Cite this DOI

A. Caroline, A. C. (2022). DEVELOPMENT OF A MACHINE AND DETECTION OF GLYCOALKALOIDS IN POTATO USING IMAGE PROCESSING TECHNIQUE. *Journal of Science & Technology*, *7*(2), 315–321. https://doi.org/10.46243/jst.2022.v7.i02.pp315-321

@article{acaroline2022development,
  author    = {A. Caroline, A. Caroline},
  title     = {{DEVELOPMENT OF A MACHINE AND DETECTION OF GLYCOALKALOIDS IN POTATO USING IMAGE PROCESSING TECHNIQUE}},
  journal   = {Journal of Science \& Technology},
  year      = {2022},
  month     = {jul},
  volume    = {7},
  number    = {2},
  pages     = {315--321},
  publisher = {Longman Publishers},
  issn      = {2456-5660},
  doi       = {10.46243/jst.2022.v7.i02.pp315-321},
  url       = {https://doi.org/10.46243/jst.2022.v7.i02.pp315-321},
  language  = {en},
  abstract  = {Glycoalkaloids are secondary natural poisonous metabolites produced by plants of the Solanaceae family. Glycoalkaloids from solanaceous plants vary depending on species. The two major glycoalkaloids found in potatoes are Solanine and chaconine. The average potato contains 0.075 mg of solanine and chaconine. The doses of 200–400 mg for adult humans and 20–40 mg for children can cause toxic symptoms to human health. Commercial potatoes have GA content of less than 0.2 mg. This research aimed at determining the total glycoalkaloid content present in potatoes using image processing technique. It is one of the non-destructive method and images can be stored and retrieved easily. Potatoes were collected from local markets in Coimbatore. The major components used in this machine are Raspberry Pi, web camera, Lcd module, memory card. The Raspberry pi is powered up with 5V power supply through USB cable. The Button interfaced with raspberry pi is triggered to capture the image from camera to classify the level of glycoalkaloids and display the result in LCD module. Through this glycoalkaloid detector the amount of glycoalkaloid present in the potatoes can be determined by both milligram and percentage values. It is a cost-effective method and ensures food safety. Published by: Longman Publishers www.jst.org.in P age 315 | 7 www.jst.org.in DOI: https://doi.org/10.46243/jst.2022.v7.i02.pp315-321}
}

TY  - JOUR
TI  - DEVELOPMENT OF A MACHINE AND DETECTION OF GLYCOALKALOIDS IN POTATO USING IMAGE PROCESSING TECHNIQUE
AU  - A. Caroline, A. Caroline
JO  - Journal of Science & Technology
PY  - 2022
DA  - 2022/07/03/
VL  - 7
IS  - 2
SP  - 315
EP  - 321
PB  - Longman Publishers
SN  - 2456-5660
LA  - en
AB  - Glycoalkaloids are secondary natural poisonous metabolites produced by plants of the Solanaceae family. Glycoalkaloids from solanaceous plants vary depending on species. The two major glycoalkaloids found in potatoes are Solanine and chaconine. The average potato contains 0.075 mg of solanine and chaconine. The doses of 200–400 mg for adult humans and 20–40 mg for children can cause toxic symptoms to human health. Commercial potatoes have GA content of less than 0.2 mg. This research aimed at determining the total glycoalkaloid content present in potatoes using image processing technique. It is one of the non-destructive method and images can be stored and retrieved easily. Potatoes were collected from local markets in Coimbatore. The major components used in this machine are Raspberry Pi, web camera, Lcd module, memory card. The Raspberry pi is powered up with 5V power supply through USB cable. The Button interfaced with raspberry pi is triggered to capture the image from camera to classify the level of glycoalkaloids and display the result in LCD module. Through this glycoalkaloid detector the amount of glycoalkaloid present in the potatoes can be determined by both milligram and percentage values. It is a cost-effective method and ensures food safety. Published by: Longman Publishers www.jst.org.in P age 315 | 7 www.jst.org.in DOI: https://doi.org/10.46243/jst.2022.v7.i02.pp315-321
DO  - 10.46243/jst.2022.v7.i02.pp315-321
UR  - https://doi.org/10.46243/jst.2022.v7.i02.pp315-321
ER  -

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    "URL": "https://doi.org/10.46243/jst.2022.v7.i02.pp315-321",
    "title": "DEVELOPMENT OF A MACHINE AND DETECTION OF GLYCOALKALOIDS IN POTATO USING IMAGE PROCESSING TECHNIQUE",
    "source": "Smart Scholars DOI Registry",
    "container-title": "Journal of Science & Technology",
    "author": [
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                2022,
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    "publisher": "Longman Publishers",
    "language": "en",
    "abstract": "Glycoalkaloids are secondary natural poisonous metabolites produced by plants of the Solanaceae family. Glycoalkaloids from solanaceous plants vary depending on species. The two major glycoalkaloids found in potatoes are Solanine and chaconine. The average potato contains 0.075 mg of solanine and chaconine. The doses of 200–400 mg for adult humans and 20–40 mg for children can cause toxic symptoms to human health. Commercial potatoes have GA content of less than 0.2 mg. This research aimed at determining the total glycoalkaloid content present in potatoes using image processing technique. It is one of the non-destructive method and images can be stored and retrieved easily. Potatoes were collected from local markets in Coimbatore. The major components used in this machine are Raspberry Pi, web camera, Lcd module, memory card. The Raspberry pi is powered up with 5V power supply through USB cable. The Button interfaced with raspberry pi is triggered to capture the image from camera to classify the level of glycoalkaloids and display the result in LCD module. Through this glycoalkaloid detector the amount of glycoalkaloid present in the potatoes can be determined by both milligram and percentage values. It is a cost-effective method and ensures food safety. Published by: Longman Publishers www.jst.org.in P age 315 | 7 www.jst.org.in DOI: https://doi.org/10.46243/jst.2022.v7.i02.pp315-321",
    "ISSN": "2456-5660"
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From the record as registered (version 2) — the record and its history. Programs: https://registry.smartscholars.in/api.php?action=cite&doi=10.46243%2Fjst.2022.v7.i02.pp315-321 gives all four in one JSON answer.