MikrobatX: The Use of SIFT Feature Extraction in a Deep Learning Approach for Identification and Classification of Microscopic Fragments of Medicinal Leaves

Alam Rahmatulloh, Hendy Suhendy, Ricky Indra Gunawan


Due to a lack of references that contain standard references, it is still difficult to evaluate the accuracy of the raw material for the medicinal plant Simplicia powder based on microscopic testing in the pharmaceutical industry. Furthermore, it takes much time to manually match the findings of microscopic tests with standard reference materials. For these reasons, artificial intelligence must be used so that researchers can rapidly and reliably forecast the kinds of medicinal plants based on microscopic fragments. Deep learning performance in computer vision has demonstrated encouraging outcomes in recent years. Convolutional neural networks (CNN) enhanced by SIFT feature extraction, dubbed "MikrobatX," are used in the proposed work to identify and classify microscopic fragment images of the medicinal plant Simplicia. This technique plays a key role in the microscopic identification and classification of medicinal plant simplicia. Using microscopic photographs of the leaves of medicinal plants, MikrobatX was able to extract essential Simplicia characteristics. Our proposed model may produce the greatest accuracy value of 89.42% for microscopic medicinal leaf Simplicia image problems, according to experimental results utilizing the Mikrobat dataset. Due to the lack of comparable research using the Microbat dataset, these findings cannot be compared to earlier investigations.


Artificial intelligence; feature extraction; medicinal plant simplicia; microscopic fragments

Full Text:



Z. Salim and E. Munadi, Info Komoditi Tanaman Obat. Jakarta: Badan Pengkajian dan Pengembangan Perdagangan Kementerian Perdagangan Republik Indonesia, 2017.

D. Harefa, “Pemanfaatan Hasil Tanaman Sebagai Tanaman Obat Keluarga (TOGA),” Madani : Indonesian Journal Of Civil Society, vol. 2, no. 2, pp. 28–36, 2020.

I. Mentari, Wirnawati, and M. Putri, . “Karakterisasi Simplisia dan Ekstrak Daun Bandotan (Ageratum conyzoides L) Sebagai Kandidat Obat Karies Gigi,” Jurnal Ilmiah Ibnu Sina, vol. 5, no. 1, pp. 1–9, 2020.

Fitriyanti, S. Qalbiah, and PI. Sayakti, “Identifikasi Kulit Batang Kalangkala (Litsea Angulata Bi) Secara Makroskopik, Mikroskopik, dan Skrining Fitokimia,” Parapemikir : Jurnal Ilmiah Farmasi, vol. 9, no. 2, pp. 1–9, 2020.

R. Sari and M. Laoli, “Karakterisasi Simplisia Dan Skrining Fitokimia Serta Analisis Secara KLT(Kromatografi Lapis Tipis) Daun dan Kulit Buah Jeruk Lemon (Citrus limon (L.) Burm.f.),” Jurnal Ilmiah Farmasi Imelda, vol. 2, no. 2, pp. 59–68, 2019.

Y. Utami, A. Umar, R. Syahruni, and I. Kadullah, “Standardisasi Simplisia dan Ekstrak Etanol Daun Leilem (Clerodendrum minahassae Teisjm. & Binn.),” Journal of Pharmaceutical and Medicinal Sciences, vol. 2, no. 1, pp. 32–39, 2017.

L. Fikayuniar, J. Ratnawati, and Y. Yun, “Uji Pendahuluan dan Karakterisasi Simplisia Herba Picisan (Drymoglossum piloselloides (L.) presl),” Uji Pendahuluan dan Karakterisasi Simplisia Herba Picisan, vol. 1, no. 1, pp. 1–8, 2016.

E. Husni, F. Ismed, and D. Afriyandi, “Standardization Study of Simplicia and Extract of Calamondin (Citrus microcarpa Bunge) Peel, Quantification of Hesperidin and Antibacterial Assay,” Pharmacognosy Journal, vol. 12, no. 4, pp. 777–783, Jun. 2020, doi: 10.5530/pj.2020.12.111.

R. Rasyid, Y. Oktavia, F. Ismet, and H. Rivai, “Characterization of Simplicia and Ethanol Extracts of Bark of Asam Kandis (Garcinia cowa Roxb),” Int. Journal of Pharmaceutical Sciences and Medicine (IJPSM), vol. 3, pp. 1–9, 2018.

S. Sutomo, H. D. Lestari, A. Arnida, and A. Sriyono, “Simplicia and Extracts Standardization from Jualing Leaves (Micromelum minutum Wight & Arn.) from South Kalimantan,” Borneo Journal of Pharmacy, vol. 2, no. 2, pp. 55–62, Nov. 2019. doi: 10.33084/bjop.v2i2.898.

