Edge Computing to Enable Front-End Data Processing Capabilities in Smart Agriculture and Forestry [* Cross-Reference *]

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.