Claudia Angélica Rivera-Romero, E. Palacios-Hernandez, O. Vite-Chávez, I. Reyes-Portillo
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引用次数: 0
摘要
大田作物白粉病的预防需要持续监测,因为作为一种真菌病害,白粉病会改变叶片的绿色色素,造成产量损失。因此,需要能确保早期发现病害的解决方案,以实现对病害的主动控制和管理。目前用于识别白粉病的方法是使用 RGB 叶片图像来检测病害程度。在发病初期,症状并不明显,但此时可以在症状出现之前控制病情。本研究建议使用支持向量机,利用 RGB 图像和颜色变换来识别葫芦科植物上的白粉病。首先,我们使用了一个图像数据集,该数据集提供了不同地点和自然光条件下五个生长季节的照片。利用灰度级共现矩阵结果计算出 22 个纹理描述符作为主要特征。建议的损害等级为 "健康叶片"、"处于真菌发芽期的叶片"、"出现初期症状的叶片 "和 "病叶"。实施结果表明,在 L * a * b 色彩空间中的准确率高于使用组合成分时的准确率,准确率为 94%,kappa Cohen 为 0.7638。
Early-Stage Identification of Powdery Mildew Levels for Cucurbit Plants in Open-Field Conditions Based on Texture Descriptors
Constant monitoring is necessary for powdery mildew prevention in field crops because, as a fungal disease, it modifies the green pigments of the leaves and is responsible for production losses. Therefore, there is a need for solutions that assure early disease detection to realize proactive control and management of the disease. The methodology currently used for the identification of powdery mildew disease uses RGB leaf images to detect damage levels. In the early stage of the disease, no symptoms are visible, but this is a point at which the disease can be controlled before the symptoms appear. This study proposes the implementation of a support vector machine to identify powdery mildew on cucurbit plants using RGB images and color transformations. First, we use an image dataset that provides photos covering five growing seasons in different locations and under natural light conditions. Twenty-two texture descriptors using the gray-level co-occurrence matrix result are calculated as the main features. The proposed damage levels are ’healthy leaves’, ’leaves in the fungal germination phase’, ’leaves with first symptoms’, and ’diseased leaves’. The implementation reveals that the accuracy in the L * a * b color space is higher than that when using the combined components, with an accuracy value of 94% and kappa Cohen of 0.7638.