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 Conference Papers Conference Paper Faculty Publications- Conference Papers 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 Potato Leaf Disease Identification using Hybrid Deep Learning Model Subject The topic of the resource CNN; leaf disease; LSTM; OCNN-LSTM; Potato Description An account of the resource The potato is one of the most significant crops in the world. However, it is prone to several leaf diseases that can result in significant productivity losses, leading to economic challenges. Early and precise disease identification is essential for sensitive crops like potatoes. Deep learning approaches have demonstrated excellent potential in image-based disease classification tasks in recent years. This paper presents a hybrid strategy for classifying potato leaf image diseases by integrating Optimised Convolutional Neural Network (OCNN) and Long Short-Term Memory (LSTM) networks. The Adaptive Shark Smell Optimisation (ASSO) technique is used to optimize the weights of CNN models. The CNN component is initially used to extract pertinent characteristics from Potato leaves, capturing significant visual patterns related to various diseases. These extracted features are then fed into the LSTM model, which utilizes its sequential learning capability to model the temporal dependencies among the extracted features. The model performance is analyzed based on Accuracy, Precision, Recall, and F1-score criteria. Experimental results showed that the hybrid OCNN-LSTM model outperforms the individual CNN, LSTM, and MobileNet models. The proposed model results are compared with existing state-of-the-art work, and it was found that the OCNN-LSTM model performed better and received 99.02% accuracy. 2023 IEEE. 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 2023 International Conference on Network, Multimedia and Information Technology, NMITCON 2023 Publisher An entity responsible for making the resource available Institute of Electrical and Electronics Engineers Inc. 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/10.1109/NMITCON58196.2023.10276091" target="_blank" rel="noreferrer noopener">https://doi.org/10.1109/NMITCON58196.2023.10276091</a> <br /><br /><a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85175401706&amp;doi=10.1109%2FNMITCON58196.2023.10276091&amp;partnerID=40&amp;md5=d88357d877dadc48b14463f596e08d74" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85175401706&amp;doi=10.1109%2fNMITCON58196.2023.10276091&amp;partnerID=40&amp;md5=d88357d877dadc48b14463f596e08d74</a> Rights Information about rights held in and over the resource Restricted Access Relation A related resource ISBN: 979-835030082-6 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 Conference paper 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., School O F Engineering and Technology, Dept, of Computer Science and Engineering Christ (Deemed to Be University), Bengaluru, India; Manohar M., School O F Engineering and Technology, Dept, of Computer Science and Engineering Christ (Deemed to Be University), Bengaluru, India