A Fog-based Smart Agriculture System to Detect Animal Intrusion

TL;DR

This paper introduces a fog-based smart agriculture system utilizing edge computing and LoRa communication to detect animal intrusions efficiently, cost-effectively, and with low latency, addressing rural connectivity challenges.

Contribution

It presents a novel fog-based infrastructure with sensor layouts and algorithms for early animal intrusion detection in rural farms, improving response time and reducing costs.

Findings

Effective animal intrusion detection within seconds

Lower system costs compared to existing solutions

Accurate prediction of animal locations

Abstract

Smart agriculture is one of the most promising areas where IoT-enabled technologies have the potential to substantially improve the quality and quantity of the crops and reduce the associated operational cost. However, building a smart agriculture system presents several challenges, including high latency and bandwidth consumption associated with cloud computing, frequent Internet disconnections in rural areas, and the need to keep costs low for farmers. This paper presents an end-to-end, fog-based smart agriculture infrastructure that incorporates edge computing and LoRa-based communication to address these challenges. Our system is deployed to transform traditional agriculture land of rural areas into smart agriculture. We address the top concern of farmers - animals intruding - by proposing a solution that detects animal intrusion using low-cost PIR sensors, cameras, and computer vision. In particular, we propose three different sensor layouts and a novel algorithm for predicting animals' future locations. Our system can detect animals before they intrude into the field, identify them, predict their future locations, and alert farmers in a timely manner. Our experiments show that the system can effectively and quickly detect animal intrusions while maintaining a much lower cost than current state-of-the-art systems.