The primary objective of this research is to engineer an intelligent inter-intra row weeding robot, employing a vision-based artificial intelligence (AI) system equipped with height adjustability capabilities. The overarching aim is to confront the pressing challenges within sustainable agricultural practices by significantly reducing human intervention in the application of hazardous chemicals, ensuring timely agricultural operations, optimizing resource inputs, and facilitating both mechanical and chemical weeding procedures across diverse crop types and growth stages. Central to our methodology is the development of an AI-driven vision-guided weeding robot, an amalgamation of cutting-edge technologies including Real-Time Kinematic (RTK) Global Positioning System (GPS), a state-of-the-art 3D Light Detection and Ranging (LIDAR) sensor, and a multi-view depth camera. These integrated components work in concert to facilitate precise navigation and obstacle detection, key factors in the robot's efficient operation within agricultural fields. The adaptive height adjustment feature of the robot stands as a pivotal innovation tailored to accommodate a wide spectrum of crop varieties, dynamically maintaining an optimal distance from vegetation. This adaptive mechanism enables the weeding robot to seamlessly maneuver through diverse crop configurations, ensuring minimal interference while effectively targeting weed eradication. The multifaceted working elements of the robot operate within both inter-row and intra-row spaces, strategically designed to execute weed elimination with optimal efficiency. Leveraging the power of AI, the robot incorporates real-time weed identification capabilities, enhancing precision and accuracy in weed management practices. This real-time identification system serves as a groundbreaking advancement, refining the robot's ability to differentiate between crops and weeds, consequently facilitating more targeted and effective control measures. Ultimately, the innovation represented by this weeding robot lies in its pioneering efforts to strike a delicate balance between minimal interference with crop growth and robust, efficient weed control strategies. This research endeavors to push the boundaries of agricultural robotics, aiming to deliver a transformative solution for sustainable and precision-driven weed management in contemporary agriculture.

1.  Ambuj, & Machavaram, R. (2024). Neuromorphic computing spiking neural network edge detection model for content based image retrieval. Network: Computation in Neural Systems, 1–31. https://doi.org/10.1080/0954898X.2024.2348018

2.  Ambuj, Nagar, H., Paul, A., Machavaram, R., & Soni, P. (2024). Reinforcement learning particle swarm optimization based trajectory planning of autonomous ground vehicle using 2D LiDAR point cloud. Robotics and Autonomous Systems, 178, 104723. https://doi.org/10.1016/j.robot.2024.104723

3. Ambuj, Machavaram, R. (2024). Optimizing Energy Expenditure in Agricultural Autonomous Ground Vehicles through a GPU-Accelerated Particle Swarm Optimization-Artificial Neural Network Framework. Cleaner Energy Systems, 100130. https://doi.org/10.1016/j.cles.2024.100130

4. A. Paul, Ambuj, H. Nagar, and R. Machavaram, "Utilizing Fine-Tuned YOLOv8 Deep Learning Model for Greenhouse Capsicum Detection and Growth Stage Determination," In 2023 7th International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC), (pp. 649-656). IEEE. https://doi.org/10.1109/I-SMAC58438.2023.10290335

5. Nagar, H., Machavaram, R., Ambuj, Soni, P., Mahore, V., & Patidar, P. (2024). Cloud-driven serverless framework for generalised tractor fuel consumption prediction model using machine learning. Cogent Engineering, 11(1), 2311810.

6. Paul, A., Machavaram, R., Ambuj, Kumar, D., & Nagar, H. (2024). Smart solutions for capsicum Harvesting: Unleashing the power of YOLO for Detection, Segmentation, growth stage Classification, Counting, and real-time mobile identification. Computers and Electronics in Agriculture, 219, 108832.

7. Nagar, H., Machavaram, R., Paul, A., Soni, P., Mahore, V., & Chouriya, Ambuj (2023, November). A Data-Driven Approach to Forecast Engine Torque of an Agricultural Tractor Across Varied Operational Range Using Machine Learning. In 2023 2nd International Conference on Futuristic Technologies (INCOFT) (pp. 1-7). IEEE.