摘要
针对设施农场存在劳动强度大、数字化与标准化程度不足以及智能化水平低等问题,研究旨在通过规范生产管理流程和提高温室自动化水平,降低劳动强度并提升农场精准化管理能力。基于Spark框架,研发设施智慧农场大数据平台。利用实验室研发的“神农物联”系列物联网系统,实现了设施环境的实时精准感知;基于钟模型构建作物生长发育模型,并融合构建知识规则,实现设施农事全过程管理的标准化;通过生长发育模型判断物候期,并运用LSTM模型对温室环境进行预测,在预测基础上实现对温室环境的智能精准控制。实验结果表明,平台显著提升了管理能力和智能精准控制水平,为设施农场的现代化管理提供了有力支撑。
In view of the problems of high labor intensity,insufficient digitization and standardization,and low level of intelligence in facility farms,this research aims to reduce labor intensity and enhance the precision management capabilities of farms by standardizing the production management process and improving greenhouse automation.Based on the Spark framework,a big data platform for smart farms was developed.The real-time and accurate sensing of the greenhouse environment was realized by using the'ShenNong'series of Internet of Things system developed by our laboratory.Based on the clock model,the crop growth and development model was constructed and the knowledge rule base of crop growth and development was integrated to realize the standardization of the whole process management of the facility agriculture.The phenological period is judged by the growth and development model,the LSTM model is used to predict the greenhouse environment,and the intelligent and precise control of the greenhouse environment is realized on the basis of the prediction.The experimental results indicated that the platform significantly improves the management ability and the level of intelligent and precise control,and providing strong support for the modern management of facility farms.
作者
张洪奇
张艳
张晨
吴勇
张大磊
柳平增
ZHANG Hong-qi;ZHANG Yan;ZHANG Chen;WU Yong;ZHANG Da-lei;LIU Ping-zeng(School of Information Science and Engineering/Shandong Agricultural University,Tai'an,271018,China;Agricultural Big Data Research Center of Shandong Agricultural University,Tai'an,271018,China;Key Laboratory of Huanghuaihai Smart Agricultural Technology,Ministry of Agriculture and Rural Affairs,Tai'an,271018,China;Shandong Yong guan Agricultural Science and Technology Development,Heze,274900,China;Taishan College of Science and Technology,Tai'an,271038,China)
出处
《山东农业大学学报（自然科学版）》
北大核心
2024年第3期295-303,F0003,共10页
Journal of Shandong Agricultural University：Natural Science Edition
基金
山东省科技特派员项目(2020KJTPY078)
山东省科技成果转化项目(YDZX2022073)。
关键词
设施农业
智慧农场
大数据平台
人工智能
Facility agriculture
intelligent farm
big data platform
artificial intelligence
