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Research on a Low-Cost, Open-Source, and Remote Monitoring Data Collector to Predict Livestock’s Habits Based on Location and Auditory Information: A Case Study from Vietnam

Author

Listed:
  • Ha Quang Thinh Ngo

    (Department of Mechatronics Engineering, Ho Chi Minh City University of Technology (HCMUT), 268 Ly Thuong Kiet Street, District 10, Ho Chi Minh City 700000, Vietnam
    Vietnam National University Ho Chi Minh City, Linh Trung Ward, Thu Duc District, Ho Chi Minh City 700000, Vietnam)

  • Thanh Phuong Nguyen

    (HUTECH Institute of Engineering, Ho Chi Minh City University of Technology (HUTECH), Ho Chi Minh City 700000, Vietnam)

  • Hung Nguyen

    (HUTECH Institute of Engineering, Ho Chi Minh City University of Technology (HUTECH), Ho Chi Minh City 700000, Vietnam)

Abstract

The supervision and feeding of grazing livestock are always difficult missions. Since animals act based on habits, the real-time monitoring data logger has become an indispensable instrument to assist farmers in recognizing the status of livestock. Position-tracked and acoustic monitoring have become commonplace as two of the best methods to characterize feeding performance in ruminants. Previously, the existing methods were limited to desktop computers and lacked a sound-collecting function. These restrictions impacted the late interventions from feeders and required a large-sized data memory. In this work, an open-source framework for a data collector that autonomously captures the health information of farm animals is introduced. In this portable hardware, a Wireless Location Acoustic Sensing System (WiLASS) is integrated to infer the health status through the activities and abnormal phenomena of farming livestock via chew–bite sound identification. WiLASS involves the open modules of ESP32-WROOM, GPS NEO-6M, ADXL335 accelerometer, GY-MAX4466 amplifier, temperature sensors, and other signal processing circuits. By means of wireless communication, the ESP32-WROOM Thing micro-processor offers high speed transmission, standard protocol, and low power consumption. Data are transferred in a real-time manner from the attached sensing modules to a digital server for further analysis. The module of GPS NEO-6M Thing brings about fast tracking, high precision, and a strong signal, which is suitable for highland applications. Some computations are incorporated into the accelerometer to estimate directional movement and vibration. The GY-MAX4466 Thing plays the role of microphone, which is used to store environmental sound. To ensure the quality of auditory data, they are recorded at a minimum sampling frequency of 10 KHz and at a 12-bit resolution. Moreover, a mobile software in pocket devices is implemented to provide extended mobility and social convenience. Converging with a cloud-based server, the multi-Thing portable platform can provide access to simultaneously supervise. Message Queuing Telemetry Transport (MQTT) protocol with low bandwidth, high reliability, and bi-direction, and which is appropriate for most operating systemsOS, is embedded into the system to prevent data loss. From the experimental results, the feasibility, effectiveness, and correctness of our approach are verified. Under the changes of climate, the proposed framework not only supports the improvement of farming techniques, but also provides a high-quality alternative for poor rural areas because of its low cost and its ability to carry out a proper policy for each species.

Suggested Citation

  • Ha Quang Thinh Ngo & Thanh Phuong Nguyen & Hung Nguyen, 2020. "Research on a Low-Cost, Open-Source, and Remote Monitoring Data Collector to Predict Livestock’s Habits Based on Location and Auditory Information: A Case Study from Vietnam," Agriculture, MDPI, vol. 10(5), pages 1-26, May.
  • Handle: RePEc:gam:jagris:v:10:y:2020:i:5:p:180-:d:359962
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    References listed on IDEAS

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    1. Guopeng Jiang & Miles Grafton & Diane Pearson & Mike Bretherton & Allister Holmes, 2019. "Integration of Precision Farming Data and Spatial Statistical Modelling to Interpret Field-Scale Maize Productivity," Agriculture, MDPI, vol. 9(11), pages 1-22, November.
    2. Alexandros Zervopoulos & Athanasios Tsipis & Aikaterini Georgia Alvanou & Konstantinos Bezas & Asterios Papamichail & Spiridon Vergis & Andreana Stylidou & Georgios Tsoumanis & Vasileios Komianos & Ge, 2020. "Wireless Sensor Network Synchronization for Precision Agriculture Applications," Agriculture, MDPI, vol. 10(3), pages 1-20, March.
    3. Monica Patricia Camas-Nafate & Peggy Alvarez-Gutiérrez & Edgar Valenzuela-Mondaca & Roger Castillo-Palomera & Yolanda del Carmen Perez-Luna, 2019. "Improved Agricultural Products Drying Through a Novel Double Collector Solar Device," Sustainability, MDPI, vol. 11(10), pages 1-13, May.
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