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 Pawaskar, Praveen; Yogish, H.K.; Pakruddin, B.; Deepa, Y. Title A name given to the resource A Novel and Efficient Deep Learning Models for Assessing AIs Impact on Disease Diagnosis in 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 Indian Journal of Agricultural Research;Volume;59;Issue;9;pp.1395-1403 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.18805/IJARe.A-6388" target="_blank" rel="noreferrer noopener">https://doi.org/10.18805/IJARe.A-6388</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105016859404?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105016859404?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 Pawaskar P., Department of Computer Science and Engineering, MS Ramaiah Institute of Technology, Bengaluru, Visvesvaraya Technological University, Karnataka, Belagavi, 590 018, India, School of Computer Science and Engineering and Information Science and Engineering, Presidency University, Karnataka, Bengaluru, 560 064, India; Yogish H.K., Department of Computer Science and Engineering, MS Ramaiah Institute of Technology, Bengaluru, Visvesvaraya Technological University, Karnataka, Belagavi, 590 018, India; Pakruddin B., School of Computer Science and Engineering and Information Science and Engineering, Presidency University, Karnataka, Bengaluru, 560 064, India; Deepa Y., Department of Computer Science and Engineering, Christ University, Karnataka, Bengaluru, 560 029, India Description An account of the resource Background: Agriculture sustains human life by providing food, raw materials and employment opportunities. However, climate change and resource limitations pose significant challenges to crop production. AI-driven smart farming has emerged as a solution to enhance agricultural efficiency, with Explainable AI (XAI) improving transparency in decision-making. Innovations such as smart sensors and automated systems have benefited key agricultural sectors, including crops, forestry, livestock and aquaculture. Turmeric, valued for its medicinal and economic significance, requires careful monitoring to combat diseases like leaf spot and leaf blotch, which can impact yield and quality. Methods: This study introduces turmeric net, a Convolutional Neural Network (CNN)-based model leveraging transfer learning to detect and classify turmeric leaf diseases. The dataset used consists of 791 original images and 3,702 augmented images obtained from mendeley data, categorized into four classes: healthy leaf, dry leaf, leaf blotch and rhizome rot. The model development was carried out using TensorFlow, with ResNet50V2 as a baseline for comparison. The models were trained on processed image data, incorporating augmentation techniques to improve robustness and generalizability. Result: The accuracy of both models was evaluated. ResNet50V2 achieved an accuracy exceeding 99%, demonstrating high effectiveness in disease classification. Meanwhile, TurmericNet attained a competitive accuracy of 98%, making it a reliable alternative for turmeric disease identification. These results indicate that deep learning-based models can significantly aid in early disease detection, providing farmers with a valuable tool to enhance crop management and productivity. 2025 , Agricultural Research Communication Centre. All rigths reserved. Subject The topic of the resource Agriculture; Classification; Disease detection; Turmeric Publisher An entity responsible for making the resource available Agricultural Research Communication Centre Relation A related resource ISSN: 3678245; 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