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

• Published in: Plant direct Vol. 5; no. 3; pp. e00317 - n/a
• Main Authors: Scarboro, Clifton G., Ruzsa, Stephanie M., Doherty, Colleen J., Kudenov, Michael W.
• Format: Journal Article
• Language: English
• Published: England John Wiley & Sons, Inc 01-03-2021; John Wiley and Sons Inc; Wiley
• Subjects: Accuracy; Cameras; Disease; disease detection; Fungicides; gray mold; Grey mold; hyperspectral imaging; Infections; Leaves; Lettuce; Mold; multispectral imaging; Original Research; Pathogens; plant pathology; Principal components analysis; reflectance indexing; multispectral imaging; disease detection; gray mold; reflectance indexing; hyperspectral imaging; plant pathology
• ISSN: 2475-4455; 2475-4455
• DOI: 10.1002/pld3.317

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.