Development of an Automated Low-Cost Multispectral Imaging System to Quantify Canopy Size and Pigmentation

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Publicado en:Sensors vol. 24, no. 17 (2024), p. 5515
Autor principal: Wacker, Kahlin
Otros Autores: Kim, Changhyeon, van Iersel, Marc W, Sidore, Benjamin, Pham, Tony, Haidekker, Mark, Seymour, Lynne, Ferrarezi, Rhuanito Soranz
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MDPI AG
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100 1 |a Wacker, Kahlin  |u Department of Horticulture, University of Georgia, Athens, GA 30602, USA; <email>kahlin.wacker@uga.edu</email> (K.W.); <email>mvanier@uga.edu</email> (M.W.v.I.); <email>benjaminsidore@gmail.com</email> (B.S.) 
245 1 |a Development of an Automated Low-Cost Multispectral Imaging System to Quantify Canopy Size and Pigmentation 
260 |b MDPI AG  |c 2024 
513 |a Journal Article 
520 3 |a Canopy imaging offers a non-destructive, efficient way to objectively measure canopy size, detect stress symptoms, and assess pigment concentrations. While it is faster and easier than traditional destructive methods, manual image analysis, including segmentation and evaluation, can be time-consuming. To make imaging more widely accessible, it’s essential to reduce the cost of imaging systems and automate the analysis process. We developed a low-cost imaging system with automated analysis using an embedded microcomputer equipped with a monochrome camera and a filter for a total hardware cost of ~USD 500. Our imaging system takes images under blue, green, red, and infrared light, as well as chlorophyll fluorescence. The system uses a Python-based program to collect and analyze images automatically. The multi-spectral imaging system separates plants from the background using a chlorophyll fluorescence image, which is also used to quantify canopy size. The system then generates normalized difference vegetation index (NDVI, “greenness”) images and histograms, providing quantitative, spatially resolved information. We verified that these indices correlate with leaf chlorophyll content and can easily add other indices by installing light sources with the desired spectrums. The low cost of the system can make this imaging technology widely available. 
651 4 |a United States--US 
653 |a Physiology 
653 |a Machine learning 
653 |a Software 
653 |a Algorithms 
653 |a Automation 
653 |a Plant growth 
653 |a Light 
653 |a Chlorophyll 
653 |a Compatible hardware 
653 |a Sensors 
700 1 |a Kim, Changhyeon  |u Department of Plant Science and Landscape Architecture, University of Connecticut, Storrs, CT 06269, USA; <email>changhyeon.kim@uconn.edu</email> 
700 1 |a van Iersel, Marc W  |u Department of Horticulture, University of Georgia, Athens, GA 30602, USA; <email>kahlin.wacker@uga.edu</email> (K.W.); <email>mvanier@uga.edu</email> (M.W.v.I.); <email>benjaminsidore@gmail.com</email> (B.S.) 
700 1 |a Sidore, Benjamin  |u Department of Horticulture, University of Georgia, Athens, GA 30602, USA; <email>kahlin.wacker@uga.edu</email> (K.W.); <email>mvanier@uga.edu</email> (M.W.v.I.); <email>benjaminsidore@gmail.com</email> (B.S.) 
700 1 |a Pham, Tony  |u College of Engineering, University of Georgia, Athens, GA 30602, USA; <email>tmp52468@uga.edu</email> (T.P.); <email>mhaidekk@uga.edu</email> (M.H.) 
700 1 |a Haidekker, Mark  |u College of Engineering, University of Georgia, Athens, GA 30602, USA; <email>tmp52468@uga.edu</email> (T.P.); <email>mhaidekk@uga.edu</email> (M.H.) 
700 1 |a Seymour, Lynne  |u Department of Statistics, University of Georgia, Athens, GA 30602, USA; <email>seymour@uga.edu</email> 
700 1 |a Ferrarezi, Rhuanito Soranz  |u Department of Horticulture, University of Georgia, Athens, GA 30602, USA; <email>kahlin.wacker@uga.edu</email> (K.W.); <email>mvanier@uga.edu</email> (M.W.v.I.); <email>benjaminsidore@gmail.com</email> (B.S.) 
773 0 |t Sensors  |g vol. 24, no. 17 (2024), p. 5515 
786 0 |d ProQuest  |t Health & Medical Collection 
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