Automated Weed Identification And Classification Using Artificial Intelligence

In contemporary agriculture, effective weed management is paramount for maximizing crop yield and mitigating environmental impact. This paper introduces an innovative automated solution for weed identification and classification, utilizing the YOLOv8m algorithm (You Only Look Once version 8 with mobile architecture). Recognized for its real-time object detection capabilities YOLOv8m has been specifically designed and refined to enable quick and precise identification of weeds in agricultural areas. The system employs a comprehensive dataset encompassing diverse weed species, growth stages, and environmental conditions to train the YOLOv8m model. Its real-time image processing capability enhances its suitability for dynamic agricultural settings. The YOLOv8m architecture is fine-tuned to identify and classify weeds rapidly, providing a reliable on-the-fly decision-making solution. The workflow includes image acquisition with various sensors, pre-processing for noise reduction, and training the model to recognize intricate features associated with different weed species. The resulting model seamlessly integrates into a user-friendly application compatible with precision agriculture technologies, empowering farmers to make informed decisions about weed management. Field trials attest to the YOLOv8mbased system’s efficacy in accurately identifying weeds and distinguishing them from crops. The algorithm’s versatility makes it adaptable for deployment across diverse agricultural landscapes. Integrating this automated weed identification system into precision farming practices not only reduces reliance on manual labor but also promotes sustainable agriculture by minimizing herbicide use. The research findings underscore YOLOv8m’s potential as a potent tool for automated weed identification, paving the way for advanced, technology-driven solutions in precision agriculture. Its application in weed management signifies a significant stride toward optimizing resource utilization and fostering sustainable farming practices amidst evolving agricultural needs.