Plant Disease Detection and Classification Based on Rat Swarm Optimization using Deep Learning Approach
Plant Disease Detection and Classification Based on Rat Swarm Optimization using Deep Learning Approach |
||
 |
 |
|
| © 2023 by IJETT Journal | ||
| Volume-71 Issue-7 |
||
| Year of Publication : 2023 | ||
| Author : D. Raja, M. Karthikeyan |
||
| DOI : 10.14445/22315381/IJETT-V71I7P204 | ||
How to Cite?
D. Raja, M. Karthikeyan, "Plant Disease Detection and Classification Based on Rat Swarm Optimization using Deep Learning Approach," International Journal of Engineering Trends and Technology, vol. 71, no. 7, pp. 42-52, 2023. Crossref, https://doi.org/10.14445/22315381/IJETT-V71I7P204
Abstract
Plant diseases and pests cause considerable agricultural and ecological damage. Earlier prevention and detection of plant diseases are the most important aspect of crop harvesting since they can efficiently decrease growth disorders, thereby minimalizing the pesticide application for pollution-free crop harvesting. In that regard, automatic recognition of plant ailment using diverse Deep Learning (DL) and Machine Learning (ML) models has become an effective approach for precision agriculture. This article introduces a Rat Swarm Optimization with DL-based Plant Disease Detection and Classification (RSODL-PDDC) approach. The presented RSODL-PDDC technique is focused on the recognition and categorizing of plant diseases by implementing Computer Vision (CV) and DL models. Initially, image preprocessing is performed for noise removal that occurs in the images. Moreover, the RSODL-PDDC algorithm uses an attention based RetinaNet model for image segmentation purposes. Besides, the Class Attention Learning (CAL) layer is exploited to capture the discriminatory class-specific features while class-wise attention based on the Inception v3 (I-v3) model is applied for fine-grained semantic feature maps. For adjusting the hyper-parameters of the I-v3 method, the RSO algorithm is employed. Finally, Adaptive Neuro-Fuzzy Inference System (ANFIS) technique can be exploited for plant disease categorization. In order to validate the performance of the RSODL-PDDC technique, a widespread experimental analysis is performed. The result analysis pointed out the enhancements of the RSODL-PDDC algorithm over other approaches.
Keywords
Plant disease detection, Precision agriculture, Rat swarm optimization, Attention layer, Deep learning.
References
[1] Madhumini Mohapatra et al., “Botanical Leaf Disease Detection and Classification Using Convolutional Neural Network: A Hybrid Metaheuristic Enabled Approach,” Computers, vol. 11, no. 5, p. 82, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[2] R. Cristin et al., “Deep Neural Network Based Rider-Cuckoo Search Algorithm for Plant Disease Detection,” Artificial Intelligence Review, vol. 53, no. 7, pp. 4993-5018, 2020.
[CrossRef] [Google Scholar] [Publisher Link]
[3] Thippa Reddy Gadekallu et al., “A Novel PCA–Whale Optimization-Based Deep Neural Network Model for Classification of Tomato Plant Diseases Using GPU,” Journal of Real-Time Image Processing, vol. 18, no. 4, pp. 1383-1396, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[4] Madhu Bala, and Vineet Mehan, “Metaheuristic Techniques for Classification Used in Identification of Plant Diseases,” ECS Transactions, vol. 107, no. 1, p. 13473, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[5] Vibhor Kumar Vishnoi, Krishan Kumar, and Brajesh Kumar, “Plant Disease Detection using Computational Intelligence and Image Processing,” Journal of Plant Diseases and Protection, vol. 128, no. 1, pp. 19-53, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[6] Muhammad Suleman Memon, Pardeep Kumar, and Rizwan Iqbal, “Meta Deep Learn Leaf Disease Identification Model for Cotton Crop,” Computers, vol. 11, no. 7, p. 102, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[7] Ahmad Almadhor et al., “AI-Driven Framework for Recognition of Guava Plant Diseases through Machine Learning from DSLR Camera Sensor based High Resolution Imagery,” Sensors, vol. 21, no. 11, p. 3830, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[8] R. Sabitha, and G. Kiruthiga, “Plant Disease Recognition and Clustering Using Fuzzy Algorithm on Data Mining,” ICTACT Journal on Soft Computing, vol. 11, no. 4, pp. 2429-2432, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[9] Yan Guo et al., “Plant Disease Identification based on Deep Learning Algorithm in Smart Farming,” Discrete Dynamics in Nature and Society, 2020.
[CrossRef] [Google Scholar] [Publisher Link]
[10] Sandeep Kumar et al., “Plant Leaf Disease Identification using Exponential Spider Monkey Optimization,” Sustainable Computing: Informatics and Systems, vol. 28, p. 100283, 2020.
