All Issue

2024 Vol.42, Issue 3

Research Article

Classification of Similarly Colored Medicinal Berries using Hyperspectral Images and Machine Learning Models
Min Chae Kim12†
,
Hyo In Yoon2†
,
Hyein Lee2
,
So Jin Park12
,
Jung Seok Yang2
,
Dae-Hyun Jung1
,
Soo Hyun Park2*
1Department of Smart Farm Science, Kyunghee University, Yongin 17104, Korea
2Smart Farm Research Center, Korea Institute of Science and Technology (KIST), Gangneung 25451, Korea
*Corresponding Author
†These authors contributed equally to this work.
30 June 2024. pp. 249-263
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Abstract

As the misuse of medicinal plants increases due to misclassifications brought about by similarities in the external characteristics (color, size, shape) of plants and their fruits, accurate identification techniques must be developed. Spectral information can be used to identify various characteristics of medicinal plants in wavelength ranges that cannot be seen by the naked eye. This study develops a non-destructive identification and classification technology for medicinal plants using hyperspectral imaging combined with machine learning models to eliminate the misidentification of medicinal berries that are very similar in size, shape, and color. Four models were used to classify different plant species: the logistic regression (LR), K-nearest neighbor (KNN), decision tree (DT), and random forest (RF) models. The optimal classification model was selected based on classification performance indicators. The dried fruit of four medicinal plant species were used: Cornus officinalis, Lycium chinense, Lycium barbarum, and Schisandra chinensis. Hyperspectral images of the samples were obtained corresponding to 150 wavelength bands in the 400–1000 nm range. For the training dataset, the average reflectance spectrum per berry was extracted. The accuracy, F1 score, confusion matrix, and receiver operating characteristic (ROC) curve were used to evaluate the performance of each classification model. The LR model performed best, with accuracy of 0.99 and an area under the curve (AUC) value of 1 for all samples. The LR model produces very accurate results, and the classification system based on it is fast and non-destructive. The machine-learning-based hyperspectral imaging classification system can be applied and scaled up to the industrial level, effectively eliminating the misuse of medicinal plants through accurate identification of these plants.

Keywords
classification model
evaluation metrics
logistic regression
medicinal plant
red fruits
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Information
  • Publisher :KOREAN SOCIETY FOR HORTICULTURAL SCIENCE
  • Publisher(Ko) :한국원예학회
  • Journal Title :Horticultural Science and Technology
  • Journal Title(Ko) :원예과학기술지
  • Volume : 42
  • No :3
  • Pages :249-263
  • Received Date : 2023-11-13
  • Revised Date : 2023-12-29
  • Accepted Date : 2024-01-08
  • DOI :https://doi.org/10.7235/HORT.20240022
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