Edge Computing to Enable Front-End Data Processing Capabilities in Smart Agriculture and Forestry

As a typical branch of distributed computing, edge computing plays an important role by shifting the computation unit from centralized servers to the local network end, thereby reducing resource consumption and alleviating network load. It allows data to be processed locally upon acquisition, without relying on unstable or limited infrastructure, making it an ideal solution for rural and remote areas, particularly in agro-forestry scenarios. Furthermore, AI and machine learning (AI/ML) techniques offer promising capabilities for accurate identification and prediction. To achieve lower redundancy, enable on-demand data analytics and optimal decision-making, this paper focuses on two key areas: IoT data processing/filtering and imagery-based object recognition/counting, with the purpose of digitalization and automation in the field. The performance results prove that the proposed edge solutions effectively reduce unnecessary data volume, achieving a false positive rate of only 1.03 % in data filtering tests and an 82 % decrease in imagery detection tests. These solutions capture only the relevant information or objects, enabling accurate and in-depth analysis based on user needs.