A lightweight deep learning method for medicinal leaf image classification using feature fusion

Machine Learning


  • Yurdakul, M. et al. MBConv-ECA based YOLO framework for blood cell detection. Signal, Image and Video Processing. ;19(9):712.;; Extraction, isolation, and identification of bioactive compounds from plant extracts. Plants 2017, 6, 42. (2025).

  • Naeem, S. et al. Ul hassan, M. The classification of medicinal plant leaves based on multispectral and texture feature using machine learning approach. Agronomy 11, 263 (2021).

    Article 

    Google Scholar 

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

    Google Scholar 

  • Amenu, E. Use and management of medicinal plants by indigenous people of Ejaji Area (Chelya Woreda) West Shoa, Ethiopia: an ethnobotanical approach. master’s thesis, addis ababa university, Addis Ababa, Ethiopia, (2007).

  • Hu, R., Lin, C., Xu, W., Liu, Y. & Long, C. Ethnobotanical study on medicinal plants used by Mulam people in guangxi, China. J. Ethnobiol. Ethnomed. 16, 40 (2020).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar 

  • 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. 18, 1451–1476 (2020).

    Article 
    CAS 

    Google Scholar 

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

    Google Scholar 

  • Sladojevic, S., Arsenovic, M., Anderla, A., Culibrk, D. & Stefanovic, D. Deep neural networks based recognition of plant diseases by leaf image classification. Comput. Intell. Neurosci. 2016, 3289801. (2016).

  • Singh, V. & Misra, A. K. Detection of plant leaf diseases using image segmentation and soft computing techniques. Inf. Process. Agric. 4, 41–49 (2017).

    Google Scholar 

  • Chouhan, S. S., Singh, U. P., Saxena, A. & Jain, S. Assessing the importance and need of artificial intelligence for precision agriculture. In Artificial Intelligence Techniques in Smart Agriculture 1–6 (Springer Nature Singapore, 2024).

    Chapter 

    Google Scholar 

  • Azadnia, R. & Kheiralipour, K. Recognition of leaves of different medicinal plant species using a robust image processing algorithm and artificial neural networks classifier. J. Appl. Res. Med. Aromat. Plants. 25, 100327 (2021).

    Google Scholar 

  • Uyar, K., Yurdakul, M. & Taşdemir, Ş. Abc-based weighted voting deep ensemble learning model for multiple eye disease detection. Biomed. Signal Process. Control. 96, 106617 (2024).

    Article 

    Google Scholar 

  • Yurdakul, M., Atabaş, İ. & Taşdemir, Ş. Almond (Prunus dulcis) varieties classification with genetic designed lightweight CNN architecture. Eur. Food Res. Technol. 250 (10), 2625–2638 (2024).

    Article 
    CAS 

    Google Scholar 

  • Bisen, D. Deep convolutional neural network based plant species recognition through features of leaf. Multimed Tools Appl. 80, 6443–6456 (2021).

    Article 

    Google Scholar 

  • Islam, S. et al. A leaf images dataset for Bangladeshi medicinal plants identification. Data Brief. 50, 109488 (2023).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Yurdakul, M., Uyar, K. & Taşdemir, Ş. Enhanced ore classification through optimized CNN ensembles and feature fusion. Iran. J. Comput. Sci. 8 (2), 491–509 (2025).

    Article 

    Google Scholar 

  • Şüyun, S. B., Yurdakul, M., Taşdemir, Ş. & Biliş, S. Triple-stream deep feature selection with metaheuristic optimization and machine learning for multi-stage hypertensive retinopathy diagnosis. Appl. Sci. 15 (12), 6485 (2025).

    Article 

    Google Scholar 

  • Yurdakul, M., Uyar, K., TaŞdemir, Ş. & AtabaŞ, İ. ROPGCViT: A novel explainable vision transformer for retinopathy of prematurity diagnosis. IEEE Access.13 77064–77079, https://doi.org/10.1109/ACCESS.2025.3564213 (2025).

  • Jamgaonkar, S., Gowda, J. S., Chouhan, S. S., Patel, R. K. & Pandey, A. October. An analysis of different yolo models for real-time object detection. in 2024 4th international conference on sustainable expert systems (ICSES) (pp. 951–955). IEEE. (2024).

  • Rao, M. S., Kumar, S. P. & Rao, K. S. A methodology for identification of ayurvedic plant based on machine learning algorithms. Int. J. Comput. Digit. Syst. 14, 10233–10241 (2023).

