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 Articles 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/. Title A name given to the resource Progressive loss-aware fine-tuning stepwise learning with GAN augmentation for rice plant disease detection Subject The topic of the resource Augmentation; Deep Learning; Plant Disease; Rice Disease; Transfer Learning Description An account of the resource Modern technology like Artificial Intelligence (AI) must be used in the agricultural sectorif sustainable agricultural output is to be achieved. One of the most convenient strategies for resolving current and future issues is data-driven agriculture. For this, disease prediction is a major task for precise farming. For predictive analysis and precise agriculture monitoring systems, with the application of AI, Machine Learning (ML) and Deep Learning (DL) play vital roles in building a more robust system. In this work, we will design a DL-integrated rice disease prediction system to be implemented for precise farming. Improvisation of the developed model to detect rice plant diseases &amp; pest attacks with a high level of precision. In this work, the Progressive Loss-Aware Fine-Tuning Stepwise Learning (PLAFTSL) model is proposed for disease detection. For step-wise learning fine-tuned ResNet50 model is used with the introduction of freezing and unfreezing layers. This reduces the training parameters and thus computational complexity. The introduction of the step-wise and progressive loss-aware layer will result in fast convergence and improved training efficiency during information exchange among layers respectively. Our proposed work uses a dataset from two sources. The result analysis is presented with an ablation study. Additionally, the baseline model, ResNet50, is used to display the outcomes of the ablation. The results demonstrate that the fine-tuned model results in better performance as compared to the transfer learning model. The Conditional Generative Adversarial Network (cGAN) augmentation is also added to the designed model which will improve detection effectiveness and can also manage the imbalance in input data. The model has achieved approx. 98% accuracy and outperforms better with comparative state-of-art models. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024. Creator An entity primarily responsible for making the resource Upreti K.; Singh P.; Jain D.; Pandey A.K.; Gupta A.; Singh H.R.; Srivastava S.K.; Prasad J.S. Source A related resource from which the described resource is derived Multimedia Tools and Applications, Vol-83, No. 37, pp. 84565-84588. Publisher An entity responsible for making the resource available Springer Date A point or period of time associated with an event in the lifecycle of the resource 2024-01-01 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1007/s11042-024-19255-z" target="_blank" rel="noreferrer noopener">https://doi.org/10.1007/s11042-024-19255-z</a> <br /><br /><a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85191303927&amp;doi=10.1007%2Fs11042-024-19255-z&amp;partnerID=40&amp;md5=1d9fd247a985f5965a4b02d4a9ca0488" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85191303927&amp;doi=10.1007%2fs11042-024-19255-z&amp;partnerID=40&amp;md5=1d9fd247a985f5965a4b02d4a9ca0488</a> Rights Information about rights held in and over the resource Restricted Access Relation A related resource ISSN: 13807501; CODEN: MTAPF 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 Article Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Upreti K., CHRIST(Deemed to be University), Delhi NCR Campus, Delhi, India; Singh P., JSS Academy of Technical Education, Noida, India; Jain D., ABES Engineering College, Ghaziabad, India; Pandey A.K., ABES Engineering College, Ghaziabad, India; Gupta A., Department of CSE (AI &amp; amp; ML), PIET Engineering College Samalkha, Haryana, India; Singh H.R., Greater Noida Institute of Technology, Greater Noida, India; Srivastava S.K., Department of Applied Computational Science &amp; amp; Engineering, GL BAJAJ Institute of Technology &amp; amp; Management, Greater Noida, India; Prasad J.S., Greater Noida Institute of Technology, Greater Noida, India