An Intelligent Framework for Herb Leaves Classification
Keywords:
Ant Colony Optimization · Pre-processing · Feature Extraction · Leaf Type Classification · Data InitializationAbstract
The grouping result is low for picture handling in the current strategies, so the proposed model has been created to further develop the characterization results. Orders of the leaf were done in light of the removed highlights. Thus, a novel Ant Colony found Gradient Boosting Model (ACbGBM) is developed with required processing parameters: filtering and classification functions to improve the classification performance and make the system more robust and efficient. In this model, better classification results were obtained and are shown in the result section. Here, leaf data is collected and initialized to the system. In this manner, pre-handling is finished over the introduced informational collection after highlight extraction. The negligible elements are taken out in highlight extraction, and the entire significance highlights are picked. The extracted features' fitness is contrasted with the fitness of ants. After that, classification is done over the extracted features. Besides, a contextual investigation is created to make sense of the functioning technique of the proposed model. As a result, the constructed model was implemented in the MATLAB programme, and the recall, accuracy, precision, error rate, and f-measure were all measured in its presence.
Downloads
References
V.G. Bhujade and V. Sambhe, Role of digital, hyper spectral, and SAR images in detection of plant disease with deep learning network, Multimed Tools Appl. Vol. 81, pp. 33645–33670, 2022. https://doi.org/10.1007/s11042-022-13055-z.
S. Patil and M. Sasikala, Segmentation and identification of medicinal plant through weighted KNN, Multimed Tools Appl. 2022. https://doi.org/10.1007/s11042-022-13201-7.
R. Al-Qurran, M. Al-Ayyoub and A. Shatnawi, Plant classification in the wild: Energy evaluation for deep learning models, Multimed Tools Appl. Vol.81, pp. 230143–30167, 202. https://doi.org/10.1007/s11042-022-12695-5.
M. Salvi, U.R. Acharya, F. Molinari and K.M. Meiburger, The impact of pre-and post-image processing techniques on deep learning frameworks: A comprehensive review for digital pathology image analysis, Comput Biol Med. Vol.128, pp.104-129, 2021. https://doi.org/10.1016/j.compbiomed.2020.104129.
Y. Hou, Q. Li, C. Zhang, G. Lu, et al. The state-of-the-art review on applications of intrusive sensing, image processing techniques, and machine learning methods in pavement monitoring and analysis, Engineering. Vol.7, no.6, pp. 845-856, 2021. https://doi.org/10.1016/j.eng.2020.07.030.
I. Alnazer, P. Bourdon, T. Urruty, et al. Recent advances in medical image processing for the evaluation of chronic kidney disease, Med Image Anal. Vol.69 ,pp. 101-960, 2021. https://doi.org/10.1016/j.media.2021.101960.
S. Bhattacharya, P.K.R. Maddikunta, Q.V. Pham, et al. Deep learning and medical image processing for coronavirus (COVID-19) pandemic: A survey, Sustain Cities Soc. Vol.65,pp. pp.102-589,2021. https://doi.org/10.1016/j.scs.2020.102589.
B.A. Kharazi and A.H. Behzadan, Flood depth mapping in street photos with image processing and deep neural networks, Comput Environ Urban Syst. Vol.88, pp. 101-628, 2021. https://doi.org/10.1016/j.compenvurbsys.2021.101628.
M. Meenu, C. Kurade, B.C. Neelapu, et al. A concise review on food quality assessment using digital image processing. Trends Food Sci Technol. Vol.118, pp. 106-124, 2021. https://doi.org/10.1016/j.tifs.2021.09.014.
R.P.A. Della, C. Miglioli, M. Caglioni, et al. A hierarchical procedure to select intrauterine and extrauterine factors for methodological validation of preterm birth risk estimation, BMC pregnancy and childbirth. Vol.21, no.1, 1-17, 2021. https://doi.org/10.1186/s12884-021-03654-3.
J.T. Morisette, K.A. Duffy, J.F. Weltzin, et al. PS3: The Pheno-Synthesis software suite for integration and analysis of multi-scale, multi-platform phenological data, Ecol Inform. 65 (2021)101-400. https://doi.org/10.1016/j.ecoinf.2021.101400.
K. Ding, K. Ma, S. Wang and E.P. Simoncelli, Comparison of full-reference image quality models for optimization of image processing systems, Int J Comput Vis. Vol.129, no.4,pp. 1258-1281, 2021. https://doi.org/10.1007/s11263-020-01419-7.
M. Zhao, J.K.W. Lee, T. Kjellstrom and W. Cai, Assessment of the economic impact of heat-related labor productivity loss: a systematic review, Clim Change. Vol.167, no.1 pp.1-16, 2021. https://doi.org/10.1007/s10584-021-03160-7.
