Generic placeholder image

Current Computer Science

Author(s): Subh Naman, Sanyam Sharma and Ashish Baldi*

DOI: 10.2174/0129503779301717240607070206

DownloadDownload PDF Flyer Cite As
A CNN-based Machine Learning Model for the Identification of Herbal Drugs: A Case Study on Cumin

Article ID: e040724231604 Pages: 11

  • * (Excluding Mailing and Handling)

Explore Articles
About Journal
For Authors
For Editors
For Reviewers

Abstract

Background: This research paper showcases the creation and assessment of a machine learning model utilizing the Efficient Net B4 architecture for the identification of cumin herbs and any potential adulterants. The research presents a comprehensive overview of the model's structure, emphasizing the different layers, their output dimensions, and the number of parameters.

Methods: The trained model consists of a grand total of 17,684,581 parameters, out of which 10,758 have been found eligible for training. The model has been found to exhibit exceptional performance on the training dataset, with an accuracy of 98.73%, a recall score of 0.95, and an F1 score of 0.93. This demonstrates its usefulness in accurately identifying cumin herbs. A confusion matrix has also been developed, which has showcased the model's remarkable proficiency in accurately detecting cumin herbs. Although there have been few occurrences of misclassification, the model has consistently shown exceptional accuracy by accurately identifying the majority of cases in both the "cumin" and "not cumin" categories.

Results: Upon comparing our model's performance to prior research, it has been found notable for its high accuracy and the potential to be applied more broadly in the field of herbal identification. This work offers an innovative way for recognizing cumin plants using machine learning, despite the little research existing in this area. It also establishes a basis for future research on identifying other important herbal items.

Conclusion: In conclusion, the machine learning model based on EfficientNet B4 has been found to exhibit exceptional accuracy and show potential for practical use in identifying cumin herbs. This study can significantly contribute to improving the authentication and quality assurance processes in the herbal product business, paving the way for future advancements in this field.

Keywords: Adulteration, CNN, cumin, herbal authentication, efficientNet B4, machine learning model.