[CrossRef] [Google Scholar] [Publisher Link]
[11] H. K. Jayaramu, Dharavath Ramesh, and Sonal Jain, “ESMO-based Plant Leaf Disease Identification: A Machine Learning Approach,” Computer Vision and Machine Learning in Agriculture, Springer, Singapore, vol. 2, pp. 155-170, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[12] Shankarnarayanan Nalini et al., “Paddy Leaf Disease Detection using an Optimized Deep Neural Network,” Computers, Materials & Continua, vol. 68, no. 1, pp. 1117-1128, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[13] J. Anitha Ruth, R. Uma, A. Meenakshi, “Apple Leaf Disease Identification Based on Optimized Deep Neural Network,” Handbook of Research on Deep Learning-Based Image Analysis under Constrained and Unconstrained Environments, IGI Global, pp. 167-185, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[14] J. Anitha Ruth et al., “Meta-Heuristic Based Deep Learning Model for Leaf Diseases Detection,” Neural Processing Letters, vol. 54, no. 6, pp. 5693-5709, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[15] M. Azath, and Vaishali Singh, "Optimized Convolutional Neural Network Based Privacy Based Collaborative Intrusion Detection System for Vehicular Ad Hoc Network," SSRG International Journal of Electrical and Electronics Engineering, vol. 10, no. 2, pp. 143-156, 2023.
[CrossRef] [Publisher Link]
[16] A. Umamageswari, N. Bharathiraja, and D. Shiny Irene, “A Novel Fuzzy C-Means based Chameleon Swarm Algorithm for Segmentation and Progressive Neural Architecture Search for Plant Disease Classification,” ICT Express, vol. 9, no. 2, pp. 160-167, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[17] Muhammad Attique Khan et al., “Cucumber Leaf Diseases Recognition using Multi Level Deep Entropy-ELM Feature Selection,” Applied Sciences, vol. 12, no. 2, p. 593, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[18] Yuanyuan Wang et al., “Automatic Ship Detection based on Retinanet Using Multi-Resolution Gaofen-3 Imagery,” Remote Sensing, vol. 11, no. 5, p. 531, 2019.
[CrossRef] [Google Scholar] [Publisher Link]
[19] Muhammad Rashid et al., “A Sustainable Deep Learning Framework for Object Recognition using Multi-Layers Deep Features Fusion and Selection,” Sustainability, vol. 12, no. 12, p. 5037, 2020.
[CrossRef] [Google Scholar] [Publisher Link]
[20] Yuansheng Hua, Lichao Mou, and Xiao Xiang Zhu, “Recurrently Exploring Class-Wise Attention in a Hybrid Convolutional and Bidirectional LSTM Network for Multi-Label Aerial Image Classification,” ISPRS Journal of Photogrammetry and Remote Sensing, vol. 149, pp. 188-199, 2019.
[CrossRef] [Google Scholar] [Publisher Link]
[21] Toufik Mzili et al., “A Novel Discrete Rat Swarm Optimization (DRSO) Algorithm for Solving The Traveling Salesman Problem,” Decision Making: Applications in Management and Engineering, vol. 5, no. 2, pp. 287-299, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[22] Vijo M Joy, Joseph John, and S Krishnakumar, "Optimal Model for Effective Power Scheduling using Levenberg-Marquardt Optimization Algorithm," SSRG International Journal of Electrical and Electronics Engineering, vol. 9, no. 10, pp. 1-6, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[23] [Online]. Available: https://www.kaggle.com/vipoooool/new-plant-diseases-dataset/data#
[24] Bin Liu et al., “Identification of Apple Leaf Diseases based on Deep Convolutional Neural Networks,” Symmetry, vol. 10, no. 1, p. 11, 2018.
[CrossRef] [Google Scholar] [Publisher Link]
[25] Chongke Bi et al., “MobileNet based Apple Leaf Diseases Identification,” Mobile Networks and Applications, vol. 27, pp. 172-180, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[26] Bin Liu et al., “Kiwifruit Leaf Disease Identification Using Improved Deep Convolutional Neural Networks,” IEEE 44th Annual Computers, Software, and Applications Conference, pp. 1267-1272, 2020.
[CrossRef] [Google Scholar] [Publisher Link]
[27] A. Pavithra, and G. Kalpana, “An Efficient Plant Disease Identification Using Intense Transfer Learning Empowered Efficient Net with Kernel Extreme Learning Machine,” 4th International Conference on Smart Systems and Inventive Technology (ICSSIT), IEEE, pp. 909-914, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[28] Mohamed H. Osman et al., “Highly Efficient MPP Tracker based on Adaptive Neuro-fuzzy Inference System for Stand-Alone Photovoltaic Generator System,” International Journal of Renewable Energy Research, vol. 12, no. 1, pp. 208-217, 2022.
[Google Scholar] [Publisher Link]