J.B. Sankey, T.T. Sankey, J. Li, et al. Quantifying plant-soil-nutrient dynamics in rangelands: Fusion of UAV hyperspectral-LiDAR, UAV multispectral-photogrammetry, and ground-based LiDAR-digital photography in a shrub-encroached desert grassland, Remote Sens Environ. Vol.253, pp.112-223, 2021. https://doi.org/10.1016/j.rse.2020.112223.
B. Chen, Y. Sun, Z. Tian, et al. GhGASA10–1 promotes the cell elongation in fiber development through the phytohormones IAA-induced, BMC Plant Biol.Vol.21, no.1 , pp. 1-15, 2021. https://doi.org/10.1186/s12870-021-03230-z.
M. Kummu, M. Heino, M. Taka, O. Varis and D. Viviroli, Climate change risks pushing one-third of global food production outside the safe climatic space, One Earth. Vol.4, no.5, pp. 720-729, 2021. https://doi.org/10.1016/j.oneear.2021.04.017.
Z. de Araujo and E. Smith, Examining English language learners’ learning needs through the lens of algebra curriculum materials, Educ Stud Math. Vol.109, no.1,pp. 65-87, 2022. https://doi.org/10.1007/s10649-021-10081-w.
A. Surendranath and P.V. Ramana, Recycled materials execution through digital image processing, Mater Today: Proc. Vol.46, pp.8795-8801, 2021. https://doi.org/10.1016/j.matpr.2021.04.151.
A. Balasundaram, G. Dilip, S. Ashokkumar, M. Manickam, K. Gurunathan and D. Kothandaraman, Detecting True Medicinal Leaves Among Similar Leaves Using Computer Vision and CNN.
R.S. Chaughule and R.S. Barve, Role of herbal medicines in the treatment of infectious diseases, Vegetos. Vol. 1-1, 2023.
S. Agrawal and S. Yellapragada, Automatic Identification of Medicinal Plants Using Morphological Features and Active Compounds, InSoft Computing: Theories and Applications: Proceedings of SoCTA. Vol.2020, no.1, pp. 719-731, 2022. Springer Singapore.
P.P. Kaur and S. Singh, Classification of herbal plant and comparative analysis of SVM and KNN classifier models on the leaf features using machine learning, InSoft Computing for Intelligent Systems: Proceedings of ICSCIS. Vol. 2020, pp.227-239, 2021. Springer Singapore.
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.196747-64, 2020.
L. Liu, X. Zhan, X. Yang, X. Guan, R. Wu, Z. Wang, Z. Luo, Y. Wang and G. Li, CPSC: Conformal prediction with shrunken centroids for efficient prediction reliability quantification and data augmentation, a case in alternative herbal medicine classification with electronic nose, IEEE Transactions on Instrumentation and Measurement. Vol.71 ,pp.1-1, 2022.
S. Fekri-Ershad, Bark texture classification using improved local ternary patterns and multilayer neural network, Expert Systems with Applications. Vol.158, pp. 113-509, 2020.
G. Mukherjee, B. Tudu and A. Chatterjee, A convolutional neural network-driven computer vision system toward identification of species and maturity stage of medicinal leaves: case studies with Neem, Tulsi and Kalmegh leaves, Soft Comput.Vol. 25, no.22,pp. 14119-14138, 2021. https://doi.org/10.1007/s00500-021-06139-9.
R. Azadnia and K. Kheiralipour, 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. Vol.25 pp.100-327, 2021. https://doi.org/10.1016/j.jarmap.2021.100327.
V.P. Kour and S. Aror, Particle swarm optimization based support vector machine (P-SVM) for the segmentation and classification of plants, IEEE Access. Vol.7, pp. 29374-29385, 2019. DOI: 10.1109/ACCESS.2019.2901900.
S. Sachar and A. Kumar, Survey of feature extraction and classification techniques to identify plant through leaves, Expert Syst Appl. Vol.167,pp.114-181, 2021. https://doi.org/10.1016/j.eswa.2020.114181.
A. Kumar, N. Singh, P. Kumar, A. Vijayvergia and K. Kumar, A novel superpixel based color spatial feature for salient object detection, In2017 conference on information and communication technology (CICT). Vol.1-5, 2017. IEEE.
K. Kumar and D.D. Shrimankar, F-DES: Fast and deep event summarization, IEEE Transactions on Multimedia. Vol.20, no.2, pp. 23-34, 2017.
K. Kumar, D.D. Shrimankar and N. Singh, Eratosthenes sieve based key-frame extraction technique for event summarization in videos, Multimedia Tools and Applications. Vol.77, pp.7383-404, 2018.
K. Kumar, A. Kumar and A. Bahuguna, D-CAD: Deep and crowded anomaly detection, InProceedings of the 7th international conference on computer and communication technology. (2017)100-105.
