Deployment of an Artificial Intelligent Robot for Weed Management in Legumes Farmland
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Abstract
This groundbreaking research introduces an AI-based approach for revolutionizing weed management in legume farmland, addressing the limitations of traditional methods and introducing a new era of cost-effective and precise weed detection and removal. Traditional methods of removing weeds from farmland involving machinery or chemicals often resulted in high costs and imprecise outcomes. To address these challenges, an advanced image recognition algorithm was proposed, which harnessed smart machines to minimize costs and environmental risks. By utilizing computer vision technology, weeds were accurately identified and targeted for removal. A machine learning model was trained using relevant datasets to enable precise weed management. The AI-powered robot, equipped with advanced image recognition algorithms, demonstrated exceptional accuracy and speed, performing weed removal and decomposition 1.2 times faster than traditional manual labour. This breakthrough in weed management technology offers farmers a means to optimize crop yields, enhance food production, and minimize the environmental impact associated with chemical herbicides. A prototype of the robot was fabricated and evaluated in real-world farming conditions. Field tests were conducted on a bean farm and it’s demonstrated the robot's exceptional accuracy, with only a 2% deviation from the actual weed quantity. This research showcased the potential of AI-based weed management systems in legume farming, offering cost-effective and precise weed detection and removal. This research sets a precedent for the integration of AI in modern agriculture, driving the industry toward a more environmentally conscious and economically viable future. The AI-based weed management system empowers farmers, ensuring bountiful harvests, increased profitability, and a greener, more sustainable tomorrow while attention should be given to manufacturing this model for industrial and or commercial applications.
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