智慧养猪中自动料线与饲喂站的应用现状及优化路径

doi:10.16174/j.issn.1673-4645.2026.04.004

Abstract

Abstract: In modern animal husbandry, automatic feed lines and feeding stations have become the core equipment of intelligent pig breeding. These equipment can gradually change pig breeding from the experience-based model in the past to the data-based fine management mode. This article sorted out the application status of these two types of equipment, compared the technical characteristics of dry material line and liquid feed line and their respective applicable scenarios, and pointed out that they have achieved obvious results in accurate feeding, improving feed utilization, reducing labor costs and improving animal welfare. At the same time, specific challenges such as high costs, data isolation, model lag and talent shortage were also encountered in the process. In response to these difficulties, the article put forward three core optimization paths. First, build a data-driven accurate nutrition dynamic model, refine nutrition regulation from group formula to individual level; second, promote the intelligent and collaborative upgrading of the feeding system, so that the feeding station can be transformed into a multi-modal health sentry station integrating vision, sound and infrared, and then with the help of PLC control, machine vision and digital twin technology, it improves the intelligence level and reliability of the automatic feed line; Third, strengthen the application and model innovation of the scenario, and implement the number of large-scale integrated farms. Intelligent integrated solutions, and provide modular and lightweight solutions for small and medium-sized pig farms. In the future, with the in-depth application of new technologies such as edge AI chips and federal learning, coupled with the gradual improvement of policies and standard systems, automatic feeding lines and feeding stations will develop in a more intelligent, integrated and personalized direction, and refined breeding management will also become the norm, which can provide strong support for the high quality and sustainable development strong support.

Key words: smart pig farming, automatic feeding line, automatic feeding station, precise feeding, digital intelligence

CLC Number:

S828

SUN Wenxuan, JIANG Bo, WANG Wenwen. The application status and optimization path of automatic feed lines and feeding stations in smart pig farming[J].China Swine Industry, 2026, 21(4): 17-25.