R.K. Koppanati and K. Kumar, P-MEC: polynomial congruence-based multimedia encryption technique over cloud, IEEE consumer electronics magazine. Vol.10, no.5, pp.41-6, 2020.
A. Atrish, N. Singh, K. Kumar and V. Kumar, An automated hierarchical framework for player recognition in sports image, InProceedings of the international conference on video and image processing. Vol.103-108, 2017.
S. Sharma and K. Kumar, ASL-3DCNN: American sign language recognition technique using 3-D convolutional neural networks, Multimedia Tools and Applications. Vol.80, no.17, pp. 26319-31, 2021.
K. Kumar, Text query based summarized event searching interface system using deep learning over cloud, Multimedia Tools and Applications. Vol.80, no.7, pp.11079-94, 2021.
K. Kumar, D.D. Shrimankar and N. Singh, Key-lectures: keyframes extraction in video lectures, In Machine intelligence and signal analysis. Vol.453-459, 2019. Springer Singapore.
S. Sharma, K. Kumar and N. Singh, Deep eigen space based ASL recognition system, IETE Journal of Research.Vol. 68, no.5, pp.3798-808, 2022.
S. Sharma, K. Kumar and N. Singh, D-FES: Deep facial expression recognition system, In 2017 conference on information and communication technology (CICT). (2017) 1-6. IEEE.
J.G. Thanikkal, A.K. Dubey and M. Thomas, Deep - Morpho Algorithm (DMA) for medicinal leaves features extraction, Multimed Tools Appl. 2023. https://doi.org/10.1007/s11042-023-14567-y.
K.L. Ramanadham and N. Savarimuthu, vCrop: an automated plant disease prediction using deep ensemble framework using real field images, Sādhanā. Vol.47, no.4, pp. 1-10, 2022.
S. Lamba, A. Baliyan and V. Kukreja, A novel GCL hybrid classification model for paddy diseases, Int. j. inf. Tecnol. Vol.15, pp. 1127–1136, 2023. https://doi.org/10.1007/s41870-022-01094-6.
J. Annrose, A cloud-based platform for soybean plant disease classification using archimedes optimization based hybrid deep learning model, Wireless Personal Communications. Vol.122, no.4 pp.995-3017, 20222.
E. Elfatimi, R. Eryigit and L. Elfatimi, Beans leaf diseases classification using mobilenet models, IEEE Access. Vol.10, pp.9471-9482, 2022.
E. Elahi and Z. Khalid, Estimating smart energy inputs packages using hybrid optimisation technique to mitigate environmental emissions of commercial fish farms, Applied Energy.Vol. 326, pp.119-602, 2022.
A. Ksibi, MobiRes-Net: A Hybrid Deep Learning Model for Detecting and Classifying Olive Leaf Diseases, Applied Sciences. Vol.12, no.20, pp.10278, 2022.
A. Tuncer, Cost-optimized hybrid convolutional neural networks for detection of plant leaf diseases, Journal of Ambient Intelligence and Humanized Computing.Vol. 12, no. 8, pp.8625-8636, 2021.
S. Abbas, A.K. Muhammad, A. Majed, T. Usman, A. Ammar, A. Fayadh, M. Arnab and T. Orawit, Crops Leaf Diseases Recognition: A Framework of Optimum Deep Learning. 2023.
P. Sahu, Classification of crop leaf diseases using image to image translation with deep-dream, Multimedia Tools and Applications. Vol. 1-35, 2023.
A.E. Minarno, Leaf Based Plant Species Classification Using Deep Convolutional Neural Network, 2022 10th International Conference on Information and Communication Technology (ICoICT). IEEE. 2022.
B. Wang, Fusing deep learning features of triplet leaf image patterns to boost soybean cultivar identification, Computers and Electronics in Agriculture. Vol.197, pp.106-914, 2022.
J.V.M. Lara and M.A.V. Ricardo, Low-cost image analysis with convolutional neural network for herpes zoster, Biomedical Signal Processing and Control. Vol. 71, pp. 103-250, 2022.
Downloads
Published
How to Cite
Issue
Section
License
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
All papers should be submitted electronically. All submitted manuscripts must be original work that is not under submission at another journal or under consideration for publication in another form, such as a monograph or chapter of a book. Authors of submitted papers are obligated not to submit their paper for publication elsewhere until an editorial decision is rendered on their submission. Further, authors of accepted papers are prohibited from publishing the results in other publications that appear before the paper is published in the Journal unless they receive approval for doing so from the Editor-In-Chief.
IJISAE open access articles are licensed under a Creative Commons Attribution-ShareAlike 4.0 International License. This license lets the audience to give appropriate credit, provide a link to the license, and indicate if changes were made and if they remix, transform, or build upon the material, they must distribute contributions under the same license as the original.
