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@article{198087,
        author = {Sayali Ramesh Yadav and Pratiksha Ubale and Abhijeet Yadgire and Mr.Sugat Pawar},
        title = {IoT-Based Autonomous Pesticide Sprinkling Rover Using Hybrid Computer Vision and Edge Computing},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {12},
        number = {11},
        pages = {9268-9274},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=198087},
        abstract = {Traditional methods of spraying pesticides often use too much chemical, which increases costs for farmers and harms the environment and people's health. This study introduces an IoT-powered autonomous rover designed for precise pesticide application in agriculture, offering a solution to these challenges. There are two controllers in the system. An ESP32-CAM module captures and handles real-time images, and an Arduino Uno manages movement and direction. A mixed computer vision approach combines Canny edge detection and HSV color space segmentation to find areas with crops and spot unhealthy leaves. Blob analysis helps tell the difference between signs of disease and natural aging in leaves, which makes it easier to judge how serious an infection is. Based on this evaluation, a customized micro-spraying method increases spraying efficiency and reduces unnecessary chemical exposure by applying pesticide only to the affected areas. The recommended technique successfully protects crops while allowing precise spraying with reduced pesticide usage, as shown by experiments done in controlled field settings. Decisions can be made instantly without the need for constant cloud connection when edge processing is integrated with IoT connectivity.},
        keywords = {Precision agriculture, IoT, autonomous agricultural rover, computer vision, edge image processing, selective pesticide spraying, leaf disease detection.},
        month = {April},
        }