Development Project of Smart Pig Farm using LoRaWAN

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Vitawat Sittakul
Gridsada Phanomchoeng
Lunchakorn Wuttisittikulkij
Widhyakorn Adornwised
Chairat Phongphanphanee

Abstract

This project develops a prototype of smart farm pig using LoRaWAN Network to apply the LoRaWAN network to the farm prototype by installing 3 of LoRaWAN Gateway Stations to be a data transmission medium between all sensors such as temperature sensors, humidity sensors and electricity power meter sensor. Here, the CCTV cameras are used to analyze to find dead pigs and calculate the weights of pigs using photos. This is to track the behaviors of pigs, environment and electricity system. All data are shown on a online website accurately. The top-view and side-view photos of pigs can be analyzed to find the pig weights with an accuracy of +/- 1.86 Kilograms from the average pig weight of 1.5 kilograms. These parameters of temperature, humidity, electricity power and pig weight can be used in future to apply as a platform with other pig farms.

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How to Cite
Sittakul, V., กฤษฎา พนมเชิง, ลัญฉกร วุฒิสิทธิกุลกิจ, วิทยากร อัศดรวิเศษ, & ชัยรัต พงศ์พันธุ์ภาณี. (2021). Development Project of Smart Pig Farm using LoRaWAN . NBTC Journal, 5(5), 215–236. Retrieved from https://so04.tci-thaijo.org/index.php/NBTC_Journal/article/view/238421
Section
Research article

References

Bell, J., Dee, H.M. (2017). Watching plants grow – a position paper on computer vision and

Arabidopsis thaliana. IET Computer Vision. 11(2), 113-121

Chaudhury, A., Ward, Talasaz., Ivanov, C. A., Brophy, A.G., Grodzinski, M. B., Huner, N.P.A.,

Patel, R.V., Barron, J.L. (2017). Machine Vision System for 3D Plant Phenotyping.

Doi:1705.00540

Liu, T,. Wei, W., Chen, W., Chengming, S., Chen, C., Rui, W., Xinkai, Z., Wenshan, G. (2016). A

shadow-based method to calculate the percentage of filled rice grains. Biosystems

Engineering. 150 (2016), 79-88

French, G., Fisher, M., Mackiewicz, M., Needle, C. (2015). Convolutional neural networks for

counting fish in Fisheries surveillance video. In British Machine Vision Conference

Workshop. BMVA Press

Dawkins, M.S., Cain, R., Roberts, S.J. (2012). Optical flow, flock behaviour and chicken

welfare. Animal Behaviour. 84(1), July, 219-223

Kashiha, M., Bahr, C., Ott, S., Moons, C.P.H., Niewold, T.A., Ödberg, F.O., Berckmans, D. (2014).

Automatic weight estimation of individual pigs using image analysis. Computers and

Electronics in Agriculture. 107 (2014). 38–44

Yang, Y., Teng, G. (2007). ESTIMATING PIG WEIGHT FROM 2D IMAGES. Department of

Agricultural and Bioenvironmental Engineering. Agricultural University. PO Box 195.

Beijing. P.R. China

Liu, T., Teng, G., Fu, W. (2011). Research and Development of Pig Weight Estimation System

Based on Image. International Conference on Electronics Communications and

Control (ICECC). November

Li, Z., LuoGuanghui, C., Liu, T. (2014). Estimation of Pig Weight by Machine Vision: A Review.

Computer and Computing Technologies in Agriculture VII. CCTA 2013. IFIP Advances

in Information and Communication Technology. 420. Springer, Berlin. Heidelberg

Kongsro, J. (2014). Estimation of pig weight using a Microsoft Kinect prototype imaging

system. Computers and Electronics in Agriculture. 109, November, 32-35

ดำรง กิตติชัยศรี, อัจฉรา ภาณุรัตน์, จรัส สว่างทัพ และ นฤมล สมคุณา (2544). การพัฒนารูปแบบการเลี้ยง

สุกรพื้นเมืองตามปรัชญาของเศรษฐกิจพอเพียงของเกษตรกรรายย่อยในลุ่มน้ำโขงตอนล่างโดย

กระบวนการมีส่วนร่วม. มหาวิทยาลัยราชภัฏบุรีรัมย์

วันดี ทาตระกูล. (2551) การศึกษาศักยภาพด้านการเลี้ยงสุกรกึ่งชีวภาพเพื่อประยุกต์ใช้สำหรับเกษตรกรราย

ย่อย. คณะเกษตรศาสตร์ ทรัพยากรธรรมชาติและสิ่งแวดล้อม. มหาวิทยาลัยนเรศวร. พิษณุโลก

Hoste, Robert. Suh, Hyun. Kortstee, Harry. (2017). Smart Farming in Pig Production and

Greenhouse Horticulture. Inventory in the Netherlands. Wageningen. Wageningen

Economic Research (Wageningen Economic Research report. ISBN 9789463432184

Xiao, D., Yang, Q., Feng, J. Z, Ke, X., Du, Z. (2017). Design and implementation of large-scale

pig farm big data. The International Tri-Conference for Precision Agriculture