Summary
Conventional weed control methods, reliant on machinery and/or herbicide application, often incurred substantial expenses and yielded imprecise results. An innovative specialised weed control robotic method for accurate and minimal herbicide use is proposed to tackle these issues. Implementing robotic herbicide spraying, weed removal, and incorporation mechanisms along with the image recognition algorithm were introduced, leveraging intelligent automation to reduce costs and environmental hazards. Through image processing, weeds were pointed out and targeted for control in the rice field. A YOLOv5 machine learning framework underwent training using relevant datasets to facilitate precise weed management. The AI-driven robotic system, incorporating advanced image recognition capabilities, e
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