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 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/. Creator An entity primarily responsible for making the resource Gokulapriya, R.; Sambandam, Rakoth Kandan; Joy, Jerin; Jacob, Kevin S. Title A name given to the resource ResFruitGrader: Leveraging Residual Networks for Advanced Fruit Quality Grading Systems 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 Lecture Notes in Networks and Systems;Volume;1286 LNNS;pp.331-341 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1007/978-981-96-4008-9_25" target="_blank" rel="noreferrer noopener">https://doi.org/10.1007/978-981-96-4008-9_25</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105018795047?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105018795047?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 Gokulapriya R., Department of CSE, Christ University, Bengaluru, India; Sambandam R.K., Department of CSE, Christ University, Bengaluru, India; Joy J., Department of CSE, Christ University, Bengaluru, India; Jacob K.S., Department of CSE, Christ University, Bengaluru, India Description An account of the resource The rising agricultural industrys requirement for effective sorting and grading procedures has increased the demand for automated and precise fruit quality assessment in recent years. This study aims to attain high classification accuracy by investigating the use of Convolutional Neural Networks for fruit quality identification. As customers place a higher value on fresh and wholesome options, the agriculture and food industries must meet rising demands for premium produce. Fruit quality must be guaranteed since it directly affects consumer happiness and the profitability of the sector. Preprocessing methods, CNN model creation, training, and evaluation utilizing cutting-edge deep learning techniques comprise the methodology applied in our study. The research demonstrates the CNN-based methods stability and dependability in identifying a range of quality attributes, such as fruit imperfections, size, color, and maturity. The suggested CNN architecture performs remarkably well, recognizing fruit quality parameters with a 99.5% accuracy rate by utilizing a collection of various fruit photos. A promising path for improving efficiency and accuracy in fruit quality assessment within the agricultural industry is provided by the researchs insights into the transferability and scalability of the developed model for practical applications in automated fruit sorting systems. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025. Subject The topic of the resource Automated quality detection system; CNN; Fruit quality detection; Image processing; Inception ResNet V2; Machine learning Publisher An entity responsible for making the resource available Springer Science and Business Media Deutschland GmbH Relation A related resource ISSN: 23673370; ISBN: 978-981964007-2; Language A language of the resource English Type The nature or genre of the resource Conference paper Rights Information about rights held in and over the resource Restricted Access; Hardcopy may be available in the library Format The file format, physical medium, or dimensions of the resource online