ResFruitGrader: Leveraging Residual Networks for Advanced Fruit Quality Grading Systems
- Title
- ResFruitGrader: Leveraging Residual Networks for Advanced Fruit Quality Grading Systems
- Creator
- Gokulapriya, R.; Sambandam, Rakoth Kandan; Joy, Jerin; Jacob, Kevin S.
- Description
- 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.
- Source
- Lecture Notes in Networks and Systems;Volume;1286 LNNS;pp.331-341
- Date
- 01-01-2025
- Publisher
- Springer Science and Business Media Deutschland GmbH
- Subject
- Automated quality detection system; CNN; Fruit quality detection; Image processing; Inception ResNet V2; Machine learning
- Coverage
- 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
- Rights
- Restricted Access; Hardcopy may be available in the library
- Relation
- ISSN: 23673370; ISBN: 978-981964007-2;
- Format
- online
- Language
- English
- Type
- Conference paper
Collection
Citation
Gokulapriya, R.; Sambandam, Rakoth Kandan; Joy, Jerin; Jacob, Kevin S., “ResFruitGrader: Leveraging Residual Networks for Advanced Fruit Quality Grading Systems,” CHRIST (Deemed To Be University) Institutional Repository, accessed June 21, 2026, https://archives.christuniversity.in/items/show/25520.