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@article{197448,
        author = {PRASANTH T and SAKTHIVEL R and SARATHI G and KEERTHANA G},
        title = {A Cognitive Vision-Based Animal Intrusion Prevention System for Next-Generation Smart Agriculture},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {12},
        number = {11},
        pages = {7575-7581},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=197448},
        abstract = {New technologies such as artificial intelligence (AI) and deep learning in agriculture have led to precision farming being radically transformed (new technology). An innovative approach utilizing AI-driven detection/response systems to reduce crop loss from wildlife damage is the introduction of the Animal Repellent System for Smart Farming (ARSPF). The growing number of humans encroaching upon natural habitats and deforestation has caused the number of human-wildlife conflicts to escalate dramatically. Wild species such as elephants, wild boars, and deer are frequently raiding agricultural crops, leading to substantial monetary losses as well as a real threat to the safety of farmers. Farmers also traditionally employ lethal (shooting/trapping) and non-lethal (protection) means of controlling the impact of wildlife on their crops. However, these methods of wildlife management and protection are often ineffective and not adaptable. The proposed system uses edge computing, incorporating a camera to capture video images as well as DCNN software to analyze the images for the presence of wildlife, enabling real-time identification of animals based on live video footage. After detecting an animal, the system classifies it based on the species and will then initiate an Animal Repelling Module that will emit species-specific ultrasonic sounds to repel the animal without injury or harming it. The use of AI as demonstrated through the proposed system is an innovative way to provide an effective, humane, and sustainable approach to crop protection and enhancing the coexistence of wildlife and production agriculture.},
        keywords = {Artificial Intelligence, Deep Learning, Precision Farming, Crop Protection, Wildlife Repellent, DCNN, Edge Computing, Human-Wildlife Conflict},
        month = {April},
        }