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 Kujur, Lawrence; Gupta, Varuna; Singhal, Abhinav Title A name given to the resource A hybrid multi-optimizer approach using CNN and GB for accurate prediction of citrus fruit diseases 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 Discover Applied Sciences;Volume;7;Issue;3;Article No.;151; Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1007/s42452-025-06593-2" target="_blank" rel="noreferrer noopener">https://doi.org/10.1007/s42452-025-06593-2</a> <br /><br /><a href="https://www.scopus.com/pages/publications/85218419799?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/85218419799?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 Kujur L., Christ University, Bengaluru, 560029, India; Gupta V., Christ University, Bengaluru, 560029, India; Singhal A., Christ University, Bengaluru, 560029, India Description An account of the resource Efficient prediction of citrus fruit diseases is essential for maintaining orchard health and productivity. Traditional diagnostic methods, often relying on manual inspection, are labor-intensive and prone to inaccuracies. Deep learning techniques, especially Convolutional Neural Networks (CNNs), offer an automated and accurate alternative. This study introduces a novel model integrating CNN with Gradient Boosting (GB) and optimized using the Nesterov-Accelerated Adaptive Moment Estimation (Nadam) optimizer to enhance prediction accuracy. The model employs a custom CNN architecture combined with GB, leveraging Nadam for faster convergence and improved performance. Trained on a dataset of 3,000 citrus fruit images sourced from Kaggle, the model follows a structured process of preprocessing, feature extraction, integration of GB with CNN, and optimal prediction. Comparative analysis using metrics such as accuracy, precision, F1 score, and recall demonstrates the model's effectiveness, achieving an accuracy of 98.03% and precision of 98.04%. This robust approach addresses limitations of traditional methods by enabling automated feature extraction and reliable disease prediction. The proposed CNN-GB-Nadam model significantly enhances efficiency and reliability, providing a valuable tool for protecting citrus fruit health and improving orchard management practices. The Author(s) 2025. Subject The topic of the resource CNN-GB-Nadam; Convolutional Neural Networks (CNN); Disease protection; Forecasting; Gradient boost (GB); NADAM Publisher An entity responsible for making the resource available Springer Nature Relation A related resource ISSN: 30049261; 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