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
PDF XML Citation
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
References
1

Adikari KE, Shrestha S, Ratnayake DT, Budhathoki A, Mohanasundaram S, Dailey MN (2021) Evaluation of artificial intelligence models for flood and drought forecasting in arid and tropical regions. Environ Model Softw 144:105136. doi:10.1016/j.envsoft.2021.105136

10.1016/j.envsoft.2021.105136
2

Agilandeeswari L, Prabukumar M, Radhesyam V, Phaneendra KLB, Farhan A (2022) Crop classification for agricultural applications in hyperspectral remote sensing images. Appl Sci 12:1670. doi:10.3390/app12031670

10.3390/app12031670
3

Amirruddin AD, Muharam FM, Ismail MH, Ismail MF, Tan NP, Karam DS (2020) Hyperspectral remote sensing for assessment of chlorophyll sufficiency levels in mature oil palm (Elaeis guineensis) based on frond numbers: Analysis of decision tree and random forest. Comput Electron Agric 169:105221. doi:10.1016/j.compag.2020.105221

10.1016/j.compag.2020.105221
4

Ariana DP, Lu R (2010) Evaluation of internal defect and surface color of whole pickles using hyperspectral imaging. J Food Eng 96:583-590. doi:10.1016/j.jfoodeng.2009.09.005

10.1016/j.jfoodeng.2009.09.005
5

Baek Y, Sul S, Cho YY (2023) Estimation of Days after Transplanting using an Artificial Intelligence CNN (Convolutional Neural Network) Model in a Closed-type Plant Factory. Hortic Sci Technol 41:81-90. doi:10.7235/HORT.20230008

10.7235/HORT.20230008
6

Bansal M, Goyal A, Choudhary A (2022) A comparative analysis of K-nearest neighbor, genetic, support vector machine, decision tree, and long short term memory algorithms in machine learning. Decis Anal J 3:100071. doi:10.1016/j.dajour.2022.100071

10.1016/j.dajour.2022.100071
7

Boateng EY, Otoo J, Abaye DA (2020) Basic tenets of classification algorithms K-nearest-neighbor, support vector machine, random forest and neural network: a review. Journal of Data Analysis and Information Processing 8:341-357. doi:10.4236/jdaip.2020.84020

10.4236/jdaip.2020.84020
8

Bochkovskiy A, Wang CY, Liao HYM (2020) Yolov4: Optimal speed and accuracy of object detection. arXiv preprint arXiv:2004.10934.

10.48550/arXiv.2004.10934
9

Chen LC, Wang BR, Chen IC, Shao CH (2010) Use of Chinese herbal medicine among menopausal women in Taiwan. Int J Gynaecol Obstet 109:63-66. doi:10.1016/j.ijgo.2009.10.014

10.1016/j.ijgo.2009.10.01419969298
10

Chien KJ, Horng CT, Huang YS, Hsieh YH, Wang CJ, Yang JS, Lu CC, Chen F (2018) Effects of Lycium barbarum (goji berry) on dry eye disease in rats. Mol Med Rep 17:809-818. doi:10.3892/mmr.2017.7947

10.3892/mmr.2017.794729115477PMC5780158
11

Chlebda DK, Rogulska A, Lojewski T (2017) Assessment of hyperspectral imaging system for colour measurement. Spectrochim Acta A Mol Biomol Spectrosc 185:55-62. doi:10.1016/j.saa.2017.05.037

10.1016/j.saa.2017.05.03728538191
12

Choi JH, Park SH, Jung DH, Park YJ, Yang JS, Park JE, Lee H, Kim SM (2022) Hyperspectral imaging-based multiple predicting models for functional component contents in Brassica juncea. Agriculture 12:1515. doi:10.3390/agriculture12101515

10.3390/agriculture12101515
13

Donno D, Beccaro GL, Mellan MG, Cerutti AK, Bounous G (2015) Goji berry fruit (Lycium spp.): Antioxidant compound fingerprint and bioactivity evaluation. J Func Foods 18:1070-1085. doi:10.1016/j.jff.2014.05.020

