Effective weed detection is essential in modern agriculture to improve crop yield and quality. Farmers can optimize their weed control strategies by applying tailored herbicides based on accurate identification of weed species and the areas they affect. Real-time object detection has been transformed by recent advances in image detection technology, especially the YOLO (You Only Look Once) algorithm, of which YOLOv7 has shown to be more accurate than its predecessors in weed detection. Because of its novel E-ELAN layer, the YOLOv7 model achieves an astonishing 97% accuracy, compared to the estimated 78% accuracy of the YOLOv5 model. cropThis study suggests using Internet of Things (IoT) sensors in conjunction with YOLOv7 to improve weed detection using an integrated strategy. It is advantageous to include a variety of sensors in the proposed work in detecting and managing weeds with greater accuracy and comprehensiveness can be achieved by combining a variety of sensors to improve the data obtained. An enhanced weed detection system can be achieved by utilizing the distinct information that each type of sensor provides. A comprehensive set of environmental data, including soil moisture, temperature and humidity, light intensity, pH, and ultrasonic distance sensors, will be used to correlate with patterns of weed growth. This information will be sent to a central Internet of Things gateway for in-the-moment analysis and merging with video footage taken agricultural fields.
Farmers can anticipate weed infestations and optimize their management tactics thanks to predictive analytics made possible by the integration of sensor data with YOLOv7's weed detecting capabilities. The potential for large herbicide application cost savings and improved crop yields, which would increase farmer profits, highlight the economic viability of this strategy. This methodology seeks to revolutionize weed control procedures by utilizing cutting-edge technology and IoT connectivity, making them more effective and efficient .