Detection of Gray Mold in Plants Using a Multispectral Imaging System

Abstract

Gray mold disease caused by the fungus Botrytis cinerea damages many crop hosts worldwide and is responsible for heavy economic losses. Early diagnosis and detection of the disease would allow for more effective crop management practices to prevent outbreaks in field or greenhouse settings. Furthermore, having a simple, non-invasive way to quantify the extent of gray mold disease is important for plant pathologists interested in quantifying infection rates. In this paper, we design and build a multispectral imaging system for discriminating between leaf regions, infected with gray mold, and those that remain unharmed on a lettuce ( Lactuca spp.) host. First, we describe a method to select two optimal (high contrast) spectral bands from continuous hyperspectral imagery (450-800 nm). We then built a system based on these two spectral bands, located at 540 and 670 nm. The resultant system uses two cameras, with a narrow band-pass spectral filter mounted on each, to measure the multispectral reflectance of a lettuce leaf. The two resulting images are combined using a normalized difference calculation that produces a single image with high contrast between the leaves’ infected and healthy regions. A classifier was then created based on the thresholding of single pixel values. We demonstrate that this simple classification produces a true positive rate of 95.25% with a false positive rate of 9.316%.

Plant phenotyping relevance

レタス葉の灰色かび病領域を定量するマルチスペクトル画像システムを開発し、分類性能も評価しており、植物病態の表現型取得が中心である。

Code and data availability

The supplied blocks contain no data availability statement, no public repository, no author code/scripts, and no deposited imagery or phenotype datasets. The paper describes a multispectral imaging system and threshold-based classifier for gray mold detection in lettuce, but all hyperspectral/multispectral data, images

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