• Deepti Barhate Amity School of Engineering & Technology, Department of Computer Science & Engineering, Amity University Rajasthan, Jaipur, India
  • Sunil Pathak Amity School of Engineering & Technology, Department of Computer Science & Engineering, Amity University Rajasthan, Jaipur, India
  • Ashutosh Kumar Dubey Chitkara University School of Engineering and Technology, Chitkara University, Himachal Pradesh, India






Plants play a crucial role in supporting all forms of life on Earth, not just humans but every living organism. Understanding the diverse range of plant species that surround us is essential due to their significance in various aspects of human life, including agriculture, the environment, medicine, cosmetics, and more. Advancements in machine learning and computer vision algorithms have opened possibilities for identifying different types of plant species, both within and across classes. Plant species detection typically involves several steps, such as image acquisition, feature extraction, categorization, and pre-processing. In this study, three datasets—namely Flavia, Swedish, and the intelligent computing laboratory (ICL) dataset—were chosen for experimentation purposes. For feature extraction, three different models were employed: k-nearest neighbour (KNN), naive Bayes (NB), and the visual geometry group (VGG)-16 model. These models were used to extract distinctive features such as shape, texture, venation, and margin from the plant images. A multiclass classification task was conducted to categorize the plant species. Among the models tested, the VGG-16 model consistently demonstrated superior performance in terms of accuracy. Specifically, when using the VGG-16 model, the obtained accuracies were 96.68% for the Flavia dataset, 97.65% for the Swedish dataset, and 96.11% for the ICL dataset.


Borowiec, M. L., Dikow, R. B., Frandsen, P. B., McKeeken, A., Valentini, G., & White, A. E. 2022. Deep Learning As A Tool For Ecology And Evolution. Methods in Ecology and Evolution, 13(8): 1640-1660. DOI: https://doi.org/10.1111/2041-210X.13901

Fadzil, A.F.A., Abd Khalid, N.E. and Ibrahim, S.2021. Amplification of Pixels In Medical Image Data For Segmentation Via Deep Learning Object-Oriented Approach. International Journal of Advanced Technology and Engineering Exploration, 8(74): 82. DOI:10.19101/IJATEE.2020.S1762117

Kaur, A. 2018. A Review On Image Enhancement With Deep Learning Approach. ACCENTS Transactions on Image Processing and Computer Vision, 4(11): 16. DOI: http://dx.doi.org/10.19101/TIPCV.2018.411002

Wäldchen J, Mäder P. 2018.Machine Learning For Image Based Species Identification. Methods in Ecology and Evolution. 9(11): 2216-25. DOI: https://doi.org/10.1111/2041-210X.13075

Pearline SA, Kumar VS. 2022. Performance Analysis Of Real-Time Plant Species Recognition Using Bilateral Network Combined With Machine Learning Classifier. Ecological Informatics. Mar 1(67): 101492. DOI:10.3233/JIFS-169911

Sohn SI, Oh YJ, Pandian S, Lee YH, Zaukuu JL, Kang HJ, Ryu TH, Cho WS, Cho YS, Shin EK. 2021. Identification Of Amaranthus Species Using Visible-Near-Infrared (Vis-NIR) Spectroscopy And Machine Learning Methods. Remote Sensing. 13(20): 4149. DOI: https://doi.org/10.3390/rs13204149

Nemade, V., Pathak, S., Dubey, A.K. and Barhate, D.2022. A Review And Computational Analysis Of Breast Cancer Using Different Machine Learning Techniques. International Journal of Emerging Technology and Advanced Engineering, 12(3): 111-118. DOI:10.46338/ijetae0322_13

Sun, X., & Shi, Y.2023. The Image Recognition of Urban Greening Tree Species Based on Deep Learning and CAMP-MKNet Model. Urban Forestry & Urban Greening, 127970. DOI: https://doi.org/10.1016/j.ufug.2023.127970

Malik, O. A., Ismail, N., Hussein, B. R., & Yahya, U.2022. Automated Real-Time Identification of Medicinal Plants Species in Natural Environment Using Deep Learning Models—A Case Study from Borneo Region. Plants, 11(15): 1952. DOI: https://doi.org/10.3390/plants11151952

Kaya A, Keceli AS, Catal C, Yalic HY, Temucin H, Tekinerdogan B.2019. Analysis Of Transfer Learning for Deep Neural Network Based Plant Classification Models. Computers And Electronics In Agriculture. 158: 20-9. DOI: 10.1016/j.compag.2019.01.041

Kamilaris A, Prenafeta-Boldú FX.2018. Deep Learning In Agriculture: A Survey. Computers And Electronics In Agriculture. 147: 70-90. DOI: 10.1016/j.compag.2018.02.016

Wagle SA, Harikrishnan R, Ali SH, Faseehuddin M.2021. Classification Of Plant Leaves Using New Compact Convolutional Neural Network Models. Plants. 11(1):24. DOI: 10.3390/plants11010024

Shambhu, S., Koundal, D., Das, P., & Sharma, C.2021. Binary Classification Of Covid-19 Ct Images Using Cnn: Covid Diagnosis Using Ct. International Journal of E-Health and Medical Communications (IJEHMC), 13(2): 1-13. DOI: 10.4018/IJEHMC.20220701.oa4

