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
收稿日期: 2024-07-12
网络出版日期: 2025-01-22
基金资助
国家重点研发计划(2024YFD1300604); 国家自然科学基金(42301460); 中国农业科学院科技创新工程(CAAS-ASTIP-2024-AII); 中央级公益性科研院所基本科研业务费专项(JBYW-AII-2024-16/18/19/23/28/35/36/40); 北京市智慧农业创新团队项目(BAIC10-2024); 农业农村部现代农业装备重点实验室开放基金课题(2023nyzbsys09)
版权
Artificial Intelligence-Driven High-Quality Development of New Quality Productive Forces in Animal Husbandry: Restraining Factors, Generation Logic and Promotion Paths
Received date: 2024-07-12
Online published: 2025-01-22
Supported by
National Key Research and Development Program(2024YFD1300604); National Natural Science Foundation of China(42301460); Science and Technology Innovation Program of the Chinese Academy of Agricultural Sciences under Grant(CAAS-ASTIP-2024-AII); Central Public- interest Scientific Institution Basal Research Fund under Grants(JBYW-AII-2024-16/18/19/23/28/35/36/40); Beijing Smart Agriculture Innovation Consortium Project(BAIC10-2024); Open Fund from Key Laboratory of Modern Agricultural Equipment, Ministry of Agriculture and Rural Development(2023nyzbsys09)
Copyright
[目的/意义] 发展新质生产力对推动畜牧业高质量发展具有重要意义。本文旨在对人工智能驱动畜牧新质生产力高质量发展开展系统研究。厘清人工智能推动畜牧新质生产力高质量发展的机理和方向,深入分析畜牧新质生产力的内涵、特征、制约因素,以及推进路径。 [进展] 畜牧新质生产力是以生物技术、信息技术和绿色技术等前沿技术创新为主导,以数智化、绿色化、生态化为产业升级方向,基本内涵表现为更高素质的劳动者、更先进的劳动资料和更广范围的劳动对象。与传统生产力相比,畜牧新质生产力是以科技创新为导向、以新发展理念为引领、以全要素生产率提升为核心的先进生产力,具有生产效率高、产业效益好、可持续发展能力强的显著特征。中国畜牧新质生产力已具备较好发展基础,但也面临畜禽育种技术创新不足、核心竞争力不强,畜牧养殖机械化率不高、智能装备自主研发能力较弱,“机器换人”需求迫切、畜牧人才量质存在短板,养殖规模化程度不高、智能化管理水平有限等制约因素。人工智能在畜牧业中可以广泛应用在环境控制、精准饲喂、健康监测与疫病防控、供应链优化等领域。人工智能经由以数字技术为代表的畜牧业技术革命性突破,以数据要素为纽带的畜牧业生产力要素创新性配置,与数字经济相适应的畜牧业产业深度转型,催生畜牧新质生产力,赋能畜牧业高质量发展。 [结论/展望] 提出了提升畜牧科技创新能力、建立畜牧业全链条信息化监管模式、加快畜牧绿色科技推广应用、提高畜牧业全产业链管理水平,以及完善重要畜禽品种商业化育种机制的畜牧新质生产力发展推进路径。
刘继芳 , 周向阳 , 李敏 , 韩书庆 , 郭雷风 , 迟亮 , 杨璐 , 吴建寨 . 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, 2025 : 1 -13 . DOI: 10.12133/j.smartag.SA202407010
[Significance] Developing new quality productive forces is of great significance for promoting high-quality development of animal husbandry. However, there is currently limited research on new quality productivity in animal husbandry, and there is a lack of in-depth analysis on its connotation, characteristics, constraints, and promotion path. [Progress] This article conducts a systematic study on the high-quality development of animal husbandry productive forces driven by artificial intelligence. The new quality productive forces of animal husbandry is led by cutting-edge technological innovations such as biotechnology, information technology, and green technology, with digitalization, greening, and ecologicalization as the direction of industrial upgrading. Its basic connotation is manifested as higher quality workers, more advanced labor materials, and a wider range of labor objects. Compared with traditional productive forces, the new quality productive forces of animal husbandry is an advanced productive forces guided by technological innovation, new development concepts, and centered on the improvement of total factor productive forces. It has significant characteristics of high production efficiency, good industrial benefits, and strong sustainable development capabilities. China's new quality productive forces in animal husbandry has a good foundation for development, but it also faces constraints such as insufficient innovation in animal husbandry breeding technology, weak core competitiveness, low mechanization rate of animal husbandry, weak independent research and development capabilities of intelligent equipment, urgent demand for "machine replacement", shortcomings in the quantity and quality of animal husbandry talents, low degree of scale of animal husbandry, and limited level of intelligent management. Artificial intelligence in animal husbandry can be widely used in environmental control, precision feeding, health monitoring and disease prevention and control, supply chain optimization and other fields. Artificial intelligence, through revolutionary breakthroughs in animal husbandry technology represented by digital technology, innovative allocation of productive forces factors in animal husbandry linked by data elements, and innovative allocation of productive forces factors in animal husbandry adapted to the digital economy, has given birth to new quality productive forces in animal husbandry and empowered the high-quality development of animal husbandry. [Conclusions and Prospects] This article proposes a path to promote the development of new quality productive forces in animal husbandry by improving the institutional mechanism of artificial intelligence to promote the development of modern animal husbandry industry, strengthening the application of artificial intelligence in animal husbandry technology innovation and promotion, and improving the management level of artificial intelligence in the entire industry chain of animal husbandry.
