The DOI matches its Crossref record; 48 of the 48 references it cites were found in a registry or an index.
Fruits and vegetables quality evaluation using computer vision: A review
Open accessAbstract
In agriculture science, automation increases the quality, economic growth and productivity of the country. The export market and quality evaluation are affected by assorting of fruits and vegetables. The crucial sensory characteristic of fruits and vegetables is appearance that impacts their market value, the consumer’s preference and choice. Although, the sorting and grading can be done by human but it is inconsistent, time consuming, variable, subjective, onerous, expensive and easily influenced by surrounding. Hence, an astute fruit grading system is needed. In recent years, various algorithms for sorting and grading are done by various researchers using computer vision. This paper presents a detailed overview of various methods i.e. preprocessing, segmentation, feature extraction, classification which addressed fruits and vegetables quality based on color, texture, size, shape and defects. In this paper, a critical comparison of different algorithm proposed by researchers for quality inspection of fruits and vegetables has been carried out.
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Cites
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Cited by
Machine learning in agriculture domain: A state-of-art survey
Meshram, V., Patil, K., Meshram, V., Hanchate, D., & Ramkteke, S. D. (2021) · Artificial Intelligence in the Life Sciences · doi:10.1016/j.ailsci.2021.100010 · Cited by 367
Metadata
- Authors
- Bhargava, A., & Bansal, A.
- Venue
- Journal of King Saud University - Computer and Information Sciences
- Published
- 2021-03
- Volume
- 33
- Issue
- 3
- Pages
- 243-257
- ISSN
- 1319-1578 (ISSN-L)
- OpenAlex
- W2807383083
- Cited by
- 631
- References
- 48
- FWCI
- 29.74
- Type
- journal article
- Language
- en
- Open access
- hybrid
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- Bhargava, A., & Bansal, A. (2021). Fruits and vegetables quality evaluation using computer vision: A review. Journal of King Saud University - Computer and Information Sciences. https://doi.org/10.1016/j.jksuci.2018.06.002
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