A Review on Detection of Medical Plant Images

Main Article Content

Marada Srinivasa Rao
S. Praveen Kumar
Konda Srinivasa Rao

Abstract

Both human and non-human life on Earth depends heavily on plants. The natural cycle is most significantly influenced by plants. Because of the sophistication of recent plant discoveries and the computerization of plants, plant identification is particularly challenging in biology and agriculture. There are a variety of reasons why automatic plant classification systems must be put into place, including instruction, resource evaluation, and environmental protection. It is thought that the leaves of medicinal plants are what distinguishes them. It is an interesting goal to identify the species of plant automatically using the photo identity of their leaves because taxonomists are undertrained and biodiversity is quickly vanishing in the current environment. Due to the need for mass production, these plants must be identified immediately. The physical and emotional health of people must be taken into consideration when developing drugs. To important processing of medical herbs is to identify and classify. Since there aren't many specialists in this field, it might be difficult to correctly identify and categorize medicinal plants. Therefore, a fully automated approach is optimal for identifying medicinal plants. The numerous means for categorizing medicinal plants that take into interpretation based on the silhouette and roughness of a plant's leaf are briefly précised in this article.

Article Details

How to Cite
Rao, M. S. ., Kumar, S. P. ., & Rao, K. S. . (2023). A Review on Detection of Medical Plant Images. International Journal on Recent and Innovation Trends in Computing and Communication, 11(4), 54–64. https://doi.org/10.17762/ijritcc.v11i4.6381
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References

Serifar, R.; Bahmani, M.; Abdi, J.; Abbaszadeh, S.; Nourmohammadi, G.A.; Rafieian-Kopaei, M. A review of the most important native medicinal plants of Iran effective on leishmaniasis according to Iranian ethnobotanical references. Int. J. Adv. Biotechnol. Res. 2017, 8, 1330–1336.

Altemimi, A.; Lakhssassi, N.; Baharlouei, A.; Watson, D.G.; Lightfoot, D.A. Phytochemicals: Extraction, isolation, and identification of bioactive compounds from plant extracts. Plants 2017, 6, 42.

Naeem, S.; Ali, A.; Chesneau, C.; Tahir, M.H.; Jamal, F.; Sherwani, R.A.K.; Ul Hassan, M. The classification of medicinal plant leaves based on multispectral and texture feature using machine learning approach. Agronomy 2021, 11, 263.

Ozioma, E.O.J.; Chinwe, O.A.N. Herbal medicines in African traditional medicine. Herb. Med. 2019, 10, 191–214.

Amenu, E. Use and Management of Medicinal Plants by Indigenous People of Ejaji Area (Chelya Wored) West Shoa, Ethiopia: An Ethnobotanical Approach. Master’s Thesis, Addis Ababa University, Addis Ababa, Ethiopia, 2007. hnobiol. Ethnomed. 2020, 16, 40.

Crini, G.; Lichtfouse, E.; Chanet, G.; Morin-Crini, N. Applications of hemp in textiles, paper industry, insulation and building materials, horticulture, animal nutrition, food and beverages, nutraceuticals, cosmetics and hygiene, medicine, agrochemistry, energy production and environment: A review. Environ. Chem. Lett. 2020, 18, 1451–1476.

Chukwuma, E.C.; Soladoye, M.O.; Feyisola, R.T. Traditional medicine and the future of medicinal Plants in Nigeria. J. Med. Plants Stud. 2015, 3, 23–29.

J. O. Ezekwesili-Ofili and A. N. C. Okaka, “Herbal Medicines in African Traditional Medicine,” Herbal Medicine, 2019.

K. Pushpanathan, M. Hanafi, S. Mashohor, and W. F. Fazlil Ilahi, “Machine Learning in Medicinal Plants Recognition: A Review,” Artificial Intelligence Review, 2020.

K. B. Barimah and C. S. Akotia, “The promotion of traditional medicine as enactment of community psychology in Ghana,” Journal of Community Psychology, vol. 43, no. 1, pp. 99–106, Dec 2015.

P. P. Kaur, S. Singh, and M. Pathak, “Review of machine learning herbal plant recognition system,” SSRN Electronic Journal, 2020.