    Article 

    Google Scholar 

  • Tsourounis, D., Kastaniotis, D., Theoharatos, C., Kazantzidis, A. & Economou, G. SIFT-CNN: when convolutional neural networks Meet dense sift descriptors for image and sequence classification. J. Imaging. 8, 256 (2022).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Abdulhussain, S. H., Mahmmod, B. M., Flusser, J., AL-Utaibi, K. A. & Sait, S. M. Fast overlapping block processing algorithm for feature extraction. Symmetry 14, 715 (2022).

    Article 
    ADS 

    Google Scholar 

  • Ramaha, N. T. et al. Brain pathology classification of Mr images using machine learning techniques. Computers 12, 167 (2023).

    Article 

    Google Scholar 

  • Beikmohammadi, A., Faez, K., Motallebi, A. & SWP-Leaf, N. E. T. A novel multistage approach for plant leaf identification based on deep learning CNN, ExpertSystems with Applications. ArXiv 202 117470, ISSN 0957–4174, https://doi.org/10.1016/j.eswa(2022).

  • Vyas, S., Mukhija, M. K. & Alaria, S. K. An efficient approach for plant leaf species identification based on SVM and SMO and performance improvement. In Intelligent Systems and Applications; Lecture Notes in Electrical Engineering 9593–15 https://doi.org/10.1007/978-981-19-6581-4_1(2023).

    Google Scholar 

  • Ambarwari, A., Adrian, Q. J., Herdiyeni, Y. & Hermadi, I. Plant species identification based on leaf venation features using SVM. Telkomnika. 18, 726. (2020).

  • Chouhan, S. S., Singh, U. P. & Jain, S. Performance Evaluation of Different Deep Learning Models Used for the Purpose of Healthy and Diseased Leaves Classification of Cherimoya (Annona Cherimola) Plantpp.1–14 (Neural Computing and Applications, 2024).

  • Zhang, F. & Zhang, X. Classification and quality evaluation of tobacco leaves based on image processing and fuzzy comprehensive evaluation. Sensors 11, 2369–2384 (2011).

    Article 
    ADS 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Rahmatullah, M. et al. A survey of medicinal plants used by Garo and non-Garo traditional medicinal practitioners in two villages of Tangail district. Bangladesh Am. Eurasian J. Sustain. Agric. 5, 350–357 (2011).

    Google Scholar 

  • Dahigaonkar, T. D. & Kalyane, R. Identification of ayurvedic medicinal plants by image processing of leaf samples. Int. Res. J. Eng. Technol. (Irjet). 5, 351–355 (2018).

    Google Scholar 

  • Sabarinathan, C., Hota, A., Raj, A., Dubey, V. K. & Ethirajulu, V. Medicinal plant leaf recognition and show medicinal uses using convolutional neural network. Int. J. Glob Eng. 1, 120–127 (2018).

    Google Scholar 

  • Khirade, S. D. & Patil, A. B. February. Plant disease detection using image processing. In Proceedings of the 2015 International Conference on Computing Communication Control and Automation, Pune, India, 26–27 ; pp. 768–771. (2015).

  • Wallelign, S., Polceanu, M. & Buche, C. Soybean plant disease identification using convolutional neural network. In Proceedings of the Thirty-First International Flairs Conference, Melbourne, FL, USA, 10 May (2018).

  • Simion, I. M., Casoni, D. & Sârbu, C. Classification of Romanian medicinal plant extracts according to the therapeutic effects using thin layer chromatography and robust chemometrics. J. Pharm. Biomed. Anal. 163, 137–143 (2019). [PubMed].

    Article 
    CAS 
    PubMed 

    Google Scholar 

  • Dhingra, G., Kumar, V. & Joshi, H. D. A novel computer vision based neutrosophic approach for leaf disease identification and classification. Measurement 135, 782–794 (2019).

    Article 
    ADS 

    Google Scholar 

  • Turkoglu, M. & Hanbay, D. Leaf-based plant species recognition based on improved local binary pattern and extreme learning machine. Phys. Stat. Mech. Appl. 527, 121297 (2019).

    Article 

    Google Scholar 

  • Qadri, S. et al. Machine vision approach for classification of citrus leaves using fused features. Int. J. Food Prop. 22, 2072–2089 (2019).

    Article 

    Google Scholar 

  • Patel, R. K., Chaudhary, A., Chouhan, S. S. & Pandey, K. K. Mango leaf disease diagnosis using Total Variation Filter Based Variational Mode Decomposition. Computers and Electrical Engineering, 120, p.109795. (2024).

