Design and Application of IoT Intelligent Management Platform for Nanfan Breeding Bases

  • JiaLe LI ,
  • HongXin ZHAO ,
  • Jia LIN ,
  • ZiKang HE ,
  • RuYi XU ,
  • GuoPing YU ,
  • GuoMin ZHOU ,
  • JianHua ZHANG
Expand
  • 1. Hainan National Breeding and Multiplication Institute at Sanya, Chinese Academy of Agricultural Sciences, Sanya 572024, Hainan, China
    2. National Agriculture Science Data Center, Beijing 10081, China
    3. Agricultural Information Institute of Chinese Academy of Agricultural Sciences Beijing 10081, China
    4. Sanya Agriculture and Rural Bureau, Sanya 572000, Hainan, China
    5. China National Rice Research Institute, Hangzhou 310006, China
    6. Institute of Farmland Irrigation, Chinese Academy of Agricultural Sciences, Xinxiang 410700, Henan, China

Received date: 2023-11-03

  Accepted date: 2023-11-22

  Online published: 2024-01-05

Abstract

The Nanfan breeding base is the "gas pedal" of China's agricultural breeding, and its strategic position is very important. In order to improve the efficiency of agricultural data collection at the Nanfan breeding base, strengthen the capacity of farmland management and meet the needs of large-scale crop breeding, there is an urgent need to build an intelligent management platform for bases based on the IoT. Taking the Potianyang base in Sanya City, Hainan Province as an object, an IoT intelligent management platform for the Nanfan breeding bases has been developed. The platform deploys sensors, video monitoring, intelligent irrigation and other equipment, and develops five functional modules, such as crop weather and soil environment sensing, video monitoring, pests monitoring. Real-time monitoring of the field's weather, soil, pests, spores and other environmental information, as well as crop growth conditions can achieve intelligent and precise irrigation. The real-time sensing and intelligent warning function of the platform can realize remote monitoring and regulation of the breeding environment, providing breeders with intelligent decision-making. By intelligently obtaining crop phenotypic information, the platform also provides breeders with a large amount of experimental materials, greatly improving the efficiency and accuracy of breeding work and providing technical support for large-scale field crop breeding. In the future, relying on modern technologies such as artificial intelligence, robots and drones, the intelligent acquisition of crop phenotypic data will replace manual phenotypic measurements, the intelligent management of crop plus generation breeding will replace manual management, and the intelligent evaluation and screening of excellent varieties will replace experienced breeders, which will realize the integration of the sky and land of the Nanfan breeding bases and the intelligent digitalization of large-scale field breeding.

Cite this article

JiaLe LI , HongXin ZHAO , Jia LIN , ZiKang HE , RuYi XU , GuoPing YU , GuoMin ZHOU , JianHua ZHANG . Design and Application of IoT Intelligent Management Platform for Nanfan Breeding Bases[J]. Journal of Agricultural Big Data, 2023 , 5(4) : 37 -46 . DOI: 10.19788/j.issn.2096-6369.230404

References

[1] 周海波. 南繁基地管理体制改革的对策建议[J]. 热带农业工程, 2021, 45(2):30-32.
[2] 新华社. “南繁硅谷”引领我国种业创新——国家南繁科研育种基地建设综述[EB/OL]. [2018]. https://www.gov.cn/xinwen/2018-05/08/content_5289261.htm.
[3] 陈冠铭, 曹兵, 刘扬. 国家南繁育制种产业发展战略路径研究[J]. 种子, 2017, 36(1):68-72.
[4] 王松林, 练炳维. 海南南繁基地建设现状及发展策略[C]// 中国农业资源与区划学会.2012年中国农业资源与区划学会学术年会论文集. 2012:183-187.
[5] 许桓瑜, 王萍, 张雨良, 等. 南繁硅谷建设的分析与思考[J]. 农学学报, 2019, 9(1):89-95.
[6] 李利如, 张孟. 智慧农业物联网平台的多场景应用[J]. 中国农业资源与区划, 2023, 44(07):116+128.
[7] 杨俊, 马霆, 郭丹. 提升数字能力赋能智慧农业发展[J]. 华中农业大学学报, 2023, 42(5):282-288.
[8] 肖鹰, 曾志丹, 张艳. 基于云计算下现代生态农业物联网监控系统的设计[J]. 农机化研究, 2023, 45(11):117-121.
[9] 尹倩, 蒋辉. 物联网技术在生物育种中的应用探析[J]. 分子植物育种, 2023, 21(16):5565-5568.
[10] SHIMONO H, HASEGAWA T, IWAMA K. Response of growth and grain yield in paddy rice to cool water at different growth stage[J]. Field Crop Research, 2002, 73(2-3): 67-79.
[11] 宋春燕, 万运帆, 李玉娥, 等. 温度和CO2浓度升高下双季稻茎蘖动态、成穗率与产量的关系[J]. 作物杂志, 2023,(3):159-166.
[12] 韩凡香, 陈倩, 包正育, 等. 秸秆带状覆盖种植马铃薯农田土壤温度及其气温响应特征[J]. 甘肃农业大学学报, 2023, 58(3):67-75.
[13] 房世波, 沈斌, 谭凯炎, 等. 大气[CO2]和温度升高对农作物生理及生产的影响[J]. 中国生态农业学报, 2010, 18(5):1116-1124.
[14] LONG S P, AINSWORTH E A, LEAKEY A D B, et al. Food for thought: lower-than-expected crop yield stimulation with rising CO2 concentrations[J]. Science, 2006, 312(5782): 1918-1921.
[15] 戴伟, 路秉翰, 许铭宇, 等. 观光温室中气候环境因子对水稻生长的影响[J]. 湖北农业科学, 2019, 58(7):57-61+66.
[16] 郭小燕, 于帅卿. 一种轻量级YOLO V5S农作物虫害目标检测模型[J/OL]. 南京农业大学学报, 1-13[2023-12-21] http://kns.cnki.net/kcms/detail/32.1148.S.20231204.1720.008.html.
[17] 温维亮, 郭新宇, 张颖, 等. 作物表型组大数据技术及装备发展研究[J]. 中国工程科学, 2023, 25(4):227-23.
Outlines

/