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 Comparative Analysis of Maize Leaf Disease Detection using Convolutional Neural Networks Subject The topic of the resource Deep learning; Efficientnet; Inceptionv3; Visual Geometry Group(VGG) Description An account of the resource Worldwide, maize is a significant cereal crop for crop productivity, identifying diseases in the plant's leaves is essential to raise a good crop. Deep learning methods that have been used in recent years to precisely identify and categorize these serious diseases, offering a non-destructive and effective way to find maize leaf ailments. In order to detect maize leaf disease, this paper suggests using three well-liked deep learning models: VGG16, Inception V3, and EfficientNet. The models were trained and assessed using a datasets of 4000 images of three distinct maize leaf diseases and a healthy class. All three models had high accuracy rates, according to the results, though EfficientNet outperformed the other two models. The suggested method can detect and track diseases in maize crops with high accuracy and can be applied practically. It can accurately classify various diseases. The study also demonstrates that deep learning models can offer a trustworthy and effective solution for detecting crop diseases, which can aid in lowering crop losses, raising crop yields, and enhancing food security. 2023 IEEE. Creator An entity primarily responsible for making the resource Kamalam G.K.; Rajasekar V.; Krishnasamy L.; Fathima Kadhoon M. Source A related resource from which the described resource is derived 3rd IEEE International Conference on ICT in Business Industry and Government, ICTBIG 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/ICTBIG59752.2023.10455997" target="_blank" rel="noreferrer noopener">https://doi.org/10.1109/ICTBIG59752.2023.10455997</a> <br /><br /><a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85189248108&amp;doi=10.1109%2FICTBIG59752.2023.10455997&amp;partnerID=40&amp;md5=6f0644b374cdf859196ff4b848f96d06" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85189248108&amp;doi=10.1109%2fICTBIG59752.2023.10455997&amp;partnerID=40&amp;md5=6f0644b374cdf859196ff4b848f96d06</a> Rights Information about rights held in and over the resource Restricted Access Relation A related resource ISBN: 979-835034327-4 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 Kamalam G.K., Kongu Engineering College, Department of Information Technology, Tamil Nadu, India; Rajasekar V., Kongu Engineering College, Department of Computer Science and Engineering, Tamil Nadu, India; Krishnasamy L., School of Engineering and Technology, Christ University, Department of Cse, Bengaluru, India; Fathima Kadhoon M., Kongu Engineering College, Department of Information Technology, Tamil Nadu, India