10.1016/j.jff.2014.05.020
14

Fan S, Li J, Zhang X, Xu D, Liu X, Dias ACP, Zhang X, Chen C (2022) A study on the identification, quantification, and biological activity of compounds from Cornus officinalis before and after in vitro gastrointestinal digestion and simulated colonic fermentation. J Func Foods 98:105272. doi:10.1016/j.jff.2022.105272

10.1016/j.jff.2022.105272
15

Faris H, Habib M, Faris M, Alomari M, Alomari A (2020) Medical speciality classification system based on binary particle swarms and ensemble of one vs. rest support vector machines. J Biomed Inform 109:103525. doi:10.1016/j.jbi.2020.103525

10.1016/j.jbi.2020.10352532781030
16

Feng L, Zhu S, Zhou L, Zhao Y, Bao Y, Zhang C, He Y (2019) Detection of subtle bruises on winter jujube using hyperspectral imaging with pixel-wise deep learning method. IEEE Access 7:64494-64505. doi:10.1109/ACCESS.2019.2917267

10.1109/ACCESS.2019.2917267
17

Fu L, Okamoto H, Kataoka T, Shibata Y (2011) Color based classification for berries of Japanese Blue Honeysuckle. Int J Food Eng 7. doi:10.2202/1556-3758.2408

10.2202/1556-3758.2408
18

Gao X, Liu, Y, An, Ni, J (2021) Active components and pharmacological effects of Cornus officinalis: Literature review. Front Pharmacol 12:633447. doi:10.3389/fphar.2021.633447

10.3389/fphar.2021.63344733912050PMC8072387
19

Gautam RK, Nadda S (2022) Hyperspectral Image Prediction Using Logistic Regression Model. In Proceedings of Emerging Trends and Technologies on Intelligent Systems: ETTIS 2022. Singapore: Springer Nature Singapore, pp 283-293. doi:10.1007/978-981-19-4182-5_22

10.1007/978-981-19-4182-5_22
20

Golpour P, Ghayour-Mobarhan M, Saki A, Esmaily H, Taghipour A, Tajfard M, Ghazizadeh H, Moohebati M, Ferns GA (2020) Comparison of support vector machine, naïve Bayes and logistic regression for assessing the necessity for coronary angiography. Int J Environ Res Public Health 17:6449. doi:10.3390/ijerph17186449

10.3390/ijerph1718644932899733PMC7558963
21

Guzmán CE, Guzman-Moreno CG, Assad-Morell JL, Carrizales-Sepulveda EF (2021) Flecainide toxicity associated with the use of goji berries: A case report. Eur Heart J Case Rep 5:ytab204. doi:10.1093/ehjcr/ytab204

10.1093/ehjcr/ytab20434084998PMC8167332
22

Ham J, Chen Y, Crawford MM, Ghosh J (2005) Investigation of the random forest framework for classification of hyperspectral data. IEEE Trans Geosci Remote Sens 43:492-501. doi:10.1109/TGRS.2004.842481

10.1109/TGRS.2004.842481
23

Han S, Williamson BD, Fong Y (2021) Improving random forest predictions in small datasets from two-phase sampling designs. BMC Medical Inform Decis Mak 21:1-9. doi:10.1186/s12911-021-01688-3

10.1186/s12911-021-01688-334809631PMC8607560
24

Handelman GS, Kok HK, Chandra, RV, Razavi AH, Huang S, Brooks M, Lee MJ, Asadi H (2019) Peering into the black box of artificial intelligence: evaluation metrics of machine learning methods. Am J Roentgenol 212:38-43. doi:10.2214/AJR.18.20224

10.2214/AJR.18.2022430332290
25

Hashim H, Abd Lati Z, Adnan NA (2019) Urban vegetation classification with NDVI threshold value method with very high resolution (VHR) Pleiades imagery. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences 42:237-240. doi:10.5194/isprs-archives-XLII-4-W16-237-2019

