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 Faculty Publications 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/. Creator An entity primarily responsible for making the resource Deepakraj, Jamuna; John, Tegil J; Sathiyanathan, S.; Josephson, P Joel; Priyanka, H.D.; Suganthi, D. Title A name given to the resource Automated Leaf Disease Detection using a Hybrid CNN-BiLSTM Model for Smart Agriculture Date A point or period of time associated with an event in the lifecycle of the resource 01-01-2025 Source A related resource from which the described resource is derived Proceedings of 8th International Conference on Computing Methodologies and Communication, ICCMC 2025;pp.1993-1998 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1109/ICCMC65190.2025.11140880" target="_blank" rel="noreferrer noopener">https://doi.org/10.1109/ICCMC65190.2025.11140880</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105016904345?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105016904345?origin=resultslist</a> Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Deepakraj J., Erode Sengunthar Engineering College, Department of Artificial Intelligence and Data Science, Tamil Nadu, Perundurai, India; John T.J., Christ University, Department of Computer Science, Karnataka, Bangalore, India; Sathiyanathan S., M.Kumarasamy College of Engineering, Department of It, Tamilnadu, Karur, India; Josephson P.J., Malla Reddy (MR) Deemed to Be University, Dept of Ece, Telangana, Hyderabad, India; Priyanka H.D., Bgs Institute of Technology, Faculty of Engineering, Management and Technology, Department of Chemistry, Mandya, India; Suganthi D., Chennai Institute of Technology, Department of Computer Science and Engineering, Chennai, India Description An account of the resource The mitigation of crop losses and the sustainability of agriculture rely on the prompt identification of foliar diseases. In large-scale agriculture, conventional identification methods such as expert eye inspections are inefficient, susceptible to errors, and labour-intensive. A growing number of individuals are seeking automated methods to monitor plant health, given that the majority of Indians are employed in agriculture. This study presents a hybrid DL strategy for leaf disease detection, encompassing preprocessing, segmentation, feature extraction, and model training. Initially, images are processed to enhance their quality and uniformity. The impacted regions of the leaf are subsequently categorised by K-Means clustering. The classification accuracy is improved by utilising several feature extraction methods. The proposed model, CNBiLS, integrates bidirectional LSTM layers with convolutional layers to leverage the spatial and sequential information in image data. When evaluated against contemporary state-of-the-art models, CNBiLS exhibited superior performance, achieving an exceptional 99.84% classification accuracy. This result underscores the model's accuracy in identifying various leaf diseases. Ultimately, CNBiLS offers a precise, scalable, and robust automated system for detecting leaf diseases, equipping farmers with timely information to manage illnesses effectively, so enhancing both the quality and yield of their crops. 2025 IEEE. Subject The topic of the resource Back Propagation Neural Network (BPNN); Graphical User Interface (GUI); Hue Saturation Value (HSV); Leaf Disease Detection (LDD); Local Binary Patterns (LBP); Markov Random Fields (MRFs) Publisher An entity responsible for making the resource available Institute of Electrical and Electronics Engineers Inc. Relation A related resource ISBN: 979-833151211-8; Language A language of the resource English Type The nature or genre of the resource Conference paper Rights Information about rights held in and over the resource Restricted Access; Hardcopy may be available in the library Format The file format, physical medium, or dimensions of the resource online