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 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/. Creator An entity primarily responsible for making the resource Lingaraju, Anoop Ganadalu; Shankaregowda, Asha Mangala; Sathiyamurthy, Babu Kumar; Channegowda, Santhrupth Budanoor; Jalapur, Shruti; Chennakeshava, Chaitra Palahalli Title A name given to the resource Optimizing citrus disease detection: a transferable convolutional neural network model enhanced with the fruitfly optimization algorithm 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 IAES International Journal of Artificial Intelligence;Volume;14;Issue;4;pp.3201-3213 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.11591/ijai.v14.i4.pp3201-3213" target="_blank" rel="noreferrer noopener">https://doi.org/10.11591/ijai.v14.i4.pp3201-3213</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105014374362?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105014374362?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 Lingaraju A.G., Department of Computer Science and Engineering, School of Engineering and Technology, CHRIST University, Bengaluru, India; Shankaregowda A.M., Department of Computer Science and Engineering, School of Engineering and Technology, CHRIST University, Bengaluru, India; Sathiyamurthy B.K., Department of Computer Science and Engineering, School of Engineering and Technology, CHRIST University, Bengaluru, India; Channegowda S.B., Department of Computer Science and Engineering, School of Engineering and Technology, CHRIST University, Bengaluru, India; Jalapur S., Department of Computer Science and Engineering, School of Engineering and Technology, CHRIST University, Bengaluru, India; Chennakeshava C.P., Department of Computer Science and Engineering, School of Engineering and Technology, CHRIST University, Bengaluru, India Description An account of the resource Fungal, bacterial, and viral diseases significantly threaten citrus production and quality worldwide, prompting producers to explore technological solutions to mitigate the financial impact of these diseases. Image analysis techniques have emerged as powerful tools for detecting citrus diseases by differentiating between healthy and diseased specimens through the extraction of discriminative features from input images. This paper introduces a valuable dataset comprising 953 color images of orange leaves from the species Citrus sinensis (L.) Osbeck, which serves to train, evaluate, and compare various algorithms aimed at identifying abnormalities in citrus fruits. The development of automated detection systems is crucial for reducing economic losses in citrus production, with this research focusing on twelve specific diseases and nutrient deficiencies. We propose a novel approach to citrus plant disease detection utilizing a hyper-parameter tuned transferrable convolutional neural network (TCNN) model, referred to as the enhanced fruitfly optimization algorithm (EFOA)-TCNN model. This model optimizes the parameters of TCNN using the EFOA and enhances architectural design by incorporating three convolutional layers alongside an energy layer instead of a traditional pooling layer. Experimental results demonstrate that the proposed EFOA-TCNN model outperforms existing state-of-the-art methods, achieving a sensitivity of 0.975 and an accuracy of 0.995. 2025, Institute of Advanced Engineering and Science. All rights reserved. Subject The topic of the resource Bacteria; Citrus fruit disease detection; Enhanced fruitfly optimization algorithm; Image analysis; Transferrable convolutional neural network Publisher An entity responsible for making the resource available Institute of Advanced Engineering and Science Relation A related resource ISSN: 20894872; Language A language of the resource English Type The nature or genre of the resource Article Rights Information about rights held in and over the resource All Open Access; Gold Open Access Format The file format, physical medium, or dimensions of the resource online