J. Wäldchen and P. Mäder, “Plant Species Identification Using Computer Vision Techniques: A Systematic Literature Review,” Archives of Computational Methods in Engineering, vol. 25, no. 2, pp. 507–543, Apr. 2018. DOI: 10.1007/s11831-016-9206-z.

B. R. Hussein, O. A. Malik, W.-H. Ong, and J. W. F. Slik, “Applications of computer vision and machine learning techniques for digitized herbarium specimens: A systematic literature review,” Ecological Informatics, vol. 69, p. 101641, Jul. 2022, doi: 10.1016/j.ecoinf.2022.101641.

Ü. Atila, M. Uçar, K. Akyol, and E. Uçar, “Plant leaf disease classification using EfficientNet deep learning model,” Ecological Informatics, vol. 61, p. 101182, Mar. 2021. DOI: 10.1016/j.ecoinf.2020.101182.

C. Yang, “Plant leaf recognition by integrating shape and texture features,” Pattern Recognition, vol. 112, p. 107809, Apr. 2021. DOI: 10.1016/j.patcog.2020.107809.

S. Alamoudi, X. Hong, and H. Wei, “Plant Leaf Recognition Using Texture Features and Semi-Supervised Spherical K-means Clustering,” in 2020 International Joint Conference on Neural Networks (IJCNN), 2020, pp. 1–8. DOI: 10.1109/IJCNN48605.2020.9207386.

T. Kasinathan and S. R. Uyyala, “Machine learning ensemble with image processing for pest identification and classification in field crops,” Neural Computing and Applications, vol. 33, no. 13, pp. 7491–7504, Jul. 2021. doi: 10.1007/s00521-020-05497-z.

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, doi: 10.1109/ACCESS.2020.3034033.

A. Taslim, S. Saon, A. K. Mahamad, M. Muladi, and W. N. Hidayat, “Plant leaf identification system using convolutional neural network,” Bulletin of Electrical Engineering and Informatics, vol. 10, no. 6, pp. 3341–3352, Dec. 2021, doi: 10.11591/eei.v10i6.2332.

A. Ahmed and S. E. Hussein, “Leaf identification using radial basis function neural networks and SSA based support vector machine,” PLOS ONE, vol. 15, no. 8, p. e0237645, Aug. 2020, doi: 10.1371/journal.pone.0237645.

H. Tang, H. Liu, W. Xiao, and N. Sebe, “When Dictionary Learning Meets Deep Learning: Deep Dictionary Learning and Coding Network for Image Recognition With Limited Data,” IEEE Transactions on Neural Networks and Learning Systems, vol. 32, no. 5, pp. 2129–2141, May 2021. doi: 10.1109/TNNLS.2020.2997289.

N. Sarika, N. Sirisala, and M. S. Velpuru, “CNN based Optical Character Recognition and Applications,” in 2021 6th International Conference on Inventive Computation Technologies (ICICT), 2021, pp. 666–672. doi: 10.1109/ICICT50816.2021.9358735.

Y. Lecun, L. Bottou, Y. Bengio, and P. Haffner, “Gradient-based learning applied to document recognition,” Proceedings of the IEEE, vol. 86, no. 11, pp. 2278–2324, 1998. doi: 10.1109/5.726791.

W. Liu et al., “A Time Delay Neural Network Based Technique for Nonlinear Microwave Device Modeling,” Micromachines, vol. 11, no. 9, p. 831, Aug. 2020. doi: 10.3390/mi11090831.

M. Desai and M. Shah, “An anatomization on breast cancer detection and diagnosis employing multi-layer perceptron neural network (MLP) and Convolutional neural network (CNN),” Clinical eHealth, vol. 4, pp. 1–11, 2021. doi: 10.1016/j.ceh.2020.11.002.

S. Nuanmeesri and W. Sriurai, “Multi-Layer Perceptron Neural Network Model Development for Chili Pepper Disease Diagnosis Using Filter and Wrapper Feature Selection Methods,” Engineering, Technology & Applied Science Research, vol. 11, no. 5, pp. 7714–7719, Oct. 2021. doi: 10.48084/etasr.4383.

J. Zhao, G. Shi, G.-B. Wang, and W.-Q. Zhang, “Automatic Speech Recognition for Low-Resource Languages: The Thuee Systems for the IARPA Openasr20 Evaluation,” in 2021 IEEE Automatic Speech Recognition and Understanding Workshop (ASRU), 2021, pp. 335–341. doi: 10.1109/ASRU51503.2021.9688260.

