A Survey of Seed Quality Analysis Using CNN
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2024
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| author | B. Deepika Dr. N. Shanmugapriya |
| author_facet | B. Deepika Dr. N. Shanmugapriya |
| contents | Grain is the primary crop that our country grows to increase agricultural income. The majority of grains on the planet are rice, wheat, and maize. These grains contain a number of impurities, such as stones, weed seeds, chaff, damaged seeds, etc. Grain quality assessment requires a big human workforce and low degrees of automation. It also lengthens and raises the cost of the testing procedure. As import and export trade grows, this conflict becomes more and more apparent. Grain handling techniques require a variety of grain varieties and their qualities before moving on to the next step. Digital image processing is a non-destructive method that is also very convenient and affordable, in contrast to the chemical approach. This paper presented a grain classification system based on machine learning and image processing algorithms to recognize quality of grains and assess the purity of grains. Techniques for image processing, segmentation, and feature extraction are used on the collected images and using the parameters like major axis length, minor axis length, area, and it also determines the purity of the grain. |
| format | Recurso digital |
| id | zenodo_https___doi_org_10_5281_zenodo_18139032 |
| institution | Zenodo |
| language | |
| publishDate | 2024 |
| publisher | Zenodo |
| record_format | zenodo |
| spellingShingle | A Survey of Seed Quality Analysis Using CNN B. Deepika Dr. N. Shanmugapriya Agriculture Classification Convolution Neural Network Prediction Seed quality Grain is the primary crop that our country grows to increase agricultural income. The majority of grains on the planet are rice, wheat, and maize. These grains contain a number of impurities, such as stones, weed seeds, chaff, damaged seeds, etc. Grain quality assessment requires a big human workforce and low degrees of automation. It also lengthens and raises the cost of the testing procedure. As import and export trade grows, this conflict becomes more and more apparent. Grain handling techniques require a variety of grain varieties and their qualities before moving on to the next step. Digital image processing is a non-destructive method that is also very convenient and affordable, in contrast to the chemical approach. This paper presented a grain classification system based on machine learning and image processing algorithms to recognize quality of grains and assess the purity of grains. Techniques for image processing, segmentation, and feature extraction are used on the collected images and using the parameters like major axis length, minor axis length, area, and it also determines the purity of the grain. |
| title | A Survey of Seed Quality Analysis Using CNN |
| topic | Agriculture Classification Convolution Neural Network Prediction Seed quality |
| url | https://doi.org/10.5281/zenodo.18139032 |