Plant Disease Detection Using CNN with The Optimization Called Beluga Whale Optimization Mechanism
DOI:
https://doi.org/10.22399/ijcesen.697Keywords:
Plant Disease Detection, Convolutional Neural Networks (CNN), Deep Learning, Beluga whale optimizationAbstract
Plant disease detection is critical for ensuring agricultural productivity. Early and accurate identification of plant diseases can help in the timely application of remedies, reducing yield loss and improving crop quality. This paper presents a deep learning(DL) approach using Convolutional Neural Networks (CNN) for plant disease detection, combined with an advanced optimization technique named the Beluga Whale Optimization Mechanism (BWOM). The CNN is implemented to extract and features from plant images, providing a robust model capable of differentiating between healthy and diseased plants. The BWOM is utilized to optimize the CNN's hyper parameters and weights, enhancing model accuracy and efficiency by reducing overfitting and improving generalization. The BWOM mimics the social behaviour and echolocation techniques of beluga whales to navigate and optimize solution spaces effectively. By iterating through population-based exploration and exploitation phases, BWOM provides a balanced search mechanism to fine-tune CNN parameters. Further the results demonstrate the effectiveness of combining CNN with BWOM in achieving high accuracy rates for plant disease classification.
References
P.W. Bidwell (1921). The agricultural revolution in new England, Am. Hist. Rev., 26(4);683-702.
Z.B. Husin, A.Y.B.M. Shakaff, A.H.B.A. Aziz, R.B.S.M (2012). Farook, Feasibility study on plant chili disease detection using image processing techniques, 2012 Third International Conference on Intelligent Systems Modelling and Simulation, IEEE 291-296
Kalpana, P., Anandan, R. (2023). A capsule attention network for plant disease classification. Traitement du Signal, 40(5);2051-2062. https://doi.org/10.18280/ts.400523.
Kalpana, P., Anandan, R., Hussien, A.G. et al. (2024). Plant disease recognition using residual convolutional enlightened Swin transformer networks. Sci Rep 14;8660. https://doi.org/10.1038/s41598-024-56393-8.
P. Kalpana, S. Sridevi (2022). A survey on recognition and classification of paddy leaf diseases using image processing and machine learning techniques. AIP Conf. Proc. 2463(1);020009. https://doi.org/10.1063/5.0080336.
Mohsin Kabir, M.; Quwsar Ohi, A.; Mridha, M.F. (2023). A Multi-plant disease diagnosis method using convolutional neural network. arXiv 2020, arXiv:2011.05151. Plant Disease Detection Using Deep Learning 15(6);49
Aldhyani, T.H.; Alkahtani, H.; Eunice, R.J.; Hemanth, D.J. (2022). Leaf Pathology Detection in Potato and Pepper Bell Plant using Convolutional Neural Networks. In Proceedings of the 2022 7th International Conference on Communication and Electronics Systems (ICCES), Coimbatore, India, pp. 1289–1294.
Prodeep, A.R.; Hoque, A.M.; Kabir, M.M.; Rahman, M.S.; Mridha, M.F. (2022). Plant Disease Identification from Leaf Images using Deep CNN’s EfficientNet. In Proceedings of the 2022 International Conference on Decision Aid Sciences and Applications (DASA), Chiangrai, Thailand, pp. 523–527.
Kalpana, P.,Y. Chanti, R. G, R. D and P. K. Pareek, (2023). SE-Resnet152 Model: Early Corn Leaf Disease Identification and Classification using Feature Based Transfer Learning Technique, 2023 International Conference on Evolutionary Algorithms and Soft Computing Techniques (EASCT), Bengaluru, India, pp. 1-6, https://doi.org/10.1109/EASCT59475.2023.10392328.
Abbas, A.; Jain, S.; Gour, M.; Vankudothu, S. (2021) Tomato plant disease detection using transfer learning with C-GAN synthetic images. Comput. Electron. Agric. 187;106279.
Anh, P.T.; Duc, H.T.M. (2021). A Benchmark of Deep Learning Models for Multi-leaf Diseases for Edge Devices. In Proceedings of the 2021 International Conference on Advanced Technologies for Communications (ATC), Ho Chi Minh City, Vietnam, 14–16 October 2021; pp. 318–323.
Astani, M.; Hasheminejad, M.; Vaghefi, M. (2022). A diverse ensemble classifier for tomato disease recognition. Comput. Electron. Agric. 198;107054.
Prodeep, A.R.; Hoque, A.M.; Kabir, M.M.; Rahman, M.S.; Mridha, M.F. (2022). Plant Disease Identification from Leaf Images using Deep CNN’s EfficientNet. In Proceedings of the 2022 International Conference on Decision Aid Sciences and Applications (DASA), Chiangrai, Thailand, 23–25 March 2022; pp. 523–527.
Enkvetchakul, P.; Surinta, O. (2022). Effective Data Augmentation and Training Techniques for Improving Deep Learning in Plant Leaf Disease Recognition. Appl. Sci. Eng. Prog. 15;3810.
Trivedi, N.K.; Gautam, V.; Anand, A.; Aljahdali, H.M.; Villar, S.G.; Anand, D.; Goyal, N.; Kadry, S. (2021). Early Detection and Classification of Tomato Leaf Disease Using High-Performance Deep Neural Network. Sensors 21;7987.
