Plant Leaf Disease Classification Using Optimal Tuned Hybrid LSTM-CNN Model
- Title
- Plant Leaf Disease Classification Using Optimal Tuned Hybrid LSTM-CNN Model
- Creator
- Patil M.A.; Manohar M.
- Description
- Tomatoes are widely cultivated and consumed worldwide and are susceptible to various leaf diseases during their growth. Therefore, early detection and prediction of leaf diseases in tomato crops are crucial. Farmers can take proactive measures to prevent the spread and minimize the impact on crop yield and quality by identifying leaf diseases in their early stages. Several Machine Learning (ML) and Deep Learning (DL) frameworks have been developed recently to identify leaf diseases. This research presents an efficient deep-learning approach based on a hybrid classifier by optimizing the CNN and LSTM models, which helps to enhance classification accuracy. Initially, Median Filtering (MF) is used for leaf image pre-processing. Then, an improved watershed approach is used for segmenting the leaf images. Subsequently, enhanced Local Gabor Pattern (LGP) and statistical and color features are extracted. An optimized CNN and LSTM are used for classification, and the weights are tuned using the SISS-OB (Self Improved Shark Smell With Opposition Behavior) algorithm. Finally, we have analyzed the performance using various measures. Since we have done segmentation, feature extraction, and optimization improvisations, our proposed methodology results are higher than other available methods and existing works. The results obtained at Learning Percentage (LP) is 90% which is far superior to those obtained at other LPs. The FNR (False Negative Rate) is much lower (0.05) at the 90th LP. The proposed model achieved better classification performance in terms of Accuracy of 97.13%, Sensitivity of 95.09%, Specificity of 95.24%, Precision of 94.31%, F measure of 96.71% and MCC 87.34%. 2023, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd.
- Source
- SN Computer Science, Vol-4, No. 6
- Date
- 2023-01-01
- Publisher
- Springer
- Subject
- CNN; Deep Learning; Hybrid classifier; LSTM; SISS-OB
- Coverage
- Patil M.A., Department of Computer Science and Engineering, Christ (Deemed to be University) School of Engineering and Technology, Kengeri, Karnataka, Bengaluru, 560074, India, Department of Computer Science and Engineering, G. Narayanamma Institute of Technology and Science, Shaikpet, Telangana, Hyderabad, 500104, India; Manohar M., Department of Computer Science and Engineering, Christ (Deemed to be University) School of Engineering and Technology, Kengeri, Karnataka, Bengaluru, 560074, India
- Rights
- Restricted Access
- Relation
- ISSN: 2662995X
- Format
- Online
- Language
- English
- Type
- Article
Collection
Citation
Patil M.A.; Manohar M., “Plant Leaf Disease Classification Using Optimal Tuned Hybrid LSTM-CNN Model,” CHRIST (Deemed To Be University) Institutional Repository, accessed April 20, 2025, https://archives.christuniversity.in/items/show/14006.