Quantification of gray mold infection in lettuce using a bispectral imaging system under laboratory conditions.

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 measuring infection rates. In this paper, we design and build a bispectral 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 explain the process of building 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 bispectral 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% in laboratory conditions.

Plant phenotyping relevance

レタス葉の灰色かび病の感染領域を定量する二波長画像システムを設計・構築し、分類性能も検証しており、植物病害状態の表現型取得が中心である。

Code and data availability

The article describes a bispectral imaging system and hyperspectral analysis for quantifying gray mold in lettuce, but contains no data availability statement, no public repository deposit, no author code URL, and no supplement with datasets, images, or analysis code. Only license/ORCID URLs appear, none of which are a

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