Agarwal, M.; Gupta, S.K.; Biswas, K.K. (2020). Development of Efficient CNN model for Tomato crop disease identification. Sustain. Comput. Inform. Syst. 28;100407. https://doi.org/10.1016/j.suscom.2020.100407
Fuentes, A.; Yoon, S.; Kim, S.C.; Park, D.S. (2017). A Robust Deep-Learning-Based Detector for Real-Time Tomato Plant Diseases and Pests Recognition. Sensors 17(9);2022. https://doi.org/10.3390/s17092022
Machireddy, C., & Chella, S. (2024). Reconfigurable Acceleration of Neural Networks: A Comprehensive Study of FPGA-based Systems. International Journal of Computational and Experimental Science and Engineering, 10(4);1007-1014. https://doi.org/10.22399/ijcesen.559
Nagalapuram, J., & S. Samundeeswari. (2024). Genetic-Based Neural Network for Enhanced Soil Texture Analysis: Integrating Soil Sensor Data for Optimized Agricultural Management. International Journal of Computational and Experimental Science and Engineering, 10(4);962-970. https://doi.org/10.22399/ijcesen.572
Priti Parag Gaikwad, & Mithra Venkatesan. (2024). KWHO-CNN: A Hybrid Metaheuristic Algorithm Based Optimzed Attention-Driven CNN for Automatic Clinical Depression Recognition. International Journal of Computational and Experimental Science and Engineering, 10(3);491-506. https://doi.org/10.22399/ijcesen.359
Ay, S. (2024). Vehicle Detection And Vehicle Tracking Applications On Traffic Video Surveillance Systems: A systematic literature review. International Journal of Computational and Experimental Science and Engineering, 10(4);1059-1068. https://doi.org/10.22399/ijcesen.629
S.D.Govardhan, Pushpavalli, R., Tatiraju.V.Rajani Kanth, & Ponmurugan Panneer Selvam. (2024). Advanced Computational Intelligence Techniques for Real-Time Decision-Making in Autonomous Systems. International Journal of Computational and Experimental Science and Engineering, 10(4);928-937. https://doi.org/10.22399/ijcesen.591
Serap Çatli dinç, akmansu, m., bora, h., üçgül, a., çetin, b. e., erpolat, p., … şentürk, e. (2024). Evaluation of a Clinical Acceptability of Deep Learning-Based Autocontouring: An Example of The Use of Artificial Intelligence in Prostate Radiotherapy. International Journal of Computational and Experimental Science and Engineering, 10(4);1181-1186. https://doi.org/10.22399/ijcesen.386
Rakesh Jha, & Singh, M. K. (2024). Analysing the Impact of Social Influence on Electric Vehicle Adoption: A Deep Learning-Based Simulation Study in Jharkhand, India. International Journal of Computational and Experimental Science and Engineering, 10(4);639-646. https://doi.org/10.22399/ijcesen.371
P, P., P, D., R, V., A, Y., & Natarajan, V. P. (2024). Chronic Lower Respiratory Diseases detection based on Deep Recursive Convolutional Neural Network. International Journal of Computational and Experimental Science and Engineering, 10(4);744-752. https://doi.org/10.22399/ijcesen.513
Suneetha Madduluri, & T. Kishorekumar. (2024). Multimodal Biometric Authentication System for Military Weapon Access: Face and ECG Authentication. International Journal of Computational and Experimental Science and Engineering, 10(4);952-961. https://doi.org/10.22399/ijcesen.565
PATHAPATI, S., N. J. NALINI, & Mahesh GADIRAJU. (2024). Comparative Evaluation of EEG signals for Mild Cognitive Impairment using Scalograms and Spectrograms with Deep Learning Models. International Journal of Computational and Experimental Science and Engineering, 10(4);859-866. https://doi.org/10.22399/ijcesen.534
J Jeysudha, K. Deiwakumari, C.A. Arun, R. Pushpavalli, Ponmurugan Panneer Selvam, & S.D. Govardhan. (2024). Hybrid Computational Intelligence Models for Robust Pattern Recognition and Data Analysis . International Journal of Computational and Experimental Science and Engineering, 10(4);1032-1040. https://doi.org/10.22399/ijcesen.624
U. S. Pavitha, S. Nikhila, & Mohan, M. (2024). Hybrid Deep Learning Based Model for Removing Grid-Line Artifacts from Radiographical Images. International Journal of Computational and Experimental Science and Engineering, 10(4);763-774. https://doi.org/10.22399/ijcesen.514
P. Jagdish Kumar, & S. Neduncheliyan. (2024). A novel optimized deep learning based intrusion detection framework for an IoT networks. International Journal of Computational and Experimental Science and Engineering, 10(4);1169-1870. https://doi.org/10.22399/ijcesen.597
Rama Lakshmi BOYAPATI, & Radhika YALAVARTHI. (2024). RESNET-53 for Extraction of Alzheimer’s Features Using Enhanced Learning Models. International Journal of Computational and Experimental Science and Engineering, 10(4);879-889. https://doi.org/10.22399/ijcesen.519
M, V., V, J., K, A., Kalakoti, G., & Nithila, E. (2024). Explainable AI for Transparent MRI Segmentation: Deep Learning and Visual Attribution in Clinical Decision Support. International Journal of Computational and Experimental Science and Engineering, 10(4);575-584. https://doi.org/10.22399/ijcesen.479
Venkatraman Umbalacheri Ramasamy. (2024). Overview of Anomaly Detection Techniques across Different Domains: A Systematic Review. International Journal of Computational and Experimental Science and Engineering, 10(4);898-910. https://doi.org/10.22399/ijcesen.522
Agnihotri, A., & Kohli, N. (2024). A novel lightweight deep learning model based on SqueezeNet architecture for viral lung disease classification in X-ray and CT images. International Journal of Computational and Experimental Science and Engineering, 10(4);592-613. https://doi.org/10.22399/ijcesen.425
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2024 International Journal of Computational and Experimental Science and Engineering
This work is licensed under a Creative Commons Attribution 4.0 International License.