1 | 习近平. 发展新质生产力是推动高质量发展的内在要求和重要着力点[J]. 奋斗, 2024(11): 4-8. |
2 | 马晓河, 杨祥雪. 以加快形成新质生产力推动农业高质量发展[J]. 农业经济问题, 2024, 45(4): 4-12. |
3 | 姜长云. 农业新质生产力: 内涵特征、发展重点、面临制约和政策建议[J]. 南京农业大学学报(社会科学版), 2024, 24(3): 1-17. |
4 | 罗必良, 耿鹏鹏. 农业新质生产力: 理论脉络、基本内核与提升路径[J]. 农业经济问题, 2024, 45(4): 13-26. |
5 | 魏后凯, 吴广昊. 以新质生产力引领现代化大农业发展[J]. 改革, 2024(5): 1-11. |
6 | 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. |
7 | |
8 | |
9 | 秦英栋, 贾文珅. 基于NB-IoT网络的兔舍环境实时监测系统[J]. 智慧农业(中英文), 2023, 5 (1): 155-165. |
10 | |
11 | 谢秋菊, 吴梦茹, 包军, 等. 融合注意力机制的个体猪脸识别[J]. 农业工程学报, 2022, 38(7): 180-188. |
12 | |
13 | 周意, 毛宽民. 基于YOLO-Unet组合网络的牛只个体识别方法研究[J/OL]. 计算机科学, 1-13. [2025-01-14]. |
14 | |
15 | |
16 | 赵一名, 沈明霞, 刘龙申, 等. 基于改进YOLOv5s和图像融合的笼养鸡死鸡检测方法研究[J]. 南京农业大学学报, 2024, 47(2): 369-382. |
17 | 刘峰, 吴文杰, 刘小磊, 等. 计算机视觉与深度学习在猪只识别中的研究进展[J]. 华中农业大学学报, 2023, 42(3): 47-56. |
18 | |
19 | 李艳文, 李菊霞, 纳腾潇, 等. 基于YOLOX-NGS的群养猪只攻击行为识别[J]. 农业工程学报, 2023, 39(24): 177-184. |
20 | |
21 | |
22 | |
23 | |
24 | 姚裔芃, 徐晨, 陈鸿基, 等. 基于关键点检测和多目标跟踪的猪只体尺估计[J]. 华南农业大学学报, 2024, 45(5): 722-729. |
25 | 耿艳利, 季燕凯, 岳晓东, 等. 基于点云语义分割的猪只体尺测量方法研究[J]. 农业机械学报, 2023, 54(7): 332-338, 380. |
26 | 翁智, 范琦, 郑志强. 基于多模态图像信息及改进实例分割网络的肉牛体尺自动测量方法[J]. 智慧农业(中英文), 2024, 6(4): 64-75. |
27 | 熊本海, 蒋林树, 杨亮, 等. 种猪生产性能测定系统开发与性能测试[J]. 农业工程学报, 2017, 33(9): 174-179. |
28 | 黄昊, 刘俊灵, 胡腾达, 等. 智能化母猪饲喂控制系统设计与试验[J]. 中国农机化学报, 2021, 42(10): 78-86. |
29 | |
30 | 刘艳昌, 郭宇戈, 张志霞, 等. 基于LoRa的生猪体征监测系统设计与实现[J]. 中国农机化学报, 2024, 45(4): 66-71, 140. |
31 | |
32 | |
33 | |
34 | 吴振邦, 陈泽锴, 田绪红, 等. 基于3D卷积视频分析的猪步态评分方法[J]. 华南农业大学学报, 2024, 45(5): 743-753. |
35 | 张博, 罗维平. 基于Swin-Unet的奶牛饲料消耗状态监测方法[J]. 华南农业大学学报, 2024, 45(5): 754-763. |
36 | |
37 | 沈明霞, 王梦雨, 刘龙申, 等. 基于深度神经网络的猪咳嗽声识别方法[J]. 农业机械学报, 2022, 53(5): 257-266. |
38 | |
39 | 杜晓冬, 滕光辉, 刘慕霖, 等. 基于轻量级卷积神经网络的种鸡发声识别方法[J]. 农业机械学报, 2022, 53(10): 271-276. |
40 | 刘剑锋, 邱小田, 周磊, 等. 猪全产业链育种技术及其国内外应用现状[J]. 中国畜牧杂志, 2024, 60(7): 1-5. |
41 | 中华人民共和国教育部. 高等教育分学科门类研究生数(总计)[EB/OL]. (2023-12-29) [2024-06-29]. |
42 | 何沛桐, 张建华, 张凝, 等. 基于视觉感知的畜禽智慧养殖管理与疫病诊断研究进展[J]. 中国农业大学学报, 2023, 28(10): 141-165. |
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