[1]
K.W. Singletary, "Cumin: Potential health benefits", Nutr. Today, vol. 56, no. 3, pp. 144-151, 2021.
[http://dx.doi.org/10.1097/NT.0000000000000479]
[2]
T. Fatima, N.B. Beenish, G. Gani, T. Qadri, and T.A. Bhat, "Antioxidant potential and health benefits of cumin", J Med Plants Stud, vol. 6, pp. 232-236, 2018.
[3]
H.B. Sowbhagya, "Chemistry, technology, and nutraceutical functions of cumin (Cuminum cyminum L): An overview", Crit. Rev. Food Sci. Nutr., vol. 53, no. 1, pp. 1-10, 2013.
[http://dx.doi.org/10.1080/10408398.2010.500223] [PMID: 23035918]
[4]
M. Nadeem, and A. Riaz, "Cumin (Cuminum cyminum) as a potential source of antioxidants", Pak. J. Food Sci., vol. 22, no. 2, pp. 101-107, 2012.
[5]
A.G. Osman, V. Raman, S. Haider, Z. Ali, A.G. Chittiboyina, and I.A. Khan, "Overview of analytical tools for the identification of adulterants in commonly traded herbs and spices", J. AOAC Int., vol. 102, no. 2, pp. 376-385, 2019.
[http://dx.doi.org/10.5740/jaoacint.18-0389] [PMID: 30646970]
[6]
A.B.S. de Lima, A.S. Batista, J.C. de Jesus, and J. de Jesus Silva, "Fast quantitative detection of black pepper and cumin adulterations by near-infrared spectroscopy and multivariate modeling", Food Control, vol. 107, p. 106802, 2020.
[http://dx.doi.org/10.1016/j.foodcont.2019.106802]
[7]
S. Roopashree, J. Anitha, T.R. Mahesh, V. Vinoth Kumar, W. Viriyasitavat, and A. Kaur, "An IoT based authentication system for therapeutic herbs measured by local descriptors using machine learning approach", Measurement, vol. 200, p. 111484, 2022.
[http://dx.doi.org/10.1016/j.measurement.2022.111484]
[8]
S. Sharma, S. Naman, J. Dwivedi, and A. Baldi, "Artificial intelligence-based smart identification system using herbal images: Decision making using various machine learning models", In: Applications of Optimization and Machine Learning in Image Processing and IoT., Chapman and Hall/CRC, pp. 123-155.
[http://dx.doi.org/10.1201/9781003364856]
[9]
A. Muneer, and S.M. Fati, "Efficient and automated herbs classification approach based on shape and texture features using deep learning", IEEE Access, vol. 8, pp. 196747-196764, 2020.
[http://dx.doi.org/10.1109/ACCESS.2020.3034033]
[10]
P.P. Kaur, S. Singh, and M. Pathak, "Review of machine learning herbal plant recognition system", Proceedings of the International Conference on Innovative Computing & Communications (ICICC) 2020, Proceedings of the International Conference on Innovative Computing & Communication (ICICC). April 1, 2020.
[http://dx.doi.org/10.2139/ssrn.3565850]
[11]
S. Naman, S. Sharma, M. Kumar, M. Kumar, and A. Baldi, "Developing a CNN-based machine learning model for cardamom identification: A transfer learning approach", Lat. Am. J. Pharm., vol. 42, no. 6, pp. 565-574, 2023.
[12]
S. Naman, S. Sharma, and A. Baldi, "Machine learning based identification of spices: A case study of Chilli", Lat. Am. J. Pharm., vol. 42, no. 10, pp. 248-261, 2023.
[13]
M.M.H. Talukder, T.A. Ria, M.M. Hasan, M.H. Rahman, and A. Sattar, "A computer vision and deep CNN modeling for spices recognition", 2022 13th International Conference on Computing Communication and Networking Technologies (ICCCNT).
Kharagpur, India, 03-05 October 2022, pp. 1-5. [http://dx.doi.org/10.1109/ICCCNT54827.2022.9984338]
[14]
A. Sundaram, A. Masud, A. AlMarhoon, and B. Sarmah, "Transfer learning approach for classification of widely used spices", YJES, vol. 19, no. 2, pp. 1-21, 2022.
[http://dx.doi.org/10.53370/001c.35690]
[15]
D.C. Khrisne, I.M.A. Suyadnya, J. Manhas, and V. Sharma, "Classification of spices using machine learning techniques", Int J Knowledge Based Com Sys, vol. 10, no. 1, pp. 221-224, 2018.
[http://dx.doi.org/10.1109/ICSGTEIS.2018.8709135]
[16]
