Enhancing Feature Extraction in Plant Image Analysis through a Multilayer Hybrid DCNN
Keywords:
Multilayer Hybrid Neural Network, Plant Image Analysis, Feature Extraction, Convolutional Neural Network, Activation FunctionsAbstract
Plant image analysis plays a pivotal role across diverse domains such as agriculture, botany, and environmental monitoring. The accurate identification and classification of plant species from images are fundamental for tasks like biodiversity assessment, disease detection, and crop management. This study introduced an innovative approach to plant image analysis through the Multilayer Hybrid Deep Convolutional Neural Network (MHDCNN), a novel architecture that synergizes the strengths of CNN and LSTM. The objective is to amplify feature extraction capabilities and elevate classification accuracy, thereby achieving exceptional precision in plant species identification Various activation functions like “Tanh”, “ReLU”, “softmax”, and “sigmoid” are integrated into the architecture to impact learning dynamics. Extensive experiments using a diverse plant image dataset validate the approach. The MHDCNN achieves an impressive 99.8% classification accuracy, highlighting its effectiveness in handling complex plant images. By blending CNN + LSTM architectures and carefully selecting activation functions for enhanced feature extraction, this research advances plant image analysis techniques. This novel approach not only contributes to deep learning (DL) in plant biology but also paves the way for future innovations in image-based plant analysis methods.
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H. M. Abdullah et al., “Present and future scopes and challenges of plant pest and disease (P&D) monitoring: Remote sensing, image processing, and artificial intelligence perspectives,” Remote Sens. Appl. Soc. Environ., vol. 32, no. June, p. 100996, 2023, doi: 10.1016/j.rsase.2023.100996.
I. Attri, L. K. Awasthi, T. P. Sharma, and P. Rathee, “A review of DL techniques used in agriculture,” Ecol. Inform., vol. 77, no. July, p. 102217, 2023, doi: 10.1016/j.ecoinf.2023.102217.
T. R. Chhetri, A. Hohenegger, A. Fensel, M. A. Kasali, and A. A. Adekunle, “Towards improving prediction accuracy and user-level explainability using DL and knowledge graphs: A study on cassava disease,” Expert Syst. Appl., vol. 233, no. June, p. 120955, 2023, doi: 10.1016/j.eswa.2023.120955.
J. Kotwal, D. R. Kashyap, and D. S. Pathan, “Agricultural plant diseases identification: From traditional approach to DL,” Mater. Today Proc., vol. 80, pp. 344–356, 2023, doi: 10.1016/j.matpr.2023.02.370.
R. Sujatha, J. M. Chatterjee, N. Z. Jhanjhi, and S. N. Brohi, “Performance of DL vs ML in plant leaf disease detection,” Microprocess. Microsyst., vol. 80, no. December 2020, p. 103615, 2021, doi: 10.1016/j.micpro.2020.103615.
C. Sarkar, D. Gupta, U. Gupta, and B. B. Hazarika, “Leaf disease detection using ML and DL: Review and challenges,” Appl. Soft Comput., vol. 145, p. 110534, 2023, doi: 10.1016/j.asoc.2023.110534.
V. Khetani, Y. Gandhi, S. Bhattacharya, S. N. Ajani, and S. Limkar, “Intelligent Systems And Applications In Engineering Cross-Domain Analysis of ML and DL : Evaluating their Impact in Diverse Domains,” vol. 11, pp. 253–262, 2023.
S. Azimi, T. Kaur, and T. K. Gandhi, “A DL approach to measure stress level in plants due to Nitrogen deficiency,” Meas. J. Int. Meas. Confed., vol. 173, no. June 2020, 2021, doi: 10.1016/j.measurement.2020.108650.
A. A. Salamai, “Ecological Informatics Enhancing mango disease diagnosis through eco-informatics : A DL approach,” Ecol. Inform., vol. 77, no. March, p. 102216, 2023, doi: 10.1016/j.ecoinf.2023.102216.
N. A. Zabidi et al., “Vision transformer meets convolutional neural network for plant disease classification,” Int. J. Biol. Macromol., vol. 2, no. 2, pp. 33–47, 2022, doi: 10.1016/j.ecoinf.2023.102245.
