Development and application of management and control system for intellectual sheep breeding based on Internet of Things platform

doi:10.12398/j.issn.2096-7217.2023.01.006

Abstract

Abstract: In recent years, there are 10 000 sheep farms in China. With the joint efforts of the whole industry, the standardized and large-scale breeding of livestock and poultry in China has developed rapidly, and a number of large-scale modern breeding enterprises have emerged. The promotion and application of new varieties, new technologies and new equipment have been accelerated, and the level of mechanization, informatization and intelligentization has been continuously improved. According to the current situation of intensive farming in sheep industry, our team successfully established “Jiyang” intelligent farming management and control system, and completed the system development and application test in the demonstration farm. The system shared and transmitted sheep data by integration of Internet of Things data, and improved the sheep farm management by the AIoT (artificial intelligence & Internet of Things) platform; intelligent control was achieved by collecting environmental control data; the usage of water, electricity and materials are clarified so as to accurately collect production data and master production costs; the energy consumption was accurately collected in actual time to achieve the increase of production and efficiency. By means of convolution algorithm and deep learning technology, AI (artifical intelligence) inventory technology is well developed to improve feeding management efficiency and then achieve disease diagnosis and traceability management. This study conducted a systematic application test on the platform, and tested the function of feeding management and AI inventory. The results showed that the data transmission of feeding management was accurate, the management function could be realized, and the accuracy of AI inventory function was 98%, far higher than expected. The development and utilization of this platform will introduce the digital management system into the sheep farm, laying a foundation for improving the intensive production level of the sheep industry.

Key words: Internet of Things platform, digital management, artificial intelligence technology, AI inventory technology

CLC Number:

S24: S818.5

Yuan Zhiyu, Zhao Yunhui, Wang Song, Zhao Zhuo, Wu Yujin, Wang Chunxin. Development and application of management and control system for intellectual sheep breeding based on Internet of Things platform[J]. Journal of Intelligent Agricultural Mechanization, 2023, 4(1): 54-61.

