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An integrated smart surveillance system for diseases monitoring in tropical plantation forests

机译：用于热带人工林疾病监测的集成智能监控系统

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The plantation forests are more susceptible to damage by pest and diseases than are natural forest. Monitoring of plantation forest is critical input to the Integrated Pest Management (IPM) process. This study proposes an integrated smart surveillance system for diseases monitoring in tropical plantation forests. The system, called Dismon, is an online and mobile application for monitoring and diseases identification in plantation forests. The application contains three components: digital library, monitoring system and disease identification system. In this work we build the intelligent system for disease monitoring by developing computer vision technology for disease identification through digital image processing. We have examined 1766 leaf samples containing five diseases: leaf spot, leaf blight, leaf curl, phyllode rust and anthracnose leaf spot. In this work, we train Support Vector Machine (SVM) for leaf diseases identification. The experimental results show that the proposed system obtained accuracy of 91% in differentiating healthy leaves and acacia leaf diseases. The ROC curve of acacia leaf identification indicates that the system is reliable to distinguish the leaf diseases. The resulting surveillance system is promising for plantation forests. This application can help surveyors, forest rangers or public users for gathering information, record observation and diseases identification in forest plantation.
机译：与天然林相比，人工林更容易受到病虫害的损害。人工林的监测是病虫害综合治理（IPM）过程的关键输入。这项研究提出了一种用于热带人工林疾病监测的集成智能监控系统。该系统称为Dismon，是一个在线和移动应用程序，用于人工林的监测和病害识别。该应用程序包含三个组件：数字图书馆，监视系统和疾病识别系统。在这项工作中，我们通过开发用于通过数字图像处理进行疾病识别的计算机视觉技术，构建了用于疾病监测的智能系统。我们检查了1766个叶子样品，其中包含五种疾病：叶斑，叶枯病，叶卷曲，叶状锈和炭疽叶斑。在这项工作中，我们训练支持向量机（SVM）进行叶片疾病识别。实验结果表明，所提出的系统在区分健康叶片和相思叶片疾病方面获得了91％的准确率。金合欢叶片识别的ROC曲线表明该系统能够可靠地识别叶片疾病。由此产生的监视系统有望用于人工林。该应用程序可以帮助测量师，护林员或公共用户收集信息，记录观察和森林人工林中的病害识别。

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