10.5194/isprs-archives-XLII-4-W16-237-2019
26

Heydarian M, Doyle TE, Samavi R (2022) MLCM: Multi-label confusion matrix. IEEE Access 10:19083-19095. doi:10.1109/ACCESS.2022.3151048

10.1109/ACCESS.2022.3151048
27

Karimpour-Reihan S, Firuzei E, Khosravi M, Abbaszade M (2018) Coagulation disorder following red clover (Trifolium Pratense) misuse: A case report. Adv J Emerg Med 2:e20. doi:10.22114/ajem.v0i0.30

10.22114/ajem.v0i0.30
28

Karki S, Basak JK, Paudel B, Deb NC, Kim NE, Kook J, Kang MY, Kim HT (2024) Classification of strawberry ripeness stages using machine learning algorithms and colour spaces. Hortic Environ Biotechnol 65:337-354. doi:10.1007/s13580-023-00559-2

10.1007/s13580-023-00559-2
29

Keivani M, Mazloum J, Sedaghatfar E, Tavakoli MB (2020) Automated Analysis of Leaf Shape, Texture, and Color Features for Plant Classification. Trait Du Signal 37:17-28. doi:10.18280/ts.370103

10.18280/ts.370103
30

Kishore M, Kulkarni SB (2016) Approaches and challenges in classification for hyperspectral data: a review. In 2016 International Conference on Electrical, Electronics, and Optimization Techniques (ICEEOT). IEEE pp 3418-3421. doi:10.1109/ICEEOT.2016.7755339

10.1109/ICEEOT.2016.7755339
31

Kortesoja M, Karhu E, Olafsdottir ES, Freysdottir J, Hanski L (2019) Impact of dibenzocyclooctadiene lignans from Schisandra chinensis on the redox status and activation of human innate immune system cells. Free Radic Biol Med 131:309-317. doi:10.1016/j.freeradbiomed.2018.12.019

10.1016/j.freeradbiomed.2018.12.01930578916
32

Li FS, Weng JK (2017) Demystifying traditional herbal medicine with modern approach. Nat Plants 3:1-7. doi:10.1038/nplants.2017.109

10.1038/nplants.2017.10928758992
33

Li J, Bioucas-Dias JM, Plaza A (2009) Semi-supervised hyperspectral image segmentation. In 2009 First Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing. IEEE pp 1-4. doi:10.1109/WHISPERS.2010.5594877

10.1109/WHISPERS.2010.5594877
34

Li M, Xu H, Deng Y (2019) Evidential decision tree based on belief entropy. Entropy 21:897. doi:10.3390/e21090897

10.3390/e21090897PMC7515420
35

Li Z, He X, Liu F, Wang J, Feng J (2018) A review of polysaccharides from Schisandra chinensis and Schisandra sphenanthera: Properties, functions and applications. Carbohydr Polym 184:178-190. doi:10.1016/j.carbpol.2017.12.058

10.1016/j.carbpol.2017.12.05829352909
36

Liu KH, Yang MH, Huang ST, Lin, C (2022) Plant species classification based on hyperspectral imaging via a lightweight convolutional neural network model. Front Plant Sci 13:855660. doi:10.3389/fpls.2022.855660

10.3389/fpls.2022.85566035498669PMC9044035
37

Llorent-Martínez EJ, Fernandez-de Cordova ML, Ortega-Barrales P, Ruiz-Medina A (2013) Characterization and comparison of the chemical composition of exotic superfoods. Microchem J 110:444-451. doi:10.1016/j.microc.2013.05.016

10.1016/j.microc.2013.05.016
38

Makantasis K, Doulamis AD, Doulamis ND, Nikitakis A (2018) Tensor-based classification models for hyperspectral data analysis. IEEE Trans Geosci Remote Sens 56:6884-6898. doi:10.1109/TGRS.2018.2845450

