Agricultural_Weeding_Robot

Weed management is a critical challenge in agriculture, impacting crop yield and quality. Traditional methods, such as manual labor and chemical herbicides, are often inefficient and environmentally harmful.

This project addresses this issue by developing an autonomous weeding robot that utilizes machine learning and robotic technologies to effectively detect and remove weeds.

METHODOLOGY

The robot employs a CNN model trained on a dataset of weed and crop images for accurate weed detection. A Arduino-based system controls robot movement, weed cutting mechanism, and communication. Bluetooth enables remote control and monitoring via a mobile app.

GOALS

Develop a cost-effective and scalable weeding solution

Reduce reliance on manual labor and chemical herbicides

Improve crop productivity and quality

Achieve high weed detection and removal accuracy

RESULTS

The robot demonstrated effective weed detection (95% accuracy) and removal (90% efficiency) in field tests. It successfully reduced manual labor and herbicide usage while minimizing crop damage.

CONCLUSION

The Agriculture Weeding Robot effectively addresses the challenges of traditional weed management by combining machine learning and robotics. The robot accurately detects and removes weeds, significantly reducing manual labor and herbicide use. Field trials demonstrate its potential to enhance crop yield and contribute to sustainable agriculture. While this research represents a promising step forward, future improvements, such as adaptability to diverse conditions and advanced machine learning integration, are necessary to fully realize the robot's potential for widespread agricultural application.