References

［1］ Vouraki S, Skourtis I, Psichos K, et al. A decision support system for economically sustainable sheep and goat farming ［J］. Animals, 2020, 10(12): 2421.
［2］Liakos K G, Busato P, Moshou D, et al. Machine learning in agriculture: A review ［J］. Sensors, 2018, 18(8): 2674.
［3］Nóbrega L, Goncalves P, Pedreiras P, et al. An IoT-based solution for intelligent farming ［J］. Sensors, 2019, 19(3): 603.
［4］Omarov R A, Isakhanov M Z, Kuder K M, et al. Experimental results of functional characteristics of IoT for free range sheep breeding ［J］. International Journal of Agricultural Resources, Governance and Ecology, 2021, 17(2-4): 301-315.
［5］Prabowo C, Mauliadi R, Alanda A, et al. The implementation of IoT (Internet of Things) for controlling cow health ［C］∥ IOP Conference Series: Materials Science and Engineering. IOP Publishing, 2020, 846(1): 012011.
［6］Wu F, Miao Z H, He C X. Remote monitoring system for intelligent slaughter production line based on Internet of Things and cloud platform ［C］∥ 2020 11th International Conference on Prognostics and System Health Management (PHM-2020 Jinan). IEEE, 2020: 538-542.
［7］袁圣钧. 基于国际视角的中国肉羊产业发展现状分析及肉羊企业发展策略研究［D］. 咸阳: 西北农林科技大学, 2020.
Yuan Shengjun. Based on the international perspective of investigation on the status of mutton industry in China and its future development strategy ［D］. Xianyang: Northwest A＆F University, 2020.
［8］次仁卓玛. 国网羊湖电站智能化视频监测系统软件设计与实现［D］. 成都: 电子科技大学, 2020.
Cirenzhuoma. Design and implementation of intelligent video monitoring system software for Yanghu power station of state grid ［D］. Chengdu: University of Electronic Science and Technology of China, 2020.
［9］罗土玉, 豆姣, 高彦玉, 等. 规模舍饲羊场中自动称重分栏设备的设计与试验［J］. 南方农机, 2021, 52(9): 8-11, 21.
Luo Tuyu, Dou Jiao, Gao Yanyu, et al. Design and experiment of automatic weighing and grouping equipment in largescale house feeding sheep farm ［J］. Forum of South China, 2021, 52(9): 8-11, 21.
［10］李振强, 王树才, 赵世达, 等. 基于机器视觉和机器学习的羊骨架自动分割方法［J］. 食品与机械, 2020, 36(6): 125-132.
Li Zhenqiang, Wang Shucai, Zhao Shida, et al. The calculation methods of goat trunk’s segmentation trajectory based on machine vision and machine learning ［J］. Food and Machinery, 2020, 36(6): 125-132.
［11］王旺虎, 郭子记, 李锦旗. 智慧养殖在肉羊产业中的应用［J］. 畜牧兽医杂志, 2021, 40(4): 81-84.
Wang Wanghu, Guo Ziji, Li Jinqi. Application of intelligent breeding in mutton sheep industry ［J］. Journal of Animal Science and Veterinary Medicine, 2021, 40(4): 81-84.
［12］刘国萍, 杨明川, 周路. 智慧畜牧服务及关键技术研究［J］. 电信网技术, 2018(5): 38-42.
Liu Guoping, Yang Mingchuan, Zhou Lu. Research on the service and key technology of intelligent animal husbandry ［J］. Telecommunications Network Technology, 2018(5): 38-42.
［13］Cappai M G, Picciau M, Nieddu G, et al. Long term performance of RFID technology in the large scale identification of small ruminants through electronic ceramic boluses: Implications for animal welfare and regulation compliance ［J］. Small Ruminant Research, 2014, 117(2-3): 169-175.
［14］申格, 吴文斌, 史云, 等. 我国智慧农业研究和应用最新进展分析［J］. 中国农业信息, 2018, 30(2): 1-14.
Shen Ge, Wu Wenbin, Shi Yun, et al. The latest progress in the research and application of smart agriculture in China ［J］. China Agricultural Informatics, 2018, 30(2): 1-14.
［15］郑娜, 金花. 基于物联网的肉羊繁育场管理系统的设计与实现［J］. 黑龙江畜牧兽医, 2017(2): 81-83.
Zheng Na, Jin Hua. Design and implementation of management system of mutton sheep breeding farm based on Internet of Things ［J］. Heilongjiang Animal Science and Veterinary Medicine, 2017(2): 81-83.
［16］郭思乐, 邱金戈, 柳广斌, 等. 华南地区规模化羊场建设存在的问题与建议［J］. 黑龙江畜牧兽医, 2021(21): 69-73.
Guo Sile, Qiu Jinge, Liu Guangbin, et al. Problems and suggestions of large-scale goat farm construction in South China ［J］. Heilongjiang Animal Science and Veterinary Medicine, 2021(21): 69-73.
［17］周利香. 基于轻量化神经网络的羊脸识别方法研究［D］. 咸阳: 西北农林科技大学, 2022.
Zhou Lixiang. Research on sheep face recognition method based on lightweight neural network ［D］. Xianyang: Northwest A&F University, 2022.
［18］吴飞, 石兰, 马梅兰, 等. 基于Django框架的肉羊遗传评估系统的设计［J］. 中国畜牧杂志, 2022, 58(11): 127-131.
Wu Fei, Shi Lan, Ma Meilan, et al. Design of genetic evaluation system for mutton sheep based on Django framework ［J］. Chinese Journal of Animal Science, 2022, 58(11): 127-131.
［19］权富生, 盘婕, 徐旭. 集约化健康养殖条件下羊病的防控策略［J］. 中国兽医杂志, 2020, 56(5): 125-127.
Quan Fusheng, Pan Jie, Xu Xu. Prevention and control strategy of sheep disease under the condition of intensive and healthy breeding ［J］. Chinese Journal of Veterinary Medicine, 2020, 56(5): 125-127.

［20］张永安, 刘艳秋, 武佩, 等. 基于物联网技术的羊只流水线屠宰过程溯源信息链构建［J］. 黑龙江畜牧兽医, 2019(24): 42-45.

Zhang Yong’an, Liu Yanqiu, Wu Pei, et al. Construction of traceability information chain of sheep slaughtering process based on Internet of Things technology ［J］. Heilongjiang Animal Science and Veterinary Medicine, 2019(24): 42-45.