Al-Saddik H, Laybros A, Simon J C, Cointault F
Agroecology, Agrosup Dijon, INRA, Univ. Bourgogne Franche-Comté, Dijon, France.
Methods Mol Biol. 2019;1875:213-238. doi: 10.1007/978-1-4939-8837-2_17.
Flavescence Dorée (FD) is a contagious and incurable grapevine disease that can be perceived on leaves. In order to contain its spread, the regulations obligate winegrowers to control each plant and to remove the suspected ones. Nevertheless, this monitoring is performed during the harvest and mobilizes many people during a strategic period for viticulture. To solve this problem, we aim to develop a Multi-Spectral (MS) imaging device ensuring an automated grapevine disease detection solution. If embedded on a UAV, the tool can provide disease outbreaks locations in a geographical information system allowing localized and direct treatment of infected vines. The high-resolution MS camera aims to allow the identification of potential FD occurrence, but the procedure can, more generally, be used to detect any type of foliar diseases on any type of vegetation.Our work consists on defining the spectral bands of the multispectral camera, responsible for identifying the desired symptoms of the disease. In fact, the FD diseased samples were selected after establishing a Polymerase Chain Reaction (PCR) confirmation test and then a feature selection technique was applied to identify the best subset of wavelengths capable of detecting FD samples. An example of a preliminary version of the MS sensor was also presented along with the geometric and radiometric required corrections. An image analysis based on texture and neural networks was also detailed for an enhanced disease classification.
葡萄黄化病(FD)是一种传染性且无法治愈的葡萄藤疾病,可在叶片上察觉。为了控制其传播,相关规定要求葡萄种植者对每株葡萄进行检查,并移除疑似患病植株。然而,这种监测在收获季节进行,且在葡萄种植的关键时期需要动员大量人力。为解决这一问题,我们旨在开发一种多光谱(MS)成像设备,以确保实现葡萄藤疾病的自动检测解决方案。如果将该工具搭载在无人机上,它能够在地理信息系统中提供疾病爆发地点,从而对受感染的葡萄藤进行局部和直接处理。高分辨率多光谱相机旨在实现对潜在葡萄黄化病发病情况的识别,但该方法更普遍地可用于检测任何类型植被上的任何类型叶部病害。我们的工作包括确定多光谱相机的光谱波段,这些波段负责识别疾病的所需症状。事实上,在建立聚合酶链反应(PCR)确认测试后选择了葡萄黄化病患病样本,然后应用特征选择技术来确定能够检测葡萄黄化病样本的最佳波长子集。还展示了多光谱传感器初步版本的示例以及所需的几何和辐射校正。还详细介绍了基于纹理和神经网络的图像分析,以实现更精确的病害分类。