D.C. Khrisne, and I.M.A. Suyadnya, "Indonesian herbs and spices recognition using smaller VGGNet-like network", 2018 International Conference on Smart Green Technology in Electrical and Information Systems (ICSGTEIS), Bali, Indonesia, 25-27 October 2018, pp. 221-224, .
[http://dx.doi.org/10.1109/ICSGTEIS.2018.8709135]
[17]
S. Jana, P.S. Nagasai, K.S. Kumar, and V.M. Nageshwar, "Categorization and grading of spices using deep learning", 2022 IEEE International Conference on Data Science and Information System (ICDSIS), Hassan, India, 29-30 July, pp. 1-6, 2022.
[http://dx.doi.org/10.1109/ICDSIS55133.2022.9915893]
[18]
S.M. Hassan, A.K. Maji, M. Jasiński, Z. Leonowicz, and E. Jasińska, "Identification of plant-leaf diseases using CNN and transfer-learning approach", Electronics, vol. 10, no. 12, p. 1388, 2021.
[http://dx.doi.org/10.3390/electronics10121388]
[19]
P. Zhang, L. Yang, and D. Li, "EfficientNet-B4-Ranger: A novel method for greenhouse cucumber disease recognition under natural complex environment", Comput. Electron. Agric., vol. 176, p. 105652, 2020.
[http://dx.doi.org/10.1016/j.compag.2020.105652]
[20]
H. Farman, J. Ahmad, B. Jan, Y. Shahzad, M. Abdullah, and A. Ullah, "Efficientnet-based robust recognition of peach plant diseases in field images", Comput. Mater. Continua, vol. 71, no. 1, pp. 2073-2089, 2022.
[http://dx.doi.org/10.32604/cmc.2022.018961]
[21]
M. Agarwal, S.K. Gupta, and K.K. Biswas, "Development of efficient CNN model for tomato crop disease identification", Sustainable Computing: Informatics and Systems, vol. 28, p. 100407, 2020.
[http://dx.doi.org/10.1016/j.suscom.2020.100407]
[22]
D. Bisen, "Deep convolutional neural network based plant species recognition through features of leaf", Multimedia Tools Appl., vol. 80, no. 4, pp. 6443-6456, 2021.
[http://dx.doi.org/10.1007/s11042-020-10038-w]
[23]
G. Latif, S.E. Abdelhamid, R.E. Mallouhy, J. Alghazo, and Z.A. Kazimi, "Deep learning utilization in agriculture: Detection of rice plant diseases using an improved CNN model", Plants, vol. 11, no. 17, p. 2230, 2022.
[http://dx.doi.org/10.3390/plants11172230] [PMID: 36079612]
[24]
B.V. Gokulnath, "Identifying and classifying plant disease using resilient LF-CNN", Ecol. Inform., vol. 63, p. 101283, 2021.
[http://dx.doi.org/10.1016/j.ecoinf.2021.101283]
[25]
H. Zhu, Q. Liu, Y. Qi, X. Huang, F. Jiang, and S. Zhang, "Plant identification based on very deep convolutional neural networks", Multimedia Tools Appl., vol. 77, no. 22, pp. 29779-29797, 2018.
[http://dx.doi.org/10.1007/s11042-017-5578-9]
[26]
R. Akter, and M.I. Hosen, "CNN-based leaf image classification for Bangladeshi medicinal plant recognition", 2020 Emerging Technology in Computing, Communication and Electronics (ETCCE). Bangladesh 21-22 December, pp. 1-6, 2020.
[http://dx.doi.org/10.1109/ETCCE51779.2020.9350900]
[27]
J. Chen, J. Chen, D. Zhang, Y. Sun, and Y.A. Nanehkaran, "Using deep transfer learning for image-based plant disease identification", Comput. Electron. Agric., vol. 173, p. 105393, 2020.
[http://dx.doi.org/10.1016/j.compag.2020.105393]
[28]
M.G. Chithra, and E.A. Siril, "Morphological variability of aerial vegetative characters among 20 Shatavari (Asparagus racemosus Willd.) collections from Kerala, India", J. Root Crops, vol. 43, no. 2, pp. 21-32, 2017.
[29]
X. Zhu, X. Zhang, Z. Sun, Y. Zheng, S. Su, and F. Chen, "Identification of oil tea (Camellia oleifera C. Abel) cultivars using EfficientNet-B4 CNN model with attention mechanism", Forests, vol. 13, no. 1, p. 1, 2021.
[http://dx.doi.org/10.3390/f13010001]
[30]
J. Cai, R. Pan, J. Lin, J. Liu, L. Zhang, X. Wen, X. Chen, and X. Zhang, "Improved EfficientNet for corn disease identification", Front. Plant Sci., vol. 14, p. 1224385, 2023.