N. Omar, A. Sengur, and S. G. S. Al-Ali, “Cascaded deep learning-based efficient approach for license plate detection and recognition,” Expert Systems with Applications, vol. 149, p. 113280, Jul. 2020. doi: 10.1016/j.eswa.2020.113280.

M. Keivani, J. Mazloum, E. Sedaghatfar, and M. Tavakoli, “Automated Analysis of Leaf Shape, Texture, and Color Features for Plant Classification,” Traitement du Signal, vol. 37, no. 1, pp. 17–28, Feb. 2020. doi: 10.18280/ts.370103.

D. Bhatt et al., “CNN Variants for Computer Vision: History, Architecture, Application, Challenges and Future Scope,” Electronics, vol. 10, no. 20, p. 2470, Oct. 2021. doi: 10.3390/electronics10202470.

A. Gomaa, M. M. Abdelwahab, and M. Abo-Zahhad, “Efficient vehicle detection and tracking strategy in aerial videos by employing morphological operations and feature points motion analysis,” Multimedia Tools and Applications, vol. 79, no. 35–36, pp. 26023–26043, Sep. 2020. doi: 10.1007/s11042-020-09242-5.

W. Burger and M. J. Burge, “Scale-Invariant Feature Transform (SIFT),” 2022, pp. 709–763. doi: 10.1007/978-3-031-05744-1_25.

Jianbo Shi and Tomasi, “Good features to track,” in Proceedings of IEEE Conference on Computer Vision and Pattern Recognition CVPR-94, 1994, pp. 593–600. doi: 10.1109/CVPR.1994.323794.

D. G. Lowe, “Distinctive Image Features from Scale-Invariant Keypoints,” International Journal of Computer Vision, vol. 60, no. 2, pp. 91–110, Nov. 2004. doi: 10.1023/B:VISI.0000029664.99615.94.

S. Faizal, “Automated Identification of Tree Species by Bark Texture Classification Using Convolutional Neural Networks,” Oct. 2022. doi: 10.22214/ijraset.2022.46846.

W. Albattah, A. Javed, M. Nawaz, M. Masood, and S. Albahli, “Artificial Intelligence-Based Drone System for Multiclass Plant Disease Detection Using an Improved Efficient Convolutional Neural Network,” Frontiers in Plant Science, vol. 13, Jun. 2022. doi: 10.3389/fpls.2022.808380.

A. J. Hati and R. R. Singh, “Artificial Intelligence in Smart Farms: Plant Phenotyping for Species Recognition and Health Condition Identification Using Deep Learning,” AI, vol. 2, no. 2, pp. 274–289, Jun. 2021. doi: 10.3390/ai2020017.

T. Singh, K. Kumar, and S. Bedi, “A Review on Artificial Intelligence Techniques for Disease Recognition in Plants,” IOP Conference Series: Materials Science and Engineering, vol. 1022, no. 1, p. 012032, Jan. 2021. doi: 10.1088/1757-899X/1022/1/012032.

J. Huixian, “The Analysis of Plants Image Recognition Based on Deep Learning and Artificial Neural Network,” IEEE Access, vol. 8, pp. 68828–68841, 2020. doi: 10.1109/ACCESS.2020.2986946.

S. G. Wu, F. S. Bao, E. Y. Xu, Y.-X. Wang, Y.-F. Chang, and Q.-L. Xiang, “A Leaf Recognition Algorithm for Plant Classification Using Probabilistic Neural Network,” in 2007 IEEE International Symposium on Signal Processing and Information Technology, 2007, pp. 11–16. doi: 10.1109/ISSPIT.2007.4458016.

I. Yahiaoui, O. Mzoughi, and N. Boujemaa, “Leaf Shape Descriptor for Tree Species Identification,” in 2012 IEEE International Conference on Multimedia and Expo, 2012, pp. 254–259. doi: 10.1109/ICME.2012.130.

N. van Hieu and N. L. H. Hien, “Automatic Plant Image Identification of Vietnamese species using Deep Learning Models,” May 2020. doi: 10.14445/22315381/IJETT-V68I4P205S.

X. Liu, D. Zhang, T. Zhang, J. Zhang, and J. Wang, “A new path plan method based on hybrid algorithm of reinforcement learning and particle swarm optimization,” Engineering Computations, vol. 39, no. 3, pp. 993–1019, Mar. 2022. doi: 10.1108/EC-09-2020-0500.

H.-S. Jo, C. Park, E. Lee, H. K. Choi, and J. Park, “Path Loss Prediction Based on Machine Learning Techniques: Principal Component Analysis, Artificial Neural Network, and Gaussian Process,” Sensors, vol. 20, no. 7, p. 1927, Mar. 2020. doi: 10.3390/s20071927.

