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@article{203510,
        author = {Mr. Mayuresh B Shinde and Dr. Bhuvaneshwar D Patil and Dr. Anjali J Joshi and Mr. Akshay B Abhang},
        title = {Literature Review on AI-Powered Autonomous Weed Detection and Removal Robot},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {12},
        number = {12},
        pages = {11508-11518},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=203510},
        abstract = {Weed control remains a significant challenge in modern agriculture, often addressed through labor-intensive manual methods or the excessive use of chemical herbicides, both of which possess severe environmental and economic drawbacks. This review paper presents a comprehensive analysis of an innovative solution: an AI-powered autonomous robot designed to detect and remove weeds with high precision. Utilizing localized and cloud-integrated embedded systems, such as the ESP32-CAM module for real-time image capture and Convolutional Neural Networks (CNNs) alongside YOLO architectures for classification, the proposed systems identify weeds and actuate precise mechanical arms or micro-sprayers for targeted elimination.  The system paradigm emphasizes low-cost hardware, modular design, and sustainable farming practices, rendering it exceptionally viable for small to medium-sized agricultural holdings. By integrating artificial intelligence, advanced robotics, and wireless communication protocols, this survey explores how modern intelligent automation minimizes chemical runtime, optimizes agricultural yield, and eliminates human labor dependency, paving the multi-disciplinary path toward sustainable precision agriculture.},
        keywords = {Weed Control, Precision Agriculture, AI-Powered Autonomous Robot, ESP32-CAM Module, Convolutional Neural Networks (CNNs), YOLO, Micro-Actuators, Automation, Sustainable Farming.},
        month = {May},
        }