Cite this DOI

G G. Jyothi, M.Anilkumar, G.Apoorva, B.Manikanta, & M.Dhanraj (2024). SMART CROP PROTECTION SYSTEM USING DEEP LEARNING. *Journal of Science & Technology*, *09*(11), 01–20. https://doi.org/10.46243/jst.2024.v9.i11.pp01-20

@article{ggjyothi2024smart,
  author    = {G G. Jyothi and M.Anilkumar and G.Apoorva and B.Manikanta and M.Dhanraj},
  title     = {{SMART CROP PROTECTION SYSTEM USING DEEP LEARNING}},
  journal   = {Journal of Science \& Technology},
  year      = {2024},
  volume    = {09},
  number    = {11},
  pages     = {01--20},
  publisher = {Longman Publishers},
  issn      = {2456-5660},
  doi       = {10.46243/jst.2024.v9.i11.pp01-20},
  url       = {https://doi.org/10.46243/jst.2024.v9.i11.pp01-20},
  language  = {en},
  abstract  = {Agriterrorism with regard to animal damage greatly affects the crop yield for farmers, resulting to some of them recording large losses. Farm animals like buffaloes, cows, goats and birds trespass in the fields trample the crops and this can only be destructive for farmers since they cannot constantly protect their shambas. Measures such as the use of barriers, wire fences, or personnel vigilance yield most of the time insufficient results. In addition to scarecrows, which enemies can easily bypass with many animals, farmers also employ human effigies.To control these problems, we introduce an AI-based Scarecrow system using video processing in real- time for crop protection from wildlife. The system uses a camera to record videos and analyzes them with YOLOv3, an object detection model together with OpenCV and the COCO names database. If any animal or bird is identified, then the system produces a sound alerting the animal not to invade the compound. Moreover, if an animal has been sensed for more than one minute consecutively, the system will alert the farmer sending him/her an e-mail and dialing the farmer`s phone number. This approach thus provides an efficient and automated way of protecting crops than depending on deterrent measures.}
}

TY  - JOUR
TI  - SMART CROP PROTECTION SYSTEM USING DEEP LEARNING
AU  - G G. Jyothi
AU  - M.Anilkumar
AU  - G.Apoorva
AU  - B.Manikanta
AU  - M.Dhanraj
JO  - Journal of Science & Technology
PY  - 2024
DA  - 2024///
VL  - 09
IS  - 11
SP  - 01
EP  - 20
PB  - Longman Publishers
SN  - 2456-5660
LA  - en
AB  - Agriterrorism with regard to animal damage greatly affects the crop yield for farmers, resulting to some of them recording large losses. Farm animals like buffaloes, cows, goats and birds trespass in the fields trample the crops and this can only be destructive for farmers since they cannot constantly protect their shambas. Measures such as the use of barriers, wire fences, or personnel vigilance yield most of the time insufficient results. In addition to scarecrows, which enemies can easily bypass with many animals, farmers also employ human effigies.To control these problems, we introduce an AI-based Scarecrow system using video processing in real- time for crop protection from wildlife. The system uses a camera to record videos and analyzes them with YOLOv3, an object detection model together with OpenCV and the COCO names database. If any animal or bird is identified, then the system produces a sound alerting the animal not to invade the compound. Moreover, if an animal has been sensed for more than one minute consecutively, the system will alert the farmer sending him/her an e-mail and dialing the farmer`s phone number. This approach thus provides an efficient and automated way of protecting crops than depending on deterrent measures.
DO  - 10.46243/jst.2024.v9.i11.pp01-20
UR  - https://doi.org/10.46243/jst.2024.v9.i11.pp01-20
ER  -

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    "type": "article-journal",
    "id": "10.46243/jst.2024.v9.i11.pp01-20",
    "DOI": "10.46243/jst.2024.v9.i11.pp01-20",
    "URL": "https://doi.org/10.46243/jst.2024.v9.i11.pp01-20",
    "title": "SMART CROP PROTECTION SYSTEM USING DEEP LEARNING",
    "source": "Smart Scholars DOI Registry",
    "container-title": "Journal of Science & Technology",
    "author": [
        {
            "family": "G G. Jyothi"
        },
        {
            "family": "M.Anilkumar"
        },
        {
            "family": "G.Apoorva"
        },
        {
            "family": "B.Manikanta"
        },
        {
            "family": "M.Dhanraj"
        }
    ],
    "issued": {
        "date-parts": [
            [
                2024
            ]
        ]
    },
    "volume": "09",
    "issue": "11",
    "page": "01-20",
    "publisher": "Longman Publishers",
    "language": "en",
    "abstract": "Agriterrorism with regard to animal damage greatly affects the crop yield for farmers, resulting to some of them recording large losses. Farm animals like buffaloes, cows, goats and birds trespass in the fields trample the crops and this can only be destructive for farmers since they cannot constantly protect their shambas. Measures such as the use of barriers, wire fences, or personnel vigilance yield most of the time insufficient results. In addition to scarecrows, which enemies can easily bypass with many animals, farmers also employ human effigies.To control these problems, we introduce an AI-based Scarecrow system using video processing in real- time for crop protection from wildlife. The system uses a camera to record videos and analyzes them with YOLOv3, an object detection model together with OpenCV and the COCO names database. If any animal or bird is identified, then the system produces a sound alerting the animal not to invade the compound. Moreover, if an animal has been sensed for more than one minute consecutively, the system will alert the farmer sending him/her an e-mail and dialing the farmer`s phone number. This approach thus provides an efficient and automated way of protecting crops than depending on deterrent measures.",
    "ISSN": "2456-5660"
}

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.2024.v9.i11.pp01-20 gives all four in one JSON answer.