References

[1] 沈明霞, 陈金鑫, 丁奇安, 等. 生猪自动化养殖装备与技术研究进展与展望[J]. 农业机械学报, 2022, 53(12): 1-19. SHEN M X, CHEN J X, DING Q A, et al. Research progress and prospect of automatic pig farming equipment and technology[J]. Transactions of the Chinese Society of Agricultural Machinery, 2022, 53(12): 1-19. [2] 豆姣, 王开云, 陈俊强, 等. 塞盘送料系统在规模猪场饲料输送环节的应用与发展[J]. 饲料研究, 2022, 45(11): 134-138. DOU J, WANG K Y, CHEN J Q, et al. Application and development of chain disc feeding system in feed delivery of large-scale pig farms[J]. Feed Research, 2022, 45(11): 134-138. [3] 孙峰杰, 辛顺进, 魏金权, 等. 养猪场自动料线的现状与方案选择[J]. 猪业科学, 2023, 40(2): 76-77. SUN F J, XIN S J , WEI J Q, et al. Current situation and solution selection of automatic feeding lines in pig farms[J]. Swine Industry Science, 2023, 40(2): 76-77. [4] 沈波, 王改琴, 刘春雪, 等. 液态饲喂系统及设备的研究与应用进展[J]. 饲料工业, 2018, 39(19): 9-13. SHEN B, WANG G Q, LIU C X, et al. Research and application progress of liquid feeding system and equipment [J]. Feed Industry, 2018, 39(19): 9-13. [5] 孟蕊,崔晓东,余礼根,等. 畜禽精准饲喂管理技术发展现状与展望[J].家畜生态学报,2021,42(2):1-7. MENG R, CUI XD, YU LG, et al. Development status and prospects of livestock and poultry precision feeding management technology [J]. Journal of Domestic Animal Ecology, 2021, 42(2): 1-7. [6] GEARY T M. Improving the performance of weaner pigs through developments in liquid feeding[D]. Plymouth: University of Plymouth, 1997. [7] 化世鹏, 谷岩, 尹东乐. 规模化猪场液态发酵智能饲喂系统的应用与实践[J]. 北方牧业, 2021(21): 14-15. HUA S P, GU Y, YIN D L. Application and practice of liquid fermentation intelligent feeding system in large-scale pig farms[J]. Northern Animal Husbandry, 2021(21): 14-15. [8] 胡雄, 吕美, 乔家运, 等. 发酵饲料液态饲喂在养猪业的应用[J]. 猪业科学, 2023, 40(12): 26-28. HU X, LV M, QIAO J Y, et al. Application of fermented feed liquid feeding in the pig industry [J]. Swine Industry Science, 2023, 40(12): 26-28. [9] 舒娟, 易烈运, 彭安, 等. 基于RFID的生猪自动饲喂控制系统的研究[J]. 中国农机化学报, 2017, 38(2): 73-75, 119. SHU J, YI L Y, PENG A, et al. Research on automatic feeding control system for pigs based on RFID [J]. Journal of Chinese Agricultural Mechanization, 2017, 38(2): 73-75, 119. [10] 赵培杰, 林都, 鲜浩, 等. 智能化母猪养殖管理系统设计[J]. 家畜生态学报, 2017, 38(11): 76-79. ZHAO P J, LIN D, XIAN H, et al. Design of intelligent sow management system [J]. Journal of Domestic Animal Ecology, 2017, 38(11): 76-79. [11] 季邦, 王春光, 刘涛. 我国生猪饲喂设备的研究现状及发展趋势[J]. 黑龙江畜牧兽医, 2016(14): 57-59, 292. JI B, WANG C G, LIU T. Research status and development trend of pig feeding equipment in China [J]. Heilongjiang Animal Science and Veterinary Medicine, 2016(14): 57-59, 292. [12] 郭嘉明, 刘彬, 夏晶晶, 等. 生猪智能粥料机饲喂方案对比优化[J]. 中国农机化学报, 2025, 46(4): 146-152. GUO J M, LIU B, XIA J J, et al. Comparative optimization of feeding schemes for intelligent porridge feeders in pig production [J]. Journal of Chinese Agricultural Mechanization, 2025, 46(4): 146-152. [13] 吕恩利, 陈高峰, 夏晶晶, 等. 哺乳母猪智能饲喂系统设计与试验[J]. 中国农机化学报, 2023, 44(7): 154-162. LV E L, CHEN G F, XIA J J, et al. Design and experiment of an intelligent feeding system for lactating sows [J]. Journal of Chinese Agricultural Mechanization, 2023, 44(7): 154-162. [14] 陈希杭. 全自动供料系统在生猪养殖中的应用效果分析[J]. 畜牧与饲料科学, 2014, 35(2): 73-75. CHEN X H. Analysis of application effects of automatic feeding system in pig breeding [J]. Animal Husbandry and Feed Science, 2014, 35(2): 73-75. [15] 汪德众, 刘希锋, 李特. 母猪自动化饲喂系统的研究[J]. 农机化研究, 2023, 45(9): 95-99, 105. WANG D Z, LIU X F, LI T. Research on automated sow feeding system [J]. Journal of Agricultural Mechanization Research, 2023, 45(9): 95-99, 105. [16] 覃荣刚, 李日华, 韦丽娇, 等. 规模化养猪场自动化智能化精确饲喂系统设计与应用[J].