[http://dx.doi.org/10.3389/fpls.2023.1224385] [PMID: 37767299]
[31]
Y. Arun, and G.S. Viknesh, "Leaf classification for plant recognition using EfficientNet architecture", 2022 IEEE Fourth International Conference on Advances in Electronics, Computers and Communications (ICAECC). Bengaluru, India, 10-11 January 2022, , pp. 1-5, .
[http://dx.doi.org/10.1109/ICAECC54045.2022.9716637]
[32]
B.T. Hanh, H. Van Manh, and N.V. Nguyen, "Enhancing the performance of transferred efficientnet models in leaf image-based plant disease classification", J. Plant Dis. Prot., vol. 129, no. 3, pp. 623-634, 2022.
[http://dx.doi.org/10.1007/s41348-022-00601-y]
[33]
J.G. Kotwal, R. Kashyap, and P.M. Shafi, "Artificial driving based EfficientNet for automatic plant leaf disease classification", Multimedia Tools Appl., vol. 83, pp. 38209-38240, 2024.
[http://dx.doi.org/10.1007/s11042-023-16882-w]
[34]
M. Gehlot, and G.C. Gandhi, "“EffiNet-TS”: A deep interpretable architecture using EfficientNet for plant disease detection and visualization", J. Plant Dis. Prot., vol. 130, no. 2, pp. 413-430, 2023.
[http://dx.doi.org/10.1007/s41348-023-00707-x]
[35]
A. Kumar, L. Nelson, and V.S. Venu, "EfficientNet-Bl based maize plant leaf disease classification using deep learning", 2024 2nd International Conference on Intelligent Data Communication Technologies and Internet of Things (IDCIoT) 2024, pp. 1636-1642, .
[http://dx.doi.org/10.1109/IDCIoT59759.2024.10467408]
[36]
T.N. Quoc, and V.T. Hoang, "Medicinal Plant identification in the wild by using CNN", 2020 International Conference on Information and Communication Technology Convergence (ICTC), Jeju, Korea (South), 21-23 October 2020, , pp. 25-29, .
[http://dx.doi.org/10.1109/ICTC49870.2020.9289480]
[37]
M.H. Al Banna, M.A. Haider, M.J. Al Nahian, M.M. Islam, K.A. Taher, and M.S. Kaiser, "Camera model identification using deep CNN and transfer learning approach", 2019 International Conference on Robotics, Electrical and Signal Processing Techniques (ICREST), Dhaka, Bangladesh, pp. 626-630, 2019.
[http://dx.doi.org/10.1109/ICREST.2019.8644194]
[38]
"K.R, H.M, S. Anand, P. Mathikshara, A. Johnson, and M.R, “Attention embedded residual CNN for disease detection in tomato leaves”", Appl. Soft Comput., vol. 86, p. 105933, 2020.
[http://dx.doi.org/10.1016/j.asoc.2019.105933]
[39]
TK. Nayak, and A.CS. Rao, Recent advances in deep learning CNN models for plant disease detection. In: Recent advances in deep learning CNN models for plant disease detection., vol. 2. Springer: Singapore, 2022, pp. 247-263.
[http://dx.doi.org/10.1007/978-981-16-9682-4_14]
[40]
T. Akiyama, Y. Kobayashi, Y. Sasaki, K. Sasaki, T. Kawaguchi, and J. Kishigami, "Mobile leaf identification system using CNN applied to plants in Hokkaido", 2019 IEEE 8th Global Conference on Consumer Electronics (GCCE), Osaka, Japan, 15-18 October 2019,, pp. 324-325, .
[http://dx.doi.org/10.1109/GCCE46687.2019.9015298]
[41]
Y. Zhao, Z. Sun, E. Tian, C. Hu, H. Zong, and F. Yang, "A CNN model for herb identification based on part priority attention mechanism", IEEE International Conference on Systems, Man, and Cybernetics (SMC), Toronto, ON, Canada, 11-14 October 2020, pp. 2565-2571, 2020.
[http://dx.doi.org/10.1109/SMC42975.2020.9283189]
[42]
M. Bhuiyan, M. Abdullahil-Oaphy, R.S. Khanam, and M. Islam, MediNET: A deep learning approach to recognize Bangladeshi ordinary medicinal plants using CNN. In: Soft Computing Techniques and Applications., Springer, 2021, pp. 371-380.
[http://dx.doi.org/10.1007/978-981-15-7394-1_35]
[43]
K. Amulya, D.K. Deepika, and D.P. Kamakshi, "An optimized hyper parameter-based CNN approach for predicting medicinal or non- medicinal leaves", Adv. Eng. Softw., vol. 172, p. 103181, 2022.
