Freshness Identification of Fruits Through the Development of a Dataset-Reference-Cited by-同舟云学术

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Freshness Identification of Fruits Through the Development of a Dataset

Published:2023 Issue: Volume: Page:53-67
ISSN:2524-7565
Container-title:Computer Vision and Machine Learning in Agriculture, Volume 3
language:
Short-container-title:

Author:

Sultana Nusrat,Jahan Musfika,Uddin Mohammad Shorif

Publisher

Springer Nature Singapore

Link

https://link.springer.com/content/pdf/10.1007/978-981-99-3754-7_4

Reference20 articles.

1. Ananthanarayana T, Ptucha R, Kelly SC (2020) Deep learning based fruit freshness classification and detection with CMOS image sensors and edge processors. Electron Imaging

2. Arakeri MP, Lakshmana (2016) Computer vision based fruit grading system for quality evaluation of tomato in agriculture industry. Proc Comput Sci 79:426–433. https://doi.org/10.1016/j.procs.2016.03.055

3. Bresilla K, Perulli GD, Boini A, Morandi B, Grappadelli LC, Manfrini L (2019) Single-shot convolution neural networks for real-time fruit detection within the tree. Front Plant Sci 10

4. Zeng G (2017) Fruit and vegetables classification system using image saliency and convolutional neural network. In: 2017 IEEE 3rd Information technology and mechatronics engineering conference (ITOEC), pp 613–617. https://doi.org/10.1109/ITOEC.2017.8122370

5. Bird JJ, Barnes CM, Manso LJ, Ekárt A, Faria DR (2021) Fruit quality and defect image classification with conditional GAN data augmentation. https://arxiv.org/abs/2104.05647

Cited by 1 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Rotten and Fresh Fruits Classification using Deep Learning;2024 IEEE International Conference on Computing, Power and Communication Technologies (IC2PCT);2024-02-09

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