ABSTRACT
The need for agricultural output to support expanding populations has increased, which has prompted the creation of creative solutions to problems like weed infestation. We provide an autonomous weed detection and eradication system to increase productivity and efficiency in agricultural fields. The system combines cutting edge sensor technology installed on a mobile robotic platform, such as computer vision and machine learning algorithms. It accurately and precisely detects undesired plants by using high resolution cameras and image processing algorithms. The real time classification of weeds among crops is made possible by deep learning models trained on large datasets. The robotic platform uses a mechanical technique for targeted weed removal after it detects it. Weeders and other precision tools carefully pull weeds while causing the least disruption to nearby crops. To guarantee that the agricultural area is covered with the least amount of human participation, the system works independently and uses an ultrasonic approach to direct the device. Connecting to cloud-based platforms makes it easier to administer and monitor operations remotely, giving farmers access to real time data. Field tests have shown the system's efficacy and efficiency, with notable decreases in weed populations while maintaining crop health and output. The system's ability to function independently lowers labor costs and operating expenses, which makes it a financially feasible option for extensive agricultural uses.