[http://dx.doi.org/10.1016/j.advengsoft.2022.103181]
[44]
T. Taori, S. Gupta, S. Bhagat, S. Gajre, and R. Manthalkar, "Cross-task cognitive load classification with identity mapping-based distributed CNN and attention-based rnn using gabor decomposed data images", J. Inst. Electron. Telecommun. Eng., pp. 1-17, 2022.
[http://dx.doi.org/10.1080/03772063.2022.2098191]
[45]
S. Manoharan J, "Flawless detection of herbal plant leaf by machine learning classifier through two stage authentication procedure", J. Artif. Intell. Capsule Netw., vol. 3, no. 2, pp. 125-139, 2021.
[http://dx.doi.org/10.36548/jaicn.2021.2.005]
[46]
M.H. Wathan, and M.H. Wathan, "Shallots classification using CNN", Int. J. Inf. Comput., vol. 3, no. 2, p. 50, 2022.
[http://dx.doi.org/10.35842/ijicom.v3i2.41]
[47]
A. Gokhale, S. Babar, S. Gawade, and S. Jadhav, "Identification of medicinal plant using image processing and machine learning", In: Applied Computer Vision and Image Processing Proceedings of ICCET 2020, vol. 1. Springer, 2020, pp. 272-282.
[http://dx.doi.org/10.1007/978-981-15-4029-5_27]
[48]
A. Kaya, A.S. Keceli, C. Catal, H.Y. Yalic, H. Temucin, and B. Tekinerdogan, "Analysis of transfer learning for deep neural network based plant classification models", Comput. Electron. Agric., vol. 158, pp. 20-29, 2019.
[http://dx.doi.org/10.1016/j.compag.2019.01.041]
[49]
Ü. Atila, M. Uçar, K. Akyol, and E. Uçar, "Plant leaf disease classification using EfficientNet deep learning model", Ecol. Inform., vol. 61, p. 101182, 2021.
[http://dx.doi.org/10.1016/j.ecoinf.2020.101182]
[50]
A.M. Mishra, S. Harnal, K. Mohiuddin, V. Gautam, O.A. Nasr, N. Goyal, M. Alwetaishi, and A. Singh, "A Deep learning-based novel approach for weed growth estimation", Intelligent Automation & Soft Computing, vol. 31, no. 2, pp. 1157-1172, 2022.
[http://dx.doi.org/10.32604/iasc.2022.020174]
[51]
R. Qureshi, M.G. Ragab, S.J. Abdulkader, A. Alqushaib, E.H. Sumiea, and H. Alhussian, "A comprehensive systematic review of YOLO for medical object detection (2018 to 2023)", IEEE Access, vol. 12, pp. 57815-57836, 2023.
[52]
W. Zhang, X. Sun, L. Zhou, X. Xie, W. Zhao, Z. Liang, and P. Zhuang, "Dual-branch collaborative learning network for crop disease identification", Front. Plant Sci., vol. 14, p. 1117478, 2023.
[http://dx.doi.org/10.3389/fpls.2023.1117478] [PMID: 36844059]
[53]
A. Che Soh, U.K. Yusof, N.F.M. Radzi, A.J. Ishak, and M.K. Hassan, "Classification of aromatic herbs using artificial intelligent technique", Pertanika J. Sci. Technol., p. 25, 2017.
[54]
W. Wang, W. Tian, W. Liao, B. Cai, and B. Li, "Identifying chinese herbal medicine by image with three deep CNNs", Proceedings of the 5th International Conference on Control Engineering and Artificial Intelligence New York, NY, USA, January, pp. 1-8, 2021.
[http://dx.doi.org/10.1145/3448218.3448221]
[55]
X. Sun, and H. Qian, "Chinese herbal medicine image recognition and retrieval by convolutional neural network", PLoS One, vol. 11, no. 6, p. e0156327, 2016.
[http://dx.doi.org/10.1371/journal.pone.0156327] [PMID: 27258404]
[56]
W. Chen, J. Tong, R. He, Y. Lin, P. Chen, Z. Chen, and X. Liu, "An easy method for identifying 315 categories of commonly-used Chinese herbal medicines based on automated image recognition using AutoML platforms", Informatics in Medicine Unlocked, vol. 25, p. 100607, 2021.
[http://dx.doi.org/10.1016/j.imu.2021.100607]
[57]
P.K. Mukherjee, Quality control and evaluation of herbal drugs: Evaluating natural products and traditional medicine., Elsevier, 2019.
[58]
G. Bodeker, and C-K. Ong, WHO global atlas of traditional, complementary and alternative medicine., World Health Organization, 2005.
[59]