10.1109/TGRS.2018.2845450
39

Mocan A, Zengin G, Uysal A, Gunes E, Mollica A, Degirmenci NS, Alpsoy L, Aktumsek A (2016) Biological and chemical insights of Morina persica L.: A source of bioactive compounds with multifunctional properties. J Funct Foods 25:94-109. doi:10.1016/j.jff.2016.05.010

10.1016/j.jff.2016.05.010
40

Qian Y, Ye M, Zhou, J (2012) Hyperspectral image classification based on structured sparse logistic regression and three-dimensional wavelet texture features. IEEE Trans Geosci Remote Sens 51:2276-2291. doi:10.1109/TGRS.2012.2209657

10.1109/TGRS.2012.2209657
41

Rajaguru H, S R SC (2019) Analysis of decision tree and k-nearest neighbor algorithm in the classification of breast cancer. Asian Pac J Cancer Prev 20:3777. doi:10.31557/APJCP.2019.20.12.3777

10.31557/APJCP.2019.20.12.377731870121PMC7173366
42

Rajani S, Veena MN (2022) Ayurvedic Plants Identification based on Machine Learning and Deep Learning Technologies. In 2022 Fourth International Conference on Emerging Research in Electronics, Computer Science and Technology (ICERECT). IEEE pp 1-6

43

Rajeshwari T, Vardhini PH, Reddy KMK, Priya KK, Sreeja K (2021) Smart Agriculture Implementation using IoT and Leaf Disease Detection using Logistic Regression. In 2021 4th International Conference on Recent Developments in Control, Automation & Power Engineering (RDCAPE). IEEE pp 619-623. doi:10.1109/RDCAPE52977.2021.9633608

10.1109/RDCAPE52977.2021.9633608
44

Robles-Velasco A, Cortes P, Munuzuri, J, Onieva L (2020) Prediction of pipe failures in water supply networks using logistic regression and support vector classification. Reliab Eng Syst Saf 196:106754. doi:10.1016/j.ress.2019.106754

10.1016/j.ress.2019.106754
45

Ruett M, Junker-Frohn LV, Siegmann B, Ellenberger J, Jaenicke H, Whitney C, Luedeling E, Tiede-Arlt P, Rascher U (2022) Hyperspectral imaging for high-throughput vitality monitoring in ornamental plant production. Sci Hortic 291:110546. doi:10.1016/j.scienta.2021.110546

10.1016/j.scienta.2021.110546
46

Salman T, Ghubaish A, Unal D, Jain R (2020) Safety score as an evaluation metric for machine learning models of security applications. IEEE Netw Lett 2:207-211. doi:10.1109/LNET.2020.3016583

10.1109/LNET.2020.3016583
47

Salve P, Yannawar P, Sardesai M (2022) Multimodal plant recognition through hybrid feature fusion technique using imaging and non-imaging hyper-spectral data. J King Saud Univ Comput Inf Sci 34:1361-1369. doi:10.1016/j.jksuci.2018.09.018

10.1016/j.jksuci.2018.09.018
48

Sathiyamoorthi V, Harshavardhanan P, Azath H, Senbagavalli M, Viswa Bharathy AM, Chokkalingam BS (2022) An effective model for predicting agricultural crop yield on remote sensing hyper-spectral images using adaptive logistic regression classifier. Concurrency Computat Pract Exper 34:e7242. doi:10.1002/cpe.7242

10.1002/cpe.7242
49

Shabani S, Samadianfard S, Sattari MT, Mosavi A, Shamshirband S, Kmet T, Várkonyi-Koczy AR (2020) Modeling pan evaporation using Gaussian process regression K-nearest neighbors random forest and support vector machines; comparative analysis. Atmosphere 11:66. doi:10.3390/atmos11010066

10.3390/atmos11010066
50

Shah K, Patel H, Sanghvi D, Shah M (2020) A comparative analysis of logistic regression, random forest and KNN models for the text classification. Augment Hum Res 5:1-16. doi:10.1007/s41133-020-00032-0

