{
    "ok": true,
    "doi": "10.46243/jst.2024.v9.i11.pp01-20",
    "doi_display": "10.46243/jst.2024.v9.i11.pp01-20",
    "doi_url": "https://doi.org/10.46243/jst.2024.v9.i11.pp01-20",
    "state": "registered",
    "url": "https://www.jst.org.in/index.php/pub/article/view/1063",
    "title": "SMART CROP PROTECTION SYSTEM USING DEEP LEARNING",
    "version": 2,
    "registered_via": "crossref",
    "prefix": {
        "prefix": "10.46243",
        "status": "live"
    },
    "registrant": {
        "name": "Longman Publishers",
        "kind": "publisher",
        "country": "India"
    },
    "reserved_at": null,
    "registered_at": "2026-09-29 22:00:41",
    "updated_at": "2026-09-30 00:00:01",
    "withdrawn_at": null,
    "withdrawn_reason": null,
    "record": {
        "format": "smartscholars-doi-metadata/1.0",
        "doi": "10.46243/jst.2024.v9.i11.pp01-20",
        "referent": "Creation",
        "type": "JournalArticle",
        "structural_type": "Digital",
        "modes": [
            "Visual"
        ],
        "characters": [
            "Language"
        ],
        "titles": [
            {
                "value": "SMART CROP PROTECTION SYSTEM USING DEEP LEARNING",
                "type": "PrincipalTitle",
                "lang": "en"
            }
        ],
        "identifiers": [
            {
                "type": "DOI",
                "value": "10.46243/jst.2024.v9.i11.pp01-20"
            }
        ],
        "agents": [
            {
                "role": "author",
                "name": {
                    "given": "",
                    "family": "G G. Jyothi"
                },
                "sequence": "first"
            },
            {
                "role": "author",
                "name": {
                    "given": "",
                    "family": "M.Anilkumar"
                },
                "sequence": "additional"
            },
            {
                "role": "author",
                "name": {
                    "given": "",
                    "family": "G.Apoorva"
                },
                "sequence": "additional"
            },
            {
                "role": "author",
                "name": {
                    "given": "",
                    "family": "B.Manikanta"
                },
                "sequence": "additional"
            },
            {
                "role": "author",
                "name": {
                    "given": "",
                    "family": "M.Dhanraj"
                },
                "sequence": "additional"
            },
            {
                "role": "publisher",
                "name": {
                    "org": "Longman Publishers"
                }
            }
        ],
        "dates": {
            "published": "2024",
            "date_type": "PublicationDate",
            "online": "2024"
        },
        "language": "en",
        "container": {
            "type": "Journal",
            "titles": [
                {
                    "value": "Journal of Science & Technology",
                    "type": "PrincipalTitle"
                }
            ],
            "identifiers": [
                {
                    "type": "ISSN",
                    "value": "2456-5660",
                    "medium": "electronic"
                }
            ],
            "volume": "09",
            "issue": "11",
            "pages": {
                "first": "01",
                "last": "20"
            }
        },
        "links": [
            {
                "url": "https://www.jst.org.in/index.php/pub/article/view/1063",
                "return_type": "text/html",
                "primary": true
            },
            {
                "url": "https://www.jst.org.in/index.php/pub/article/download/1063/924",
                "purpose": "text-mining",
                "return_type": "application/pdf"
            },
            {
                "url": "https://www.jst.org.in/index.php/pub/article/download/1063/4021",
                "purpose": "text-mining",
                "return_type": "application/xml"
            },
            {
                "url": "https://www.jst.org.in/index.php/pub/article/view/1063/924",
                "purpose": "similarity-checking"
            }
        ],
        "abstract": {
            "value": "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.",
            "lang": "en"
        },
        "license": {
            "url": "https://creativecommons.org/licenses/by/4.0/",
            "start": "2024-01-01",
            "applies_to": "vor"
        },
        "references": [
            {
                "key": "ref1",
                "unstructured": "M. A. Hossain, A. Haque, and M. Z. Hossain, “Smart agriculture monitoring and animal intrusion detection using AI,” Journal of Agricultural Informatics, vol. 11, no. 2, pp. 35- 42, 2021"
            },
            {
                "key": "ref2",
                "unstructured": "J. K. Patel and M. C. Gupta, “AI-based animal detection for crop protection,” IEEE Access, vol. 9, pp. 47538-47550, 2020"
            },
            {
                "key": "ref3",