  • Kan, H. X., Jin, L. & Zhou, F. L. Classification of medicinal plant leaf image based on multi-feature extraction. Pattern Recognit. Image Anal. 27 (3), 581–587 (2017).

    Article 

    Google Scholar 

  • Begue, A., Kowlessur, V., Singh, U., Mahomoodally, F. & Pudaruth, S. Automatic recognition of medicinal plants using machine learning techniques. Int. J. Adv. Comput. Sci. Appl. 8 (4), 166–175 (2017).

    Google Scholar 

  • De Luna, R. G. 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).

  • Vijayshree, T. & Gopal, A. Identification of herbal plant leaves using image processing algorithm: review. Res. J. Pharm. Biol. Chem. Sci. 9 (4), 1221–1228 (2018).

    Google Scholar 

  • Gupta, S. et al. Secure and lightweight authentication protocol for privacy preserving communications in smart City applications. Sustainability 15, 5346. https://doi.org/10.3390/su15065346 (2023).

    Article 
    ADS 

    Google Scholar 

  • Britto, L. & Pacifico, L. Plant species classification using Extreme learning machine, Anais do XVI Encontro Nacional de Inteligˆencia Artificial e Computacional, pp. 13–24, (2019).

  • Dissanayake, D. M. C. & Kumara, W. G. C. W. Plant leaf identification based on machine learning algorithms. Sri Lankan J. Technology, 60–66, (2021).

  • Naeem, S., Ali, A., Chesneau, C., Tahir, M. H. & Jamal, F. The classification of medicinal plant leaves based on multispectral and texture feature using machine learning approach. Agronomy, 11, 2, 263 https://doi.org/10.3390/agronomy11020263 (2021).

  • Xue, J. R. et al. Automated Chinese medicinal plants classification based on machine learning using leaf morpho-colorimetry, fractal dimension and visible/near infrared spectroscopy. Int. J. Agricultural Biol. Eng. 12 (2), 123–131 (2019).

    Article 

    Google Scholar 

  • Kaur, S. & Kaur, P. Plant species identification based on plant leaf using computer vision and machine learning techniques. J. Multimedia Inform. Syst. 6 (2), 49–60 (2019).

    Article 
    MathSciNet 

    Google Scholar 

  • Chouhan, S. S., Singh, U. P., Sharma, U. & Jain, S. Classification of different plant species using deep learning and machine learning algorithms. Wireless Pers. Commun. 136 (4), 2275–2298 (2024).

    Article 

    Google Scholar 

  • Hu, J., Chen, Z., Yang, M., Zhang, R. & Cui, Y. A multiscale fusion convolutional neural network for plant leaf recognition. IEEE Signal. Process. Lett. 25, 853–857 (2018).

    Article 
    ADS 

    Google Scholar 

  • Azizi, A., Gilandeh, Y. A., Mesri-Gundoshmian, T., Saleh-Bigdeli, A. A. & Moghaddam, H. A. Classification of soil aggregates: A novel approach based on deep learning. Soil. Tillage Res. 199, 104586 (2020).

    Article 

    Google Scholar 

  • Logeshwaran, J. et al. Improving crop production using an agro-deep learning framework in precision agriculture. BMC Bioinform. 25, 341. https://doi.org/10.1186/s12859-024-05970-9 (2024).

    Article 
    CAS 

    Google Scholar 

  • Pearline, S. A., Kumar, V. S. & Harini, S. A study on plant recognition using conventional image processing and deep learning approaches. J. Intell. Fuzzy Syst. 36, 1997–2004 (2019).

    Google Scholar 

  • Zhu, Y. et al. TA-CNN: Two-way attention models in deep convolutional neural network for plant recognition. Neurocomputing 365, 191–200 (2019).

    Article 

    Google Scholar 

  • Muneer, A. & Fati, S. M. Efficient and automated herbs classification approach based on shape and texture features using deep learning. IEEE Access. 8, 196747–196764 (2020).

    Article 

    Google Scholar 

  • Reddy, S. R., Varma, G. P. & Davuluri, R. L. Optimized convolutional neural network model for plant species identification from leaf images using computer vision. Int J. Speech Technol 26 23–50 https://doi.org/10.1007/s10772-021-09843-x (2021).

  • Zhang, C., Zhou, P., Li, C. & Liu, L. 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, 26–28 October ; pp. 2143–2150. (2015).

