Modified Convolutional Block Attention Mechanism-Based VGG16 for Apple Plant Leaf Disease Classification
| International Journal of Electronics and Communication Engineering |
| © 2024 by SSRG - IJECE Journal |
| Volume 11 Issue 8 |
| Year of Publication : 2024 |
| Authors : Pooja Chandrakantbhai Gajjar, Hetal Nirmit Dalal, Hina Kalpesh Patel, Nisha Kantilal Prajapati |
10.14445/23488549/IJECE-V11I8P107
How to Cite?
Pooja Chandrakantbhai Gajjar, Hetal Nirmit Dalal, Hina Kalpesh Patel, Nisha Kantilal Prajapati, "Modified Convolutional Block Attention Mechanism-Based VGG16 for Apple Plant Leaf Disease Classification," SSRG International Journal of Electronics and Communication Engineering, vol. 11, no. 8, pp. 62-71, 2024. Crossref, https://doi.org/10.14445/23488549/IJECE-V11I8P107
Abstract:
Agriculture plays a significant role in a country's development like India, where most of the population’s livelihood is based on agriculture. The production of apples plays a vital role in the agricultural sector by contributing to the economy through domestic consumption and export. However, different diseases exhibit similar visual symptoms on apple leaves, such as spots or discolorations, leading to misclassification. This research proposes the Modified Convolutional Block Attention Mechanism-VGG16 (MCBAM-VGG16) to classify apple plant leaf disease accurately. Global Average Pooling (GAP) layer and CBAM are added after the first convolutional layer which minimizes the number of parameters and helps to avoid underfitting issues during training. Initially, images are obtained from the plant village dataset to evaluate the proposed approach. Then, data augmentation is used to transform images, which helps MCBAM-VGG16 attain better tolerance and generalization ability. Then, W-Net is employed to segment images that capture both global context and fine-grained information within the leaf images. At last, MCBAM-VGG16 classifies apple plant leaf disease accurately. When compared to the existing techniques like Deep Convolutional Neural Networks with three convolutional layers (Conv-3 DCNN), the improved DCNN and Random Sample Consensus (RANSAC), MCBAM-VGG16 achieves a superior accuracy of 0.998.
Keywords:
Agriculture, Apple plant leaf disease, Data augmentation, Modified convolutional block attention mechanism-VGG16, W-Net.
References:
[1] Sharad Hasan, Sarwar Jahan, and Md. Imdadul Islam, “Disease Detection of Apple Leaf with Combination of Color Segmentation and Modified DWT,” Journal of King Saud University-Computer and Information Sciences, vol. 34, no. 9, pp.7212-7224, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[2] Prabhjot Kaur et al., “Recognition of Leaf Disease Using Hybrid Convolutional Neural Network by Applying Feature Reduction,” Sensors, vol. 22, no. 2, pp. 1-16, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[3] Jiye Zheng et al., “RepDI: A Light-Weight CPU Network for Apple Leaf Disease Identification,” Computers and Electronics in Agriculture, vol. 212, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[4] Lili Fu et al., “Lightweight-Convolutional Neural Network for Apple Leaf Disease Identification,” Frontiers in Plant Science, vol. 13, pp. 1-10, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[5] Emmanuel Moupojou et al., “FieldPlant: A Dataset of Field Plant Images for Plant Disease Detection and Classification with Deep Learning,” IEEE Access, vol. 11, pp. 35398-35410, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[6] Waleed Albattah et al., “A Novel Deep Learning Method for Detection and Classification of Plant Diseases,” Complex & Intelligent Systems, vol. 8, no. 1, pp. 507-524, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[7] Yuxi Gao et al., “Apple Leaf Disease Identification in Complex Background based on BAM-Net,” Agronomy, vol. 13, no. 5, pp. 1-22, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[8] Saleh Albahli, and Marriam Nawaz, “DCNet: DenseNet-77-Based CornerNet Model for the Tomato Plant Leaf Disease Detection and Classification,” Frontiers in Plant Science, vol. 13, pp. 1-19, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[9] Kangchen Liu, and Xiujun Zhang, “PiTLiD: Identification of Plant Disease from Leaf Images Based on Convolutional Neural Network,” IEEE/ACM Transactions on Computational Biology and Bioinformatics, vol. 20, no. 2, pp. 1278-1288, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[10] K. Indira, and H. Mallika, “Classification of Plant Leaf Disease Using Deep Learning,” Journal of The Institution of Engineers (India): Series B, vol. 105, no. 3, pp. 609-620, 2024.