                "unstructured": "R. Sharma and S. Kumar, “The role of IoT and AI in modern farming,” International Journal of Emerging Trends in Engineering Research, vol. 8, no. 4, pp. 1192-1201, 2020"
            },
            {
                "key": "ref4",
                "unstructured": "S. N. Singh and V. Singh, “A review on AIdriven solutions for smart farming,” Agricultural Engineering International: CIGR Journal, vol. 21, no. 3, pp. 105-117, 2019"
            },
            {
                "key": "ref5",
                "unstructured": "A. P. Mahesh and K. S. Rao, “Machine learning approaches for animal intrusion detection and crop protection,” International Journal of Advanced Science and Technology, vol. 29, no. 5, pp. 4025-4036, 2020"
            },
            {
                "key": "ref6",
                "unstructured": "P. Zhang and D. Huang, “Application of AI in sustainable agriculture: A comprehensive review,” Sustainable Computing: Informatics and Systems, vol. 32, pp. 100524, 2022"
            },
            {
                "key": "ref7",
                "unstructured": "M. Chakraborty and P. Ghosh, “AI and IoT integration in smart crop protection systems,” Advances in Science, Technology and Engineering Systems Journal, vol. 6, no. 1, pp. 234-240, 2021"
            },
            {
                "key": "ref8",
                "doi": "10.1002/9781394175376.ch12",
                "unstructured": "Swathi, A., V. Swathi, Shilpa Choudhary, and Munish Kumar. “Wearable Gait Authentication: A Framework for Secure User Identification in Healthcare.” Optimized Predictive Models in Healthcare Using Machine Learning (2024): 195-214"
            },
            {
                "key": "ref9",
                "doi": "10.1002/9781394175512.ch8",
                "unstructured": "Gowroju, Swathi, Shilpa Choudhary, Sandhya Raajaani, and Regula Srilakshmi. “Semantic Segmentation of Aerial Images Using Pixel Wise Segmentation.” Advances in Aerial Sensing and Imaging (2024): 145-164"
            },
            {
                "key": "ref10",
                "doi": "10.1002/9781394175512.ch11",
                "unstructured": "Gowroju, Swathi, Shilpa Choudhary, Medipally Rishitha, Singanaboina Tejaswi, Lankala Shashank Reddy, and Mallepally Sujith Reddy. “Drone-Assisted Image Forgery Detection Using Generative Adversarial Net-Based Module.” Advances in Aerial Sensing and Imaging (2024): 245-266"
            },
            {
                "key": "ref11",
                "doi": "10.4018/979-8-3693-3253-5.ch008",
                "unstructured": "Gowroju, Swathi, and Saurabh Karling. “Multinational Enterprises’ Digital Transformation, Sustainability, and Purpose: A Holistic View.” In Driving Decentralization and Disruption With Digital Technologies, pp. 108- 123. IGI Global, 2024"
            },
            {
                "key": "ref12",
                "doi": "10.1002/9781119785491.ch11",
                "unstructured": "Gowroju, Swathi, V. Swathi, and Ankita Tiwari. “Handwriting and Speech-Based Secured Multimodal Biometrics Identification Technique.” Multimodal Biometric and Machine Learning Technologies: Applications for Computer Vision (2023): 227-250"
            },
            {
                "key": "ref13",
                "doi": "10.2174/9789815124514123010008",
                "unstructured": "Gowroju, Swathi, V. Swathi, J. Narasimha Murthy, and D. Sai Kamesh. “Real-Time Object Detection and Localization for Autonomous Driving.” Handbook of Artificial Intelligence (2023): 112"
            },
            {
                "key": "ref14",
                "doi": "10.1002/9781394186570.ch6",
                "unstructured": "Gowroju, Swathi, G. Mounika, D. Bhavana, Shaik Abdul Latheef, and A. Abhilash. “Artificial Intelligence–Based Active Virtual Voice Assistant.” Explainable Machine Learning Models and Architectures (2023): 81-103"
            },
            {
                "key": "ref15",
                "doi": "10.1002/9781394168002.ch8",
                "unstructured": "Gowroju, Swathi, and N. Santhosh Ramchander. “Applications of Drones—A Review.” Drone Technology: Future Trends and Practical Applications (2023): 183-206"
            }
        ],
        "record": {
            "registrant": "Longman Publishers",
            "registered": "2026-09-24",
            "updated": "2026-09-27",
            "issue_number": 1,
            "source": "crossref-api",
            "source_agency": "Crossref (member 25296)"
        }
    },
    "record_sha256": "a6e31cb0772d276549eb211ceb6fec7b1f37ce21d28cf60e70b533a2eeee53d9",
    "handle": {
        "synced_at": "2026-10-01 18:14:05",
        "url": "https://www.jst.org.in/index.php/pub/article/view/1063"
    },
    "links": {
        "record_page": "https://registry.smartscholars.in/record.php?doi=10.46243%2Fjst.2024.v9.i11.pp01-20",
        "system_metadata": "https://registry.smartscholars.in/resolve.php?doi=10.46243%2Fjst.2024.v9.i11.pp01-20&as=system",
        "history": "https://registry.smartscholars.in/api.php?action=history&doi=10.46243%2Fjst.2024.v9.i11.pp01-20",
        "kernel_xml": "https://registry.smartscholars.in/resolve.php?doi=10.46243%2Fjst.2024.v9.i11.pp01-20&as=xml"
    }
}