  • Roopashree, S., Anitha, J. & DeepHerb: A vision based system for medicinal plants using Xception features. IEEE Access. 9, 135927–135941 (2021).

    Article 

    Google Scholar 

  • Leila, E., Othman, S. B. & Sakli, H. An Internet of Robotic Things System for combating coronavirus disease pandemic(COVID-19), 2020 20th International Conference on Sciences and Techniques of Automatic Control and Computer Engineering (STA), Monastir, Tunisia, pp. 333–337, (2020). https://doi.org/10.1109/STA50679.2020.9329310

  • Prasvita, D. S., Herdiyeni, Y. & MedLeaf Mobile application for medicinal plant identification based on leaf image. Int. J. Adv. Sci. Eng. Inf. Technol. 3, 103 (2013).

    Article 

    Google Scholar 

  • Herdiyeni, Y. & Wahyuni, N. K. S. Mobile application for Indonesian medicinal plants identification using fuzzy local binary pattern and fuzzy color histogram. In Proceedings of the International Conference on Advanced Computer Science and Information Systems (ICACSIS), Depok, Indonesia, 1–2 December 2012; IEEE: Piscatway, NJ, USA, ; pp. 301–306. (2012).

  • Cheng, Q., Zhao, H., Wang, C. & Du, H. An android application for plant identification. in proceedings of the 4th information technology and mechatronics engineering conference (itoec), Chongqing, China, 14–16 December 2018; IEEE: Piscatway, NJ, USA, ; pp. 60–64. (2018).

  • Munisami, T., Ramsurn, M., Kishnah, S. & Pudaruth, S. Plant leaf recognition using shape features and colour histogram with K-nearest neighbour classifiers. Procedia Comput. Sci. 58, 740–747 (2015).

    Article 

    Google Scholar 

  • Zhao, Z. Q. et al. ApLeaf: an efficient android-based plant leaf identification system. Neurocomputing 151, 1112–1119 (2015).

    Article 

    Google Scholar 

  • Priyankara, H. A. C. & Withanage, D. K. Computer assisted plant identification system for Android. In Proceedings of the moratuwa engineering research conference (MERCon), Moratuwa, Sri Lanka, 7–8 April 2015; IEEE: Piscatway, NJ, USA, ; pp. 148–153. (2015).

  • Akiyama, T. et al. Mobile Leaf Identification System using CNN applied to plants in Hokkaido. In Proceedings of the 8th Global Conference on Consumer Electronics (GCCE), Osaka, Japan, 15–18 October ; IEEE: Piscatway, NJ, USA, 2019; pp. 324–325. (2019).

  • Singh, M. M. A A survey on different methods for medicinal plants identification and classification systemurvey on different methods for medicinal plants identification and classification system. Revista Gestão Inovação E Tecnologias. 11 (4), 3191–3202 (2021).

    Article 

    Google Scholar 

  • Nguyen, T. T. N., Le, T. L., Vu, H. & Hoang, V. S. Towards an automatic plant identification system without dedicated dataset. Int. J. Mach. Learn. Comput. 9 (1), 26–34 (2019).

    Article 

    Google Scholar 

  • Jaiganesh, M., Sathyadevi, M., Chakravarthy, K. S. & Sarada, C. Identification of plant species using CNNclassifier. J. Crit. Reviews. 7 (3), 923–931 (2020).

    Google Scholar 

  • Huynh, H. X., Truong, B. Q., Nguyen Thanh, K. T. & Truong, D. Q. Plant plant identification using new architecture convolutional neural networks combine with replacing the red of color channel image by vein morphology leafdentification using new architecture convolutional neural networks combine with replacing the red of color channel image by vein morphology leaf. Vietnam J. Comput. Sci., 07, 02, 197–208, (2020).

  • Banzi, J. & Abayo, T. Plant species identification from leaf images using deep learning models (CNN-LSTM architecture). Tanzan. J. Forestry Nat. Conserv. 90 (3), 93–103 (2021).

    Google Scholar 

  • Karahan, T. & Nabiyev, V. Plant identification with convolutional neural networks and transfer learning. Pamukkale Univ. J. Eng. Sci. 27 (5), 638–645 (2021).

    Article 

    Google Scholar 

  • Pravin, A. & Deepa, C. A identification of Piper plant species based on deep learning networks. Turkish J. Comput. Math. Educ. (TURCOMAT). 12 (10), 6740–6749 (2021).