Thakur, B., Kumar, N., & Gupta, G.2022. Machine Learning Techniques with ANOVA For the Prediction of Breast Cancer. International Journal of Advanced Technology and Engineering Exploration, 9(87): 232. DOI:10.19101/IJATEE.2021.874555

Chand, L., Dhiman, A.S. and Singh, S.2023. Detection Of Whitefly Pests in Crops Employing Image Enhancement and Machine Learning. International Journal of Advanced Technology and Engineering Exploration. 10(102): 569-589. DOI:10.19101/IJATEE.2022.10100289

Naeem S, Ali A, Chesneau C, Tahir MH, Jamal F, Sherwani RA, Ul Hassan M.2021. The Classification of Medicinal Plant Leaves Based on Multispectral and Texture Feature Using Machine Learning Approach. Agronomy. 11(2): 263. DOI: 10.3390/agronomy11020263

Sabarinathan C, Hota A, Raj A, Dubey VK, Ethirajulu V.2018.Medicinal Plant Leaf Recognition and Show Medicinal Uses Using Convolutional Neural Network. International Journal of Global Engineering. 1(3): 120-7.

Picek, L., Šulc, M., Patel, Y., & Matas, J.2022. Plant Recognition By AI: Deep Neural Nets, Transformers, And Knn In Deep Embeddings. Frontiers in Plant Science, 2788. DOI: https://doi.org/10.3389/fpls.2022.787527

Mata-Montero, E., & Carranza-Rojas, J. 2016. Automated Plant Species Identification: Challenges And Opportunities. In ICT for Promoting Human Development and Protecting the Environment: 6th IFIP World Information Technology Forum, WITFOR 2016, San José, Costa Rica, September 12-14, Proceedings 6: 26-36. Springer International Publishing. DOI:10.1007/978-3-319-44447-5_3

Zhang, S., Huang, W., Huang, Y. A., & Zhang, C.2020. Plant Species Recognition Methods Using Leaf Image: Overview. Neurocomputing, 408: 246-272. DOI: https://doi.org/10.1016/j.neucom.2019.09.113

Bakhshipour A, Jafari A.2018 Feb. Evaluation Of Support Vector Machine And Artificial Neural Networks In Weed Detection Using Shape Features. Computers and Electronics in Agriculture. 145: 153-60.DOI: 10.1016/j.compag.2017.12.032

Soltani S, Feilhauer H, Duker R, Kattenborn T.2022. Transfer Learning from Citizen Science Photographs Enables Plant Species Identification In Uavs Imagery. ISPRS Open Journal of Photogrammetry and Remote Sensing. 100016. DOI: https://doi.org/10.1016/j.ophoto.2022.100016

Qian W, Huang Y, Liu Q, Fan W, Sun Z, Dong H, Wan F, Qiao X.2020. UAV And A Deep Convolutional Neural Network For Monitoring Invasive Alien Plants In The Wild. Computers and Electronics in Agriculture. 174: 105519. DOI: https://doi.org/10.1016/j.compag.2020.105519

Thanikkal JG, Dubey AK, Thomas MT.2017. Whether Color, Shape and Texture Of Leaves Are The Key Features For Image Processing Based Plant Recognition? An Analysis! In 2017 Recent Developments in Control, Automation & Power Engineering (RDCAPE) Oct 26 2017 404-409. IEEE. DOI: https://doi.org/10.18280/ts.370103

Mouine S, Yahiaoui I, Verroust-Blondet A.2019.Plant Species Recognition Using Spatial Correlation Between the Leaf Margin And The Leaf Salient Points. In IEEE international conference on image processing 2019, 1466-1470. IEEE. DOI: https://hal.inria.fr/hal-00865162

Ren S, He K, Girshick R, Sun J.2015.Faster R-Cnn: Towards Real-Time Object Detection with Region Proposal Networks. Advances in neural information processing systems. 28. DOI: arXiv:1506.01497v3 [cs.CV]

Jamil N, Hussin NA, Nordin S, Awang K.2015.Automatic Plant Identification: Is Shape the Key Feature? Procedia Computer Science. 76:436-42. DOI: 10.1016/j.procs.2015.12.287.

Saleem, M.H.,Potgieter, J.,Arif, K.M. 2019. Plant Disease Detection and Classification by Deep Learning. Plants. 8: 468. DOI: 10.3390/plants8110468.

Canziani, A., Paszke, A., Culurciello, E.2017. An Analysis of Deep Neural Network Models for Practical Applications. arXiv 2017, arXiv:1605.07678. DOI: https://doi.org/10.48550/arXiv.1605.07678

He, K., Zhang, X., Ren, S., Sun, J.2016. Deep Residual Learning For Image Recognition. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA, 27–30 June 2016. DOI: 1http://image-net.org/challenges/LSVRC/2015

Krizhevsky, A., Sutskever, I. 2012; Hinton, G.E. Imagenet classification with deep convolutional neural networks. Communications of the ACM. 60: 1097–1105. DOI: https://doi.org/10.1145/3065386

Atienza, R. 2018. Advanced Deep Learning with Keras: Apply Deep Learning Techniques, Autoencoders, GANs, Variational Autoen-Coders, Deep Reinforcement Learning, Policy Gradients, and More, Packt Publishing Ltd.: Birmingham, UK, 2018.