A. A. Boadu and A. Asase, “Documentation of herbal medicines used for the treatment and management of human diseases by some communities in southern Ghana,” Evidence- based Complementary and Alternative Medicine, pp. 1–12, 2017.

A. R. Sfar, N. Boujemaa, and D. Geman, “Vantage feature frames for fine-grained categorization,” in Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 835–842, San Juan, PR, USA, 2013.

J. W¨aldchen and P. M¨ader, “Plant Plant Species Identification Using Computer Vision Techniques: A Systematic Literature Reviewpecies identification using computer vision techniques: a systematic literature review,” Archives of Computational Methods in Engineering, vol. 25, no. 2, pp. 507–543, 2018.

M. ?Sulc and J. Matas, “Fine-grained recognition of plants from images,” Plant Methods, vol. 13, no. 1, p. 115, Dec 2017.

M. Lasseck, “Image-based plant species identification with deep Convolutional Neural Networks,” CEUR Workshop Proceedings, vol. 1866, 2017.

S. O. Oppong, “Ghanaian Leaf Dataset,” Textural Analysis for Medicinal Plants Identification Using Log Gabor Filters, 2022.

A. Sarkar, Y. Yang, and M. Vihinen, “Variation benchmark datasets: update, criteria, quality and applications,” Database, p. baz117, Jan, 2020.

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 Proceedings of the 2007 IEEE International Symposium on Signal Processing and Information Technology, pp. 11–16, Giza, Egypt, December 2007.

Y. Zhang, J. Cui, Z. Wang, J. Kang, and Y. Min, “Leaf image recognition based on bag of features,” Applied Sciences, vol. 10, pp. 5177–15, 2020.

O. J. O. S¨oderkvist, “Computer Vision Classification of Leaves from Swedish Trees,” Computer Vision,” Department of Electrical Engineering Link¨oping University, 2001.

S. Kaur and P. Kaur, “Plant Species Identification based on Plant Leaf Using Computer Vision and Machine Learning Techniques,” Journal of Multimedia Information System, vol. 6, no. 2, pp. 49–60, 2019.

S. Roopashree and J. Anitha, Medicinal Leaf Dataset, Mendeley Data, 2020.

Wu, S.G., Bao, F.S., Xu, E.Y., Wang, Y.X., Chang, Y.F., Xiang, Q.L. (2007). A leaf recognition algorithm for plant classification using probabilistic neural network. In 2007 IEEE International Symposium on Signal Processing and Information Technology, pp. 11-16.

T. Munisami, M. Ramsurn, S. Kishnah, and S. Pudaruth, “Plant Leaf Recognition Using Shape Features and Colour Histogram with K-nearest Neighbour Classifiers,” Procedia Computer Science, vol. 58, pp. 740–747, 2015.

Hlaing, C.S.; Zaw, S.M.M. Model-based statistical features for mobile phone image of tomato plant disease classification. In Proceedings of the 2017 18th International Conference on Parallel and Distributed Computing, Applications and Technologies (PDCAT), Taipei, Taiwan, 18–20 December 2017; pp. 223–229.

Camargo, A.; Smith, J. Image pattern classification for the identification of disease causing agents in plants. Comput. Electron. Agric. 2009, 66, 121–125.

Prasad, S.; Peddoju, S.K.; Ghosh, D. Multi-resolution mobile vision system for plant leaf disease diagnosis. Signal Image Video Process. 2016, 10, 379–388.

Islam, M.; Dinh, A.;Wahid, K.; Bhowmik, P. Detection of potato diseases using image segmentation and multiclass support vector machine. In Proceedings of the 2017 IEEE 30th Canadian Conference on Electrical and Computer Engineering (CCECE), Windsor, ON, Canada, 30 April–3 May 2017; pp. 1–4.

Anthonys, G.; Wickramarachchi, N. An image recognition system for crop disease identification of paddy fields in Sri Lanka. In Proceedings of the 2009 International Conference on Industrial and Information Systems (ICIIS), Peradeniya, Sri Lanka, 28–31 December 2009; pp. 403–407.

Yao, Q.; Guan, Z.; Zhou, Y.; Tang, J.; Hu, Y.; Yang, B. Application of support vector machine for detecting rice diseases using shape and color texture features. In Proceedings of the International Conference on Engineering Computation, Vancouver, BC, Canada, 29–31 August 2009; pp. 79–83.

