disease symptoms. The system also provides recommendations for appropriate diagnosis and treatment. The effectiveness of this approach promises significant benefits for the agricultural sector by enhancing crop yields and strengthening food security.
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