B. Fataniya, M. Joshi, U. Modi, and T. Zaveri, “Automatic Identification of Licorice and Rhubarb by Microscopic Image Processing,” Procedia Computer Science, vol. 58, pp. 723–730, 2015. doi: 10.1016/j.procs.2015.08.093.

P. Palimkar, R. N. Shaw, and A. Ghosh, “Machine Learning Technique to Prognosis Diabetes Disease: Random Forest Classifier Approach,” 2022, pp. 219–244. doi: 10.1007/978-981-16-2164-2_19

J. Tang, A. Henderson, and P. Gardner, “Exploring AdaBoost and Random Forests machine learning approaches for infrared pathology on unbalanced data sets,” The Analyst, vol. 146, no. 19, pp. 5880–5891, 2021. doi: 10.1039/D0AN02155E.

D. R. Sarvamangala and R. v. Kulkarni, “Convolutional neural networks in medical image understanding: a survey,” Evolutionary Intelligence, vol. 15, no. 1, pp. 1–22, Mar. 2022. doi: 10.1007/s12065-020-00540-3.

X. Zhang et al., “Understanding the learning mechanism of convolutional neural networks in spectral analysis,” Analytica Chimica Acta, vol. 1119, pp. 41–51, Jul. 2020. doi: 10.1016/j.aca.2020.03.055.

T. F. Gonzalez, Ed., Handbook of Approximation Algorithms and Metaheuristics. Chapman and Hall/CRC, 2007 [Online]. doi: 10.1201/9781420010749.

M. Lin, Q. Chen, and S. Yan, “Network In Network,” Dec. 2013.

K. Simonyan and A. Zisserman, “Very Deep Convolutional Networks for Large-Scale Image Recognition,” Sep. 2014.

C. Szegedy et al., “Going deeper with convolutions,” in 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2015, pp. 1–9. doi: 10.1109/CVPR.2015.7298594.

H. Zhang et al., “ResNeSt: Split-Attention Networks,” in 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), 2022, pp. 2735–2745. doi: 10.1109/CVPRW56347.2022.00309.

N. Verma and S. Tiwari, “Transfer Learning based Facial Emotion Recognization using Efficient Net-B0 CNN Model,” 2021.

I. Ihsan, E. W. Hidayat, and A. Rahmatulloh, “Identification of Bacterial Leaf Blight and Brown Spot Disease In Rice Plants With Image Processing Approach,” Jurnal Ilmiah Teknik Elektro Komputer dan Informatika, vol. 5, no. 2, p. 59, Feb. 2020. doi: 10.26555/jiteki.v5i2.14136.

D. M. Kumar, D. Satyanarayana, and M. N. G. Prasad, “An improved Gabor wavelet transform and rough K-means clustering algorithm for MRI brain tumor image segmentation,” Multimedia Tools and Applications, vol. 80, no. 5, pp. 6939–6957, Feb. 2021. doi: 10.1007/s11042-020-09635-6.

I. J. Hussein, M. A. Burhanuddin, M. A. Mohammed, N. Benameur, M. S. Maashi, and M. S. Maashi, “Fully‐automatic identification of gynaecological abnormality using a new adaptive frequency filter and histogram of oriented gradients (HOG),” Expert Systems, vol. 39, no. 3, Mar. 2022. doi: 10.1111/exsy.12789.

B. K. Varghese, A. Augustine, J. M. Babu, D. Sunny, and S. Cherian, “INFOPLANT: Plant Recognition using Convolutional Neural Networks,” in 2020 Fourth International Conference on Computing Methodologies and Communication (ICCMC), 2020, pp. 800–807. doi: 10.1109/ICCMC48092.2020.ICCMC-000149.

R. U. Rao, M. S. Lahari, K. P. Sri, K. Y. Srujana, and D. Yaswanth, “Identification of Medicinal Plants using Deep Learning,” International Journal for Research in Applied Science and Engineering Technology, vol. 10, no. 4, pp. 306–322, Apr. 2022. doi: 10.22214/ijraset.2022.41190.

A. H. Aono et al., “A stomata classification and detection system in microscope images of maize cultivars,” PLOS ONE, vol. 16, no. 10, p. e0258679, Oct. 2021. doi: 10.1371/journal.pone.0258679.

W. Cui, Q. Lu, A. M. Qureshi, W. Li, and K. Wu, “An adaptive LeNet-5 model for anomaly detection,” Information Security Journal: A Global Perspective, vol. 30, no. 1, pp. 19–29, Jan. 2021. doi: 10.1080/19393555.2020.1797248.

DOI: http://dx.doi.org/10.18517/ijaseit.13.4.18436


  • There are currently no refbacks.

Published by INSIGHT - Indonesian Society for Knowledge and Human Development