Fuente: Wipo "precision agriculture" The present invention relates to an edge computing-powered precision agricultural system for resource management and real-time crop monitoring. The system comprises IoT sensors to monitor environmental parameters such as soil moisture, temperature, and humidity; cameras or drones to capture crop images for visual analysis; Convolutional Neural Networks (CNN) to analyse spatial data like crop images for disease or pest detection; and Long Short-Term Memory (LSTM) to process time-series data such as weather trends and soil moisture for predictive analytics. Edge computing optimises activities like fertilisation, irrigation, and pest identification by enabling real-time analysis of temporal data using LSTM and spatial data using Convolutional Neural Networks (CNN). The system further comprises a user friendly interface that gives farmers actionable information and real-time notifications, this method guarantees effective decision-making even in isolated locations with poor connection. The use of edge computing makes precision agriculture an economical and ecologically sustainable activity, encouraging increased yields and effective resource use by lowering bandwidth requirements and facilitating quicker reactions.