Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Articles Article Faculty Publications -Articles Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Plant Leaf Disease Classification Using Optimal Tuned Hybrid LSTM-CNN Model Subject The topic of the resource CNN; Deep Learning; Hybrid classifier; LSTM; SISS-OB Description An account of the resource 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. Creator An entity primarily responsible for making the resource Patil M.A.; Manohar M. Source A related resource from which the described resource is derived SN Computer Science, Vol-4, No. 6 Publisher An entity responsible for making the resource available Springer Date A point or period of time associated with an event in the lifecycle of the resource 2023-01-01 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1007/s42979-023-02245-7" target="_blank" rel="noreferrer noopener">https://doi.org/10.1007/s42979-023-02245-7</a> <br /><br /><a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85171553061&amp;doi=10.1007%2Fs42979-023-02245-7&amp;partnerID=40&amp;md5=1899f1745684f58c92311d298b4b3b9a" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85171553061&amp;doi=10.1007%2fs42979-023-02245-7&amp;partnerID=40&amp;md5=1899f1745684f58c92311d298b4b3b9a</a> Rights Information about rights held in and over the resource Restricted Access Relation A related resource ISSN: 2662995X Format The file format, physical medium, or dimensions of the resource Online Language A language of the resource English Type The nature or genre of the resource Article Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant 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