企业科技与发展, 2021(7): 55-57. QIN R G, LI R H, WEI L J, et al. Design and application of automated intelligent precision feeding systems for large-scale pig farms [J]. Enterprise Technology and Development, 2021(7): 55-57. [17] 刘华东, 周昕, 李向军, 等. 母猪精准饲喂系统、模型及其优化算法[J]. 黑龙江畜牧兽医, 2023(3): 31-35. LIU H D, ZHOU X, LI X J, et al. Precision feeding system, model, and optimization algorithms for sows [J]. Heilongjiang Animal Science and Veterinary Medicine, 2023(3): 31-35. [18] 顾宪红, 张俊玲. 母猪电子群养系统和个体限位栏系统繁殖性能及动物福利水平对比分析[J]. 畜牧兽医学报, 2016, 47(6): 1189-1197. GU XH, ZHANG J L. Comparative analysis of reproductive performance and animal welfare levels between electronic sow feeding group housing system and individual stall system [J]. Acta Veterinaria et Zootechnica Sinica, 2016, 47(6): 1189-1197. [19] 尹汉周, 姚美玲, 黄大鹏. 规模化猪场智能化饲养设备发展现状及成本效益[J]. 黑龙江畜牧兽医, 2016(6):69-71. YIN H Z, YAO M L, HUANG D P. Development status and cost-benefit analysis of intelligent breeding equipment in large-scale pig farms [J]. Heilongjiang Animal Science and Veterinary Medicine, 2016(6): 69-71. [20] 潘俊毅, 吴青瑶, 谭碧娥, 等. 生长育肥猪精准营养供给技术及智能化养殖设备研究进展[J]. 畜牧兽医学报, 2025, 56(2): 501-512. PAN J Y, WU Q Y, TAN B E, et al. Research progress on precision nutrition supply technology and intelligent breeding equipment for growing-finishing pigs[J]. Acta Veterinaria et Zootechnica Sinica, 2025, 56(2): 501-512. [21] 牛合利. 基于物联网技术的畜牧饲料饲喂系统应用研究[J]. 中国饲料, 2025(8): 161-164. NIU H L. Application research on livestock feed feeding systems based on Internet of things technology[J]. China Feed, 2025(8): 161-164. [22] 方金津, 伍冠锁, 邓小红, 等. 智慧养猪关键技术研究现状与展望[J]. 国外畜牧学(猪与禽), 2025, 45(6): 19-22. FANG J J, WU G S, DENG X H, et al. Current status and outlook of research on key technologies for smart swine farming[J]. Animal Science Abroad(Pigs and Poultry), 2025, 45(6): 19-22. [23] 熊本海, 杨振刚, 杨亮, 等. 中国畜牧业物联网技术应用研究进展[J]. 农业工程学报, 2015, 31(S1): 237-246. XIONG B H, YANG Z G, YANG L, et al. Research progress on application of Internet of Things technology in animal husbandry in China[J]. Transactions of the Chinese Society of Agricultural Engineering, 2015, 31(S1): 237-246. [24] 刘继芳, 周向阳, 李敏, 等. 人工智能驱动畜牧新质生产力高质量发展：制约因素、生成逻辑与推进路径[J]. 智慧农业(中英文), 2025, 7(1): 165-177. LIU J F, ZHOU X Y, LI M, et al. Artificial intelligence driving high-quality development of new productive forces in animal husbandry: constraints, generation logic and promotion paths [J]. Smart Agriculture, 2025, 7(1): 165-177. [25] 唐瑜嵘, 沈明霞, 薛鸿翔, 等. 人工智能技术在畜禽养殖业的发展现状与展望[J]. 智能化农业装备学报(中英文), 2023, 4(1): 1-16. TANG Y R, SHEN M X, XUE H X, et al. Development status and prospect of artificial intelligence technology in livestock and poultry breeding[J]. Journal of Intelligent Agricultural Mechanization, 2023, 4(1): 1-16. [26] 王怀禹, 罗通彪, 刘强, 等. 养殖业数智化转型升级背景下的畜牧人才培养探索实践案例[J]. 猪业科学, 2023, 40(9): 46-49. WANG H Y, LUO T B, LIU Q, et al. Exploration and practice case of livestock talent training under the background of digital and intelligent transformation in the breeding industry [J]. Swine Industry Science, 2023, 40(9): 46-49. [27] FUNK T H, ROHRER G A, BROWN-BRANDL T M, et al. Online feeding behavior monitoring of individual group-housed grow-finish pigs using a low-frequency RFID electronic feeding system[J]. Translational Animal Science, 2024, 8: txae051. [28] MASELYNE J, VAN NUFFEL A, BRIENE P, et al. Online warning systems for individual fattening pigs based on their feeding pattern[J]. Biosystems Engineering, 2018, 173: 143-156. [29] 肖德琴, 吕玉定, 黄一桂, 等. 生猪智能检测技术研究进展与未来展望[J]. 智慧农业(中英文), 2026, 8(1): 86-103. XIAO D Q, LV Y D, HUANG Y G, et al. Research progress and future prospects of pig intelligent detection technology[J]. Smart Agriculture, 2026, 8(1): 86-103. [30] 陆明洲, 沈明霞, 丁永前, 等. 