Kan HX, Jin L, Zhou FL. 2017. Classification Of Medicinal Plant Leaf Image Based On Multi-Feature Extraction. Pattern Recognition and Image Analysis. 27(3): 581-7. DOI: https://doi.org/10.1134/S105466181703018X

Lee SH, Chan CS, Mayo SJ, Remagnino P.2017. How Deep Learning Extracts And Learns Leaf Features For Plant Classification. Pattern Recognition. 71: 1-3. DOI: http://dx.doi.org/10.1016/j.patcog.2017.05.015

Wei Tan J, Chang SW, Abdul-Kareem S, Yap HJ, Yong KT. 2018. Deep Learning For Plant Species Classification Using Leaf Vein Morphometric. IEEE/ACM transactions on computational biology and bioinformatics. 17(1): 82-90. DOI: 10.1109/TCBB.2018.2848653

Milioto A, Lottes P, Stachniss C. 2018. Real-Time Semantic Segmentation of Crop And Weed For Precision Agriculture Robots Leveraging Background Knowledge In Cnns. In 2018 IEEE international conference on robotics and automation (ICRA) May 21. 2229-2235. DOI: https://doi.org/10.48550/arXiv.1709.06764

Charters J, Wang Z, Chi Z, Tsoi AC, Feng DD.2014. EAGLE: A Novel Descriptor for Identifying Plant Species Using Leaf Lamina Vascular Features. In IEEE international conference on multimedia and expo workshops (ICMEW) 2014 Jul 14. 1-6. DOI: 10.1109/ICMEW.2014.6890557.

Chaudhury A, Barron JL. 2018. Plant Species Identification from Occluded Leaf Images. IEEE/ACM transactions on computational biology and bioinformatics. 17(3): 1042-55. DOI: 10.1109/TCBB.2018.2873611

Pereira CS, Morais R, Reis MJ. 2019.Deep Learning Techniques for Grape Plant Species Identification In Natural Images. Sensors. 19(22): 4850. DOI: 10.3390/s19224850

Pushpanathan K, Hanafi M, Mashohor S, Fazlil Ilahi WF. 2020.Machine Learning in Medicinal Plants Recognition: A Review. Artificial Intelligence Review. 1-23. DOI: 10.1007/s10462-020-09847-0

Yang C, Wei H. 2020. Plant Species Recognition Using Triangle-Distance Representation. IEEE Access. 7: 178108-20. DOI: 10.1109/ACCESS.2019.2958416

Sobha PM, Thomas PA. 2019. Deep Learning for Plant Species Classification Survey. In international conference on advances in computing, communication and control 2019. 1-6. DOI: 10.1109/ICAC347590.2019.9036796

Hati AJ, Singh RR. 2021. Artificial Intelligence In Smart Farms: Plant Phenotyping For Species Recognition And Health Condition Identification Using Deep Learning. AI. 2(2): 274-89. DOI: 10.1109/ICAC347590.2019.9036796

Ibrahim NM, Gabr DG, Rahman AU, Dash S, Nayyar A. 2022. A Deep Learning Approach To Intelligent Fruit Identification And Family Classification. Multimedia Tools and Applications. 1-6. DOI: https://doi.org/10.1007/s11042-022-12942-9

Barhate D, Pathak S, Dubey AK, Nemade V. 2022. Cohort Study On Recognition Of Plant Species Using Deep Learning Methods. In Journal of Physics: Conference Series. 2273(1): 012006. IOP Publishing. DOI: 10.1088/1742-6596/2273/1/012006

Abdallah HB, Henry CJ, Ramanna S. 2022. Plant Species Recognition With Optimized 3d Polynomial Neural Networks And Variably Overlapping Time-Coherent Sliding Window. arXiv preprint arXiv:2203.02611. 2022 Mar 4. DOI: https://doi.org/10.48550/arXiv.2203.02611

Xiong J, Yu D, Liu S, Shu L, Wang X, Liu Z. 2021. A Review Of Plant Phenotypic Image Recognition Technology Based On Deep Learning. Electronics. 10(1): 81.

Zhang, Y., Cui, J., Wang, Z., Kang, J., & Min, Y.2020. Leaf Image Recognition Based On Bag Of Features. Applied Sciences, 10(15): 5177. DOI: https://doi.org/10.3390/app10155177

Wang, B., Gao, Y., Sun, C., Blumenstein, M., & La Salle, J. 2017. Can Walking And Measuring Along Chord Bunches Better Describe Leaf Shapes? In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 6119-6128. DOI: 10.1109/CVPR.2017.221







How to Cite

A DEEP LEARNING METHODOLOGY FOR PLANT SPECIES RECOGNITION USING MORPHOLOGY OF LEAVES. (2023). ASEAN Engineering Journal, 13(4), 95-102. https://doi.org/10.11113/aej.v13.19461