M. Ekor, "The growing use of herbal medicines: Issues relating to adverse reactions and challenges in monitoring safety", Front. Pharmacol., vol. 4, p. 177, 2014.
[http://dx.doi.org/10.3389/fphar.2013.00177] [PMID: 24454289]
[60]
K. Dubey, and K.P. Dubey, "Biodiversity conservation of medicinal plants", J. Res. Educ. Indian Med., vol. 17, no. 1–2, pp. 1-6, 2011.
[61]
P. Devangan, G. Bajad, S. Loharkar, N. Wadate, S. Gollapalli, T. Dutta, A. Baldi, S. Singh, A. Katyal, R. Chandra, and P.A. Singh, Quality-by-design strategy for developing novel herbal products. In: Introduction to Quality by Design (QbD) From Theory to Practice., Springer, 2024, pp. 263-295.
[http://dx.doi.org/10.1007/978-981-99-8034-5_11]
[62]
P.A. Singh, A. Sood, and A. Baldi, "An agro-ecological zoning model highlighting potential growing areas for medicinal plants in Punjab", IJPER, vol. 55, no. 2s, pp. s492-s500, 2021.
[http://dx.doi.org/10.5530/ijper.55.2s.120]
[63]
P.A. Singh, N. Bajwa, S. Chinnam, A. Chandan, and A. Baldi, "An overview of some important deliberations to promote medicinal plants cultivation", J. Appl. Res. Med. Aromat. Plants, vol. 31, p. 100400, 2022.
[http://dx.doi.org/10.1016/j.jarmap.2022.100400]
[64]
P.A. Singh, A. Sood, and A. Baldi, "Determining constraints in medicinal plants adoption: A model geospatial study in the Indian state of Punjab", J. Appl. Res. Med. Aromat. Plants, vol. 25, p. 100342, 2021.
[http://dx.doi.org/10.1016/j.jarmap.2021.100342]
[65]
A. Baldi, and P.K. Bansal, "Traditional and herbal medicines: Understanding & exploration (part II)", Curr. Tradit. Med., vol. 6, no. 4, pp. 258-259, 2020.
[66]
V. Sharma, and I.N. Sarkar, "Bioinformatics opportunities for identification and study of medicinal plants", Brief. Bioinform., vol. 14, no. 2, pp. 238-250, 2013.
[http://dx.doi.org/10.1093/bib/bbs021] [PMID: 22589384]
[67]
D. Luo, D. Fan, H. Yu, and Z. Li, "A new processing technique for the identification of Chinese herbal medicine", International Conference on Computational and Information Sciences, Shiyang, China 21-23 June, pp. 474-477, 2013.
[http://dx.doi.org/10.1109/ICCIS.2013.131]
[68]
K. Everstine, J. Spink, and S. Kennedy, "Economically motivated adulteration (EMA) of food: Common characteristics of EMA incidents", J. Food Prot., vol. 76, no. 4, pp. 723-735, 2013.
[http://dx.doi.org/10.4315/0362-028X.JFP-12-399] [PMID: 23575142]
[69]
G. Heubl, "New aspects of DNA-based authentication of Chinese medicinal plants by molecular biological techniques", Planta Med., vol. 76, no. 17, pp. 1963-1974, 2010.
[http://dx.doi.org/10.1055/s-0030-1250519] [PMID: 21058240]
[70]
N.K. Korir, J. Han, L. Shangguan, C. Wang, E. Kayesh, Y. Zhang, and J. Fang, "Plant variety and cultivar identification: Advances and prospects", Crit. Rev. Biotechnol., vol. 33, no. 2, pp. 111-125, 2013.
[http://dx.doi.org/10.3109/07388551.2012.675314] [PMID: 22698516]
[71]
P.A. Singh, N. Bajwa, S. Naman, and A. Baldi, "A review on robust computational approaches based identification and authentication of herbal raw drugs", Lett. Drug Des. Discov., vol. 17, no. 9, pp. 1066-1083, 2020.
[http://dx.doi.org/10.2174/1570180817666200304125520]
[72]
Y.B. Zhang, P.P.H. But, Z.T. Wang, and P.C. Shaw, "Current approaches for the authentication of medicinal Dendrobium species and its products", Plant Genet. Resour., vol. 3, no. 2, pp. 144-148, 2005.
[http://dx.doi.org/10.1079/PGR200578]
[73]
N. Sucher, and M. Carles, "Genome-based approaches to the authentication of medicinal plants", Planta Med., vol. 74, no. 6, pp. 603-623, 2008.
[http://dx.doi.org/10.1055/s-2008-1074517] [PMID: 18449847]
[74]
S. Chanda, "Importance of pharmacognostic study of medicinal plants: An overview", J. Pharmacogn. Phytochem., vol. 2, no. 5, pp. 69-73, 2014.