    Google Scholar 

  • Chung, Y., Chou, C. A. & Li, C. Y. Central attention and a dual path convolutional neural network in real-world tree species recognition. Int. J. Environ. Res. Public Health. 18 (3), 961–929 (2021).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Adetiba, E. et al. LeafsnapNet: leafsnapnet: an experimentally evolved deep learning model for recognition of plant species based on leafsnap image datasetn experimentally evolved deep learning model for recognition of plant species based on leafsnap image dataset. J. Comput. Sci. 17 (3), 349–363 (2021).

    Article 

    Google Scholar 

  • Quoc Bao, T., Tan Kiet, N. T., Quoc Dinh, T. & Hiep, H. X. Plant species identification from leaf patterns using histogram of oriented gradients feature space and convolution neural networks, Journal of Information and Telecommunication, vol. 4, no. 2, pp. 140–150, Sep. (2020).

  • Mehdipour Ghazi, M., Yanikoglu, B. & Aptoula, E. Plant identification using deep neural networks via optimization of transfer learning parameters. Neurocomputing 235, 228–235 (April 2016).

  • Krause, J., Sugita, G., Baek, K. & Lim, L. WTPlant (What’s That Plant?) A deep learning system for identifying plants in natural images, in Proceedings of the ACM on international conference on multimedia retrieval, pp. 517– 520, June2018. (2018).

  • Sulc, M. & Matas, J. Texture-based leaf identification, computer vision – ECCV 2014 workshops, springer international publishing, midtown manhattan, New York City, pp. 185–200, (2015).

  • Zhang, C., Zhou, P., Li, C. & Liu, L. 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, December (2015).

  • Pawara, P., Okafor, E., Schomaker, L. & Wiering, M. Data augmentation for plant classification, advanced concepts for intelligent vision systems, springer international publishing, midtown manhattan, New York City, pp. 615–626, (2017).

  • Barre, B. C., St´ over, K. F. & Mu¨ller Steinhage,¨ leafnet: A computer vision system for automatic plant species identification. Ecol. Inf. 40, 50–56 (2017).

    Article 

    Google Scholar 

  • Anubha Pearline, S., Sathiesh Kumar, V. & Harini, S. A study on plant recognition using conventional image processing and deep learning approaches, Journal of Intelligent & Fuzzy Systems, vol. 36, no. 3, p. 2004. (1997).

  • Blesslin Elizabeth, C. P. & Baulkani, S. Novel network for medicinal leaves identification. IETE J. Res., 69(4), 1772–1782 https://doi.org/10.1080/03772063.2021.2016504, (2022).

  • Bodhwani, V., Acharjya, D. P. & Bodhwani, U. Deep residual networks for plant identification. In Proceedings of the International Conference on Pervasive Computing Advances and Applications, Jaipur, India, 8–10 January ; pp. 186–194 (2019).

  • Tiwari, S. A comparative study of deep learning models with handcraft features and non-handcraft features for automatic plant species identification. Int. J. Agric. Environ. Inf. Syst. 11, 44–57 (2020).

    Article 

    Google Scholar 

  • Yang, K., Zhong, W. & Li, F. Leaf segmentation and classification with a complicated background using deep learning. Agronomy 10, 1721 (2020).

    Article 

    Google Scholar 

  • Villaruz, J. A. Deep convolutional neural network feature extraction for berry trees classification. J. Adv. Inf. Technol. 12, 226–233 (2021).

    Google Scholar 

  • Pushpa, B. R. & Rani, N. S. DIMPSAR: Dataset for Indian medicinal plant species analysis and recognition. Data in Brief, 49, p.109388. (2023).

  • Devi, K., Gupta, P., Grover, D. & Dhindsa, A. An effective feature extraction approach for iris recognition system, Indian J. Sci. Technol., vol. 9, no. December, pp. 1–5, (2016).

  • Bansal, A., Agarwal, R. & Sharma, R. K. Statistical feature extraction based Iris recognition system. Sadhana 41 (5), 507–518 (2016).

    Article 
    MathSciNet 

    Google Scholar 

  • Harsha, R. & Ramesha, K. DWT based feature extraction for Iris recognition. Int. J. Adv. Reserach Comput. Commun. Eng. 4 (5), 300–306 (2015).

    Google Scholar 

  • Kerim, S. J. & Mohammed A. A New Iris feature extraction and pattern matching based on statistical measurement, International Journal of Emerging Trends & Technology in Computer Science 3, 5, 226–231, (2014).



  • Source link

    Leave a Reply

    Your email address will not be published. Required fields are marked *