A Microservice-Based Smart Agriculture System to Detect Animal Intrusion at the Edge

Jinpeng Miao (), Dasari Rajasekhar, Shivakant Mishra (), Sanjeet Kumar Nayak and Ramanarayan Yadav
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Jinpeng Miao: Department of Computer Science, University of Colorado at Boulder, Boulder, CO 80309, USA
Dasari Rajasekhar: Department of Computer Science and Engineering, Indian Institute of Information Technology, Design and Manufacturing, Chennai 600127, Tamil Nadu, India
Shivakant Mishra: Department of Computer Science, University of Colorado at Boulder, Boulder, CO 80309, USA
Sanjeet Kumar Nayak: Department of Computer Science and Engineering, Indian Institute of Information Technology, Design and Manufacturing, Chennai 600127, Tamil Nadu, India
Ramanarayan Yadav: Department of Electrical and Computer Science Engineering, Institute of Infrastructure Technology Research and Management, Ahmedabad 380026, Gujarat, India

Future Internet, 2024, vol. 16, issue 8, 1-20

Abstract: Smart agriculture stands as a promising domain for IoT-enabled technologies, with the potential to elevate crop quality, quantity, and operational efficiency. However, implementing a smart agriculture system encounters challenges such as the high latency and bandwidth consumption linked to cloud computing, Internet disconnections in rural locales, and the imperative of cost efficiency for farmers. Addressing these hurdles, this paper advocates a fog-based smart agriculture infrastructure integrating edge computing and LoRa communication. We tackle farmers’ prime concern of animal intrusion by presenting a solution leveraging low-cost PIR sensors, cameras, and computer vision to detect intrusions and predict animal locations using an innovative algorithm. Our system detects intrusions pre-emptively, identifies intruders, forecasts their movements, and promptly alerts farmers. Additionally, we compare our proposed strategy with other approaches and measure their power consumptions, demonstrating significant energy savings afforded by our strategy. Experimental results highlight the effectiveness, energy efficiency, and cost-effectiveness of our system compared to state-of-the-art systems.

Keywords: smart agriculture; animal intrusion detection; LoRa; fog computing (search for similar items in EconPapers)
JEL-codes: O3 (search for similar items in EconPapers)
Date: 2024
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