Autonomous Weed Detection for Precision Spraying Using Deep Learning

The emergence of artificial intelligence (AI) and Internet of Things (IoT) technologies is revolutionizing agricultural practices by facilitating smarter and more efficient methods of farming. This manuscript delineates the creation of an autonomous robotic system specifically engineered for precise weed detection and the selective application of herbicides within agricultural domains. The proposed system integrates a robotic vehicle outfitted with a deep learning-based vision apparatus to identify weeds, alongside a selective spraying mechanism that exclusively targets undesirable flora, thereby diminishing herbicide consumption and lessening environmental repercussions. The robotic vehicle operates autonomously within the agricultural landscape via waypoint navigation, concurrently capturing real-time video through an affixed camera, which is subsequently analyzed to detect and pinpoint weeds. By utilizing AI-driven inference outcomes, the selective spraying mechanism is activated solely upon the identification of weeds, thereby enhancing resource efficiency and alleviating manual labor requirements. This methodology offers a scalable and sustainable alternative to conventional weed management strategies, thereby assisting farmers in curtailing expenses while augmenting field productivity. Empirical field trials substantiate the efficacy of the system in autonomously traversing diverse agricultural settings and executing precise weed spraying with accuracy. Key Words: : Autonomous robotic vehicle, precision agriculture, weed detection, deep learning, IoT, selective spraying, waypoint navigation, herbicide reduction, smart farming etc.