Al Bashish, D.; Braik, M.; Bani-Ahmad, S. A framework for detection and classification of plant leaf and stem diseases. In Proceedings of the 2010 International Conference on Signal and Image Processing (ICSIP), Chennai, India, 15–17 December 2010; pp. 113–118.

Padol, P.B.; Yadav, A.A. SVM classifier based grape leaf disease detection. In Proceedings of the 2016 Conference on Advances in Signal Processing (CASP), Pune, India, 9–11 June 2016; pp. 175–179.

Padol, P.B.; Sawant, S. Fusion classification technique used to detect downy and Powdery Mildew grape leaf diseases. In Proceedings of the 2016 International Conference on Global Trends in Signal Processing, Information Computing and Communication (ICGTSPICC), Jalgaon, India, 22–24 December 2016; pp. 298–301.

Pujari, J.D.; Yakkundimath, R.; Byadgi, A.S. Image processing-based detection of fungal diseases in plants. Procedia Comput. Sci. 2015, 46, 1802–1808.

Chuanlei, Z.; Shanwen, Z.; Jucheng, Y.; Yancui, S.; Jia, C. Apple leaf disease identification using genetic algorithm and correlation-based feature selection method. Int. J. Agric. Biol. Eng. 2017, 10, 74–83.

Sabrol, H.; Satish, K. Tomato plant disease classification in digital images using classification tree. In Proceedings of the 2016 International Conference on Communication and Signal Processing (ICCSP), Melmaruvathur, India, 6–8 April 2016; pp. 1242–1246.

Habibollah Agh “A Convolutional Neural Network with A New Architecture Applied On Leaf Classification,” The IIOAB Journal, Vol. 07, Suppl 05, ISSN 0326 - 0331.

Jiachun Liu, Jia at al., “Unsupervised Representation Learning of Image Based Plant Disease with Deep Convolutional Generative Adversarial Networks,” IEEE, 37th Chinese Control Conference, Wuhan, 2018, pp. 09159 – 09163.

Rhouma, M.B.H., Žuni?, J. and Younis, M.C., 2017. Moment invariants for multi-component shapes with applications to leaf classification. Computers and electronics in agriculture, 142, pp.326- 337.

Chau, A.L., Hernandez, R.R., Mora, V.T., Canales, J.C., Mazahua, L.R. and Lamont, F.G., 2017. Detection of Compound Leaves for Plant Identification. IEEE Latin America Transactions, 15(11), pp.2185- 2190.

Grinblat, G.L., Uzal, L.C., Larese, M.G. and Granitto, P.M., 2016. Deep learning for plant identification using vein morphological patterns. Computers and Electronics in Agriculture, 127, pp.418-424.

Roshchina, V.V., Kuchin, A.V. and Yashin, V.A., 2017. Application of Autofluorescence for Analysis of Medicinal Plants. International Journal of Spectroscopy, 2017.

Pushpa BR, Anand C and Mithun Nambiar P, “Ayurvedic Plant Species Recognition using Statistical Parameters on Leaf Images”, International Journal of Applied Engineering Research, Vol 11, No 7, pp 5142-5147, 2016.

Fan Shizhong, “Gelsemium elegan – An intangible killer,” Medpharm & Health, 2008, 4,36.

Herdiyeni, Y., & Santoni, M. M. Combination of morphological, local binary pattern variance and color moments features for Indonesian medicinal plants identification. In Advanced Computer Science and Information Systems (ICACSIS) IEEE International Conference, 2012, 255-259.

Janani, R., & Gopal, A. Identification of selected medicinal plant leaves using image features and ANN. In Advanced Electronic Systems (ICAES), IEEE International Conference. 2013, 238-242.

Kumar, E. S., &Talasila, V. Leaf features-based approach for automated identification of medicinal plants. In Communications and Signal Processing (ICCSP), IEEE International Conference, 2014, 210-214.

Rega, P. K., &Emantoko, S. Microplate luminescence automated digital analyzer for medicinal plants evaluation on quorum sensing inhibition. In QiR (Quality in Research), IEEE International Conference, 2013, 31-34

H. X. Kan, L. Jin, and F. L. Zhou, “Classification of medicinal plant leaf image based on multi-feature extraction,” Pattern Recognition and Image Analysis, vol. 27, no. 3, pp. 581–587, 2017.

