International Journal of Innovative Research in Computer and Communication Engineering
ISSN Approved Journal | Impact factor: 8.771 | ESTD: 2013 | Follows UGC CARE Journal Norms and Guidelines
| Monthly, Peer-Reviewed, Refereed, Scholarly, Multidisciplinary and Open Access Journal | High Impact Factor 8.771 (Calculated by Google Scholar and Semantic Scholar | AI-Powered Research Tool | Indexing in all Major Database & Metadata, Citation Generator | Digital Object Identifier (DOI) |
| TITLE | AI-Based Automated Weed Detection and Removal |
|---|---|
| ABSTRACT | Weed management is essential in agriculture because weeds compete with crops for water, nutrients, and sunlight, reducing yield and crop health. Traditional methods like manual weeding are labor-intensive and costly, while chemical herbicides harm the environment and human health. To address these issues, the Deep Learning Based Weed Detection and Plucking System provides an automated, eco-friendly solution using artificial intelligence and robotics. The system employs deep learning models such as CNNs or object detection algorithms like YOLOv8 to accurately distinguish weeds from crops based on features like shape, color, and texture. A camera mounted on an embedded platform such as Raspberry Pi captures real-time field images, which are processed to detect weeds with high accuracy. Once identified, a robotic arm or servo-based gripper precisely plucks the weeds using the detected coordinates, ensuring crops remain unharmed. The robot navigates the field using motors and sensors for obstacle avoidance. This system reduces labor dependency, eliminates herbicide use, supports precision agriculture, and enhances productivity while promoting sustainable farming practices [1]. |
| AUTHOR | DR. INDIRA S P, MOUNASHREE C S, NEHA A KILLEDAR, RAKSHITHA K, VIDYASHREE K S Associate Professor, Dept. of CSE., Jain Institute of Technology Davangere, India UG Students, Dept. of CSE, Jain Institute of Technology Davangere, Karnataka, India |
| VOLUME | 177 |
| DOI | DOI: 10.15680/IJIRCCE.2025.1312152 |
| pdf/152_AI-Based Automated Weed Detection and Removal.pdf | |
| KEYWORDS | |
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