10.1007/s41133-020-00032-0
51

Sheykhmousa M, Mahdianpari M, Ghanbari H, Mohammadimanesh F, Ghamisi P, Homayouni S (2020) Support vector machine versus random forest for remote sensing image classification: A meta-analysis and systematic review. IEEE J Sel Top Appl Earth Obs Remot Sens 13:6308-6325. doi:10.1109/JSTARS.2020.3026724

10.1109/JSTARS.2020.3026724
52

Skenderidis P, Leontopoulos S, Lampakis D (2022) Goji berry: Health promoting properties. Nutraceuticals 2:32-48. doi:10.3390/nutraceuticals2010003

10.3390/nutraceuticals2010003
53

Speiser JL, Miller ME, Tooze J, Ip E (2019) A comparison of random forest variable selection methods for classification prediction modeling. Expert Syst Appl 134:93-101. doi:10.1016/j.eswa.2019.05.028

10.1016/j.eswa.2019.05.02832968335PMC7508310
54

Teixeira F, Silva AM, Delerue-Matos C, Rodrigues F (2023) Lycium barbarum Berries (Solanaceae) as Source of Bioactive Compounds for Healthy Purposes: A Review. Int J Mol Scie 24:4777. doi:10.3390/ijms24054777

10.3390/ijms2405477736902206PMC10003350
55

Theissler A, Thomas M, Burch M, Gerschner F (2022) ConfusionVis: Comparative evaluation and selection of multi-class classifiers based on confusion matrices. Knowl Based Syst 247:108651. doi:10.1016/j.knosys.2022.108651

10.1016/j.knosys.2022.108651
56

Wang AX, Chukova SS, Nguyen BP (2023) Ensemble k-nearest neighbors based on centroid displacement. Inf Sci 629:313-323. doi:10.1016/j.ins.2023.02.004

10.1016/j.ins.2023.02.004
57

Wang D, Dowell FE, Lacey RE (1999) Single Wheat Kernel Color Classification by Using Near‐Infrared Reflectance Spectra. Cereal Chem 76:30-33. doi:10.1094/CCHEM.1999.76.1.30

10.1094/CCHEM.1999.76.1.30
58

Wong TT, Yeh PY (2019) Reliable accuracy estimates from k-fold cross validation. IEEE Trans Knowl Data Eng 32:1586-1594. doi:10.1109/TKDE.2019.2912815

10.1109/TKDE.2019.2912815
59

Ye W, Yan T, Zhang C, Duan L, Chen W, Song H, Zhang Y, Xu W, Gao P (2022) Detection of Pesticide Residue Level in Grape Using Hyperspectral Imaging with Machine Learning. Foods 11:1609. doi:10.3390/foods11111609

10.3390/foods1111160935681359PMC9180647
60

Zeng L, Liu L, Chen D, Lu H, Xue Y, Bi H, Yang W (2023) The innovative model based on artificial intelligence algorithms to predict recurrence risk of patients with postoperative breast cancer. Front Oncol 13:1117420. doi:10.3389/fonc.2023.1117420

10.3389/fonc.2023.111742036959794PMC10029918
61

Zhou H, Zhang J, Zhou Y, Guo X, Ma Y (2021) A feature selection algorithm of decision tree based on feature weight. Expert Syst Appl 164:113842. doi:10.1016/j.eswa.2020.113842

10.1016/j.eswa.2020.113842
62

Zou X, Hu Y, Tian Z, Shen K (2019) Logistic regression model optimization and case analysis. In 2019 IEEE 7th international conference on computer science and network technology (ICCSNT). IEEE pp 135-139. doi:10.1109/ICCSNT47585.2019.8962457

10.1109/ICCSNT47585.2019.8962457PMC6483450
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 : 2024-08-01
  • DOI :https://doi.org/10.7235/HORT.20240022
Journal Informaiton Horticultural Science and Technology Horticultural Science and Technology
Online Submission
  • scopus
  • NRF
  • KOFST
  • crossref crossmark
  • crossref cited-by
  • crosscheck
  • orcid
  • open access
  • ccl
Journal Informaiton Journal Informaiton - close