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http://dx.doi.org/10.11626/KJEB.2021.39.4.581

Classification of Convolvulaceae plants using Vis-NIR spectroscopy and machine learning  

Yong-Ho Lee (Plant Life & Environmental Science, Hankyong National University)
Soo-In Sohn (Biosafety Division, National Institute of Agricultural Science, RDA)
Sun-Hee Hong (Plant Life & Environmental Science, Hankyong National University)
Chang-Seok Kim (Highland Agriculture Research Institute, National Institute of Crop Science, RDA)
Chae-Sun Na (Wild Plant seeds Research Division, Baekdudaegan National Arboretum)
In-Soon Kim (Institute for Future Environmental Ecology Co., Ltd)
Min-Sang Jang (Institute for Future Environmental Ecology Co., Ltd)
Young-Ju Oh (Institute for Future Environmental Ecology Co., Ltd)
Publication Information
Korean Journal of Environmental Biology / v.39, no.4, 2021 , pp. 581-589 More about this Journal
Abstract
Using visible-near infrared(Vis-NIR) spectra combined with machine learning methods, the feasibility of quick and non-destructive classification of Convolvulaceae species was studied. The main aim of this study is to classify six Convolvulaceae species in the field in different geographical regions of South Korea using a handheld spectrometer. Spectra were taken at 1.5 nm intervals from the adaxial side of the leaves in the Vis-NIR spectral region between 400 and 1,075 nm. The obtained spectra were preprocessed with three different preprocessing methods to find the best preprocessing approach with the highest classification accuracy. Preprocessed spectra of the six Convolvulaceae sp. were provided as input for the machine learning analysis. After cross-validation, the classification accuracy of various combinations of preprocessing and modeling ranged between 43.4% and 98.6%. The combination of Savitzky-Golay and Support vector machine methods showed the highest classification accuracy of 98.6% for the discrimination of Convolvulaceae sp. The growth stage of the plants, different measuring locations, and the scanning position of leaves on the plant were some of the crucial factors that affected the outcomes in this investigation. We conclude that Vis-NIR spectroscopy, coupled with suitable preprocessing and machine learning approaches, can be used in the field to effectively discriminate Convolvulaceae sp. for effective weed monitoring and management.
Keywords
Convolvulaceae; Vis-NIR spectroscopy; machine learning; species discrimination;
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