Unverified paper record
Protocol for the Definition of a Multi-Spectral Sensor for Specific Foliar Disease Detection: Case of "Flavescence Dorée".
Methods in molecular biology (Clifton, N.J.) · 1 Jan 2019 · 10.1007/978-1-4939-8837-2_17
Abstract
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.
Plant phenotyping relevance
ブドウ葉の病徴を対象に、マルチスペクトルセンサーの波長選定、補正、画像解析を開発しており、植物病害状態の取得・分類手法が研究の中心である。
abstractwe aim to develop a Multi-Spectral (MS) imaging device ensuring an automated grapevine disease detection solution.
abstractOur work consists on defining the spectral bands of the multispectral camera, responsible for identifying the desired symptoms of the disease.
abstractAn image analysis based on texture and neural networks was also detailed for an enhanced disease classification.
Code and data availability
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