A. Begue, V. Kowlessur, U. Singh, F. Mahomoodally, and S. Pudaruth, “Automatic recognition of medicinal plants using machine learning techniques,” International Journal of Advanced Computer Science and Applications, vol. 8, no. 4, pp. 166–175, 2017.

R. G. De Luna, R. G. Baldovino, E. A. Cotoco et al., “Identification of philippine herbal medicine plant leaf using artificial neural network,” HNICEM, in Proceedings of the 2017 - 9th International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment and Management, pp. 1–8, Manila, Philippines, 2018.

T. Vijayshree and A. Gopal, “Identification of herbal plant leaves using image processing algorithm: review,” Research Journal of Pharmaceutical, Biological and Chemical Sciences, vol. 9, no. 4, pp. 1221–1228, 2018.

L. Britto and L. Pacifico, “Plant species classification using Extreme learning machine,” Anais do XVI Encontro Nacional de Inteligencia Artificial e Computational, pp. 13–24, 2019.

D. M. C. Dissanayake and W. G. C. W. Kumara, “Plant Leaf Identification Based on Machine Learning Algorithms,” Sri Lankan Journal of Technology, pp. 60–66, 2021.

S. Naeem, A. Ali, C. Chesneau, M. H. Tahir, and F. Jamal, “The Classification of Medicinal Plant Leaves Based on Multispectral and Texture Feature Using Machine Learning Approach,” Agronomy, vol. 11, no. 2, 2021.

J. R. Xue, S. Fuentes, C. Poblete-Echeverria et al., “Automated Chinese medicinal plants classification based on machine learning using leaf morpho-colorimetry, fractal dimension and visible/near infrared spectroscopy,” International Journal of Agricultural and Biological Engineering, vol. 12, no. 2, pp. 123–131, 2019.

S. Kaur and P. Kaur, “Plant species identification based on plant leaf using computer vision and machine learning techniques,” Journal of Multimedia Information System, vol. 6, no. 2, pp. 49–60, 2019.

M. M. Singh, “A Survey on Different Methods for Medicinal Plants Identification and Classification System on different methods for medicinal plants identification and classification system,” Revista Gestão Inovação e Tecnologias, vol. 11, no. 4, pp. 3191–3202, 2021.

M. Jaiganesh, M. Sathyadevi, K. S. Chakravarthy, and C. Sarada, “Identification of plant species using CNN classifier,” Journal Of Critical Reviews, vol. 7, no. 3, pp. 923–931, 2020.

C. Zhang, P. Zhou, C. Li, and L. Liu, “A Convolutional Neural Network for Leaves Recognition Using Data Augmentation,” in Proceedings of the 2015 IEEE International Conference on Computer and Information Technology; Ubiquitous Computing and Communications; Dependable, Autonomic and Secure Computing Pervasive Intelligence and

Computing, Liverpool, UK, December2015.

H. X. Huynh, B. Q. Truong, K. T. Nguyen Thanh, and D. Q. Truong, “Plant Identification Using New Architecture Convolutional Neural Networks Combine with Replacing the Red of Color Channel Image by Vein Morphology Leaf identification using new architecture convolutional neural networks combine with replacing the red of color channel image by vein morphology leaf,” Vietnam Journal of Computer Science, vol. 07, no. 02, pp. 197–208, Feb 2020.

M. Sulc and J. Matas, Texture-Based Leaf Identification,” Computer Vision - ECCV 2014 Workshops, Springer International Publishing, Midtown Manhattan, New York City, pp. 185–200, 2015.

P. Pawara, E. Okafor, L. Schomaker, and M. Wiering, Data Augmentation for Plant Classification,” Advanced Concepts For Intelligent Vision Systems, Springer International Publishing, Midtown Manhattan, New York City, pp. 615–626, 2017.

P. Barre, B. C. St¨over, K. F. M¨uller, and V. Steinhage, “LeafNet: A computer vision system for automatic plant species identification,” Ecological Informatics, vol. 40, pp. 50–56, 2017.

A. Tharwat, “Classification assessment methods,” Applied Computing and Informatics, vol. 17, no. 1, pp. 168–192, Jan. 2021.