[CrossRef] [Google Scholar] [Publisher Link]
[11] Adesh V. Panchal et al., “Image-Based Plant Diseases Detection using Deep Learning,” Materials Today: Proceedings, vol. 80, no. 3, pp. 3500-3506, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[12] Poornima Singh Thakur, Tanuja Sheorey, and Aparajita Ojha, “VGG-ICNN: A Lightweight CNN Model for Crop Disease Identification,” Multimedia Tools and Applications, vol. 82, no. 1, pp. 497-520, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[13] Gao Ang et al., “Construction and Verification of Machine Vision Algorithm Model Based on Apple Leaf Disease Images,” Frontiers in Plant Science, vol. 14, pp. 1-13, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[14] Ruchi Gajjar et al., “Real-Time Detection and Identification of Plant Leaf Diseases Using Convolutional Neural Networks on an Embedded Platform,” The Visual Computer, vol. 38, no. 8, pp. 2923-2938, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[15] Bui Thi Hanh, Hoang Van Manh, and Ngoc-Viet Nguyen, “Enhancing the Performance of Transferred Efficientnet Models in Leaf ImageBased Plant Disease Classification,” Journal of Plant Diseases and Protection, vol. 129, no. 3, pp. 623-634, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[16] Vibhor Kumar Vishnoi et al., “Detection of Apple Plant Diseases Using Leaf Images through Convolutional Neural Network,” IEEE Access, vol. 11, pp. 6594-6609, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[17] Dharmendra Kumar Mahato, Amit Pundir, and Geetika Jain Saxena, “An Improved Deep Convolutional Neural Network for Image-Based Apple Plant Leaf Disease Detection and Identification,” Journal of the Institution of Engineers (India): Series A, vol. 103, no. 4, pp. 975- 987, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[18] Yashwant Kurmi, and Suchi Gangwar, “A Leaf Image Localization Based Algorithm for Different Crops Disease Classification,” Information Processing in Agriculture, vol. 9, no. 3, pp. 456-474, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[19] Khalid M. Hosny et al., “Multi-Class Classification of Plant Leaf Diseases Using Feature Fusion of Deep Convolutional Neural Network and Local Binary Pattern,” IEEE Access, vol. 11, pp. 62307-62317, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[20] Xin Zhang et al., “A Plant Leaf Disease Image Classification Method Integrating Capsule Network and Residual Network,” IEEE Access, vol. 12, pp. 44573-44585, 2024.
[CrossRef] [Google Scholar] [Publisher Link]
[21] [Online]. Available: https://www.kaggle.com/datasets/abdallahalidev/plantvillage-dataset
[22] Qing Yang, Shukai Duan, and Lidan Wang, “Efficient Identification of Apple Leaf Diseases in the Wild Using Convolutional Neural Networks,” Agronomy, vol. 12, no. 11, pp. 1-20, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[23] Ruisong Zhang et al., “W-Net: Structure and Texture Interaction for Image Inpainting,” IEEE Transactions on Multimedia, vol. 25, pp. 7299-7310, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[24] Shagun Sharma et al., “A Deep Learning Based Convolutional Neural Network Model with VGG16 Feature Extractor for the Detection of Alzheimer Disease Using MRI Scans,” Measurement: Sensors, vol. 24, pp. 1-8, 2022.
[CrossRef] [Google Scholar] [Publisher Link]