畜牧信息智能监测研究进展[J]. 中国农业科学, 2012, 45(14): 2939-2947. LU M Z, SHEN M X, DING Y Q, et al. Review on the intelligent technology for animal husbandry information monitoring[J]. Scientia Agricultura Sinica, 2012, 45(14): 2939-2947. [31] 艾景军, 韩继福, 许万根, 等. 生长肥育猪增重过程的模拟[J]. 吉林农业大学学报, 1998(2): 78-82, 99. AI J J, HAN J F, XU W G, et al. Simulation of the weight gain process in growing-finishing pigs [J]. Journal of Jilin Agricultural University, 1998(2): 78-82, 99. [32] DURAND M, LARGOU?T C, DE BEAUFORT L B, et al. Prediction of the daily nutrient requirements of gestating sows based on sensor data and machine-learning algorithms[J]. Journal of Animal Science, 2023, 101: skad337. [33] 陈虹余, 尹令, 杨明, 等. 视觉和传感器融合的多模态猪只行为识别[J]. 农业工程学报, 2025, 41(8): 194-203. CHEN H Y, YIN L, YANG M, et al. Multi-modal recognition of pig behavior based on fusion of vision and sensors [J]. Transactions of the Chinese Society of Agricultural Engineering (Transactions of the CSAE), 2025, 41(8): 194-203. [34] 黎煊, 赵建, 高云, 等. 基于深度信念网络的猪咳嗽声识别[J]. 农业机械学报, 2018, 49(3):179-186. LI X, ZHAO J, GAO Y, et al. Recognition of pig cough sounds based on deep belief nets [J]. Transactions of the Chinese Society for Agricultural Machinery, 2018, 49(3): 179-186. [35] LU M Z, HE J, CHEN C, et al. An automatic ear base temperature extraction method for top view piglet thermal image[J]. Computers and Electronics in Agriculture, 2018, 155: 339-347. [36] 黄月芹, 李日华, 韦丽娇, 等. 基于PLC控制的智能饲喂系统设计[J]. 农业工程, 2025, 15(8): 112-117. HUANG Y Q, LI R H, WEI L J, et al. Design of an intelligent feeding system based on PLC control[J]. Agricultural Engineering, 2025, 15(8): 112-117. [37] 周旭. 基于机器视觉的饲料投喂一体化精准控制系统研究[J]. 中国饲料, 2025(14): 81-84. ZHOU X. Research on an integrated precise control system for feed feeding based on machine vision [J]. China Feed, 2025(14): 81-84. [38] HE X Y, LIN W J, LI Z W, et al. A prediction method for remaining feed weight in intelligent feeders from electrical signal time series[J]. Smart Agricultural Technology, 2025, 12: 101575. [39] 陶杰, 吴凡, 黄克亚, 等. 数字孪生技术在生猪精准饲喂中的应用研究进展[J]. 江苏农业科学, 2025, 53(20): 10-18. TAO J, WU F, HUANG K Y, et al. Research progress on application of digital twin technology in pig precision feeding [J]. Jiangsu Agricultural Sciences, 2025, 53(20): 10-18. [40] 王泽平, 应诗家, 房银龙, 等. 生猪智慧养殖物联网系统设计与实现[J]. 中国农机化学报, 2025, 46(1):73-77, 98. WANG Z P, YING S J, FANG Y L, et al. Design and implementation of IoT system in swine intelligent breeding[J]. Journal of Chinese Agricultural Mechanization, 2025, 46(1): 73-77. [41] 薛飞跃, 周玉玲, 李俊凯, 等. 智慧养殖农业物联网与边缘计算中大模型技术应用综述[J]. 农业机械学报, 2025, 56(9): 291-311. XUE F Y, ZHOU Y L, LI J K, et al. Application of foundation model technology for smart farming in edge computing and agricultural Internet of Things(agri-IoT)[J]. Transactions of the Chinese Society for Agricultural Machinery, 2025, 56(9): 291-311. [42] SRINIVASAGAN R, EL SAYED M S, AL-RASHEED M I, et al. Edge intelligence for poultry welfare: utilizing tiny machine learning neural network processors for vocalization analysis[J]. PLoS One, 2025, 20(1): e0316920. [43] GARRO R J, WILSON C S, SWAIN D L, et al. A systematic literature review on the applications of federated learning and enabling technologies for livestock management[J]. Computers and Electronics in Agriculture, 2025, 234: 110180.

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The application status and optimization path of automatic feed lines and feeding stations in smart pig farming

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