J. Zhang, C. Ye, and B. Wang, “Tomato Plant Leaf Disease Segmentation and Multiclass Disease Detection Using Hybrid Optimization enabled DL,” J. Pharm. Biomed. Anal., p. 114342, 2021, doi: 10.1016/j.jbiotec.2023.07.011.
T. Daniya and S. Vigneshwari, “Rider Water Wave-enabled DL for disease detection in rice plant,” Adv. Eng. Softw., vol. 182, no. February, p. 103472, 2023, doi: 10.1016/j.advengsoft.2023.103472.
S. R. G. Reddy, G. P. S. Varma, and R. L. Davuluri, “Resnet-based modified red deer optimization with DLCNN classifier for plant disease identification and classification,” Comput. Electr. Eng., vol. 105, no. May 2022, p. 108492, 2023, doi: 10.1016/j.compeleceng.2022.108492.
S. Alsubai, A. K. Dutta, A. H. Alkhayyat, M. M. Jaber, A. H. Abbas, and A. Kumar, “Hybrid DL with improved Salp swarm optimization based multi-class grape disease classification model,” Comput. Electr. Eng., vol. 108, no. December 2022, p. 108733, 2023, doi: 10.1016/j.compeleceng.2023.108733.
M. Sharma and V. Jindal, “Approximation techniques for apple disease detection and prediction using computer enabled technologies: A review,” Remote Sens. Appl. Soc. Environ., p. 101038, 2023, doi: 10.1016/j.rsase.2023.101038.
A. Pal and V. Kumar, “AgriDet: Plant Leaf Disease severity classification using agriculture detection framework,” Eng. Appl. Artif. Intell., vol. 119, no. May 2022, p. 105754, 2023, doi: 10.1016/j.engappai.2022.105754.
Y. Kaya and E. Gürsoy, “A novel multi-head CNN design to identify plant diseases using the fusion of RGB images,” Ecol. Inform., vol. 75, no. July 2022, p. 101998, 2023, doi: 10.1016/j.ecoinf.2023.101998.
A. Pandey and K. Jain, “A robust deep attention dense convolutional neural network for plant leaf disease identification and classification from smart phone captured real world images,” Ecol. Inform., vol. 70, no. June, p. 101725, 2022, doi: 10.1016/j.ecoinf.2022.101725.
D. Barhate, S. Pathak, and A. K. Dubey, “Hyperparameter-tuned batch-updated stochastic gradient descent: Plant species identification by using hybrid DL,” Ecol. Inform., vol. 75, no. March, p. 102094, 2023, doi: 10.1016/j.ecoinf.2023.102094.
S. Ashwinkumar, S. Rajagopal, V. Manimaran, and B. Jegajothi, “Automated plant leaf disease detection and classification using optimal MobileNet based convolutional neural networks,” Mater. Today Proc., vol. 51, pp. 480–487, 2021, doi: 10.1016/j.matpr.2021.05.584.
A. Ali, “PlantVillage Dataset | Kaggle,” Kaggle. 2019, [Online]. Available: https://www.kaggle.com/datasets/abdallahalidev/plantvillage-dataset.
Dr. Bhushan Bandre. (2013). Design and Analysis of Low Power Energy Efficient Braun Multiplier. International Journal of New Practices in Management and Engineering, 2(01), 08 - 16. Retrieved from http://ijnpme.org/index.php/IJNPME/article/view/12
M, T. ., & K, P. . (2023). An Enhanced Expectation Maximization Text Document Clustering Algorithm for E-Content Analysis. International Journal on Recent and Innovation Trends in Computing and Communication, 11(1), 12–19. https://doi.org/10.17762/ijritcc.v11i1.5982
Anand, R., Ahamad, S., Veeraiah, V., Janardan, S. K., Dhabliya, D., Sindhwani, N., & Gupta, A. (2023). Optimizing 6G wireless network security for effective communication. Innovative smart materials used in wireless communication technology (pp. 1-20) doi:10.4018/978-1-6684-7000-8.ch001 Retrieved from www.scopus.com
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