基于传感器的农业温室数据直报系统与智能调控研究

doi:10.13304/j.nykjdb.2023.0925

中国农业科技导报 ›› 2024, Vol. 26 ›› Issue (7): 93-102.DOI: 10.13304/j.nykjdb.2023.0925

基于传感器的农业温室数据直报系统与智能调控研究

熊晓菲¹^,²(), 吴文茜², 霍洪彦², 张馨³, 于艳⁴, 安冬⁵, 张同⁶(), 吴建伟¹^,²()

^1.北京市农林科学院信息技术研究中心, 北京 100971
^2.北京派得伟业科技发展有限公司, 北京 100097
^3.北京市农林科学院智能装备技术研究中心, 北京 100097
^4.上海垄垦信息科技有限公司, 上海 201900
^5.中国农业大学信息与电气工程学院, 北京 100193
^6.烟台大学计算机与控制工程学院, 山东烟台 264005

收稿日期:2023-12-16 接受日期:2024-01-19 出版日期:2024-07-15 发布日期:2024-07-12
通讯作者: 张同,吴建伟
作者简介:熊晓菲 E-mail：xiongxf@pdwy.com.cn
基金资助:
上海市科技兴农项目(沪农科推字2022第3-2号);北京市农林科学院项目(JJP2023-04);北京市科技计划项目(Z221100005822014)

Research on Sensor-based Agricultural Greenhouse Data Direct Reporting System and Intelligent Control

Xiaofei XIONG¹^,²(), Wenqian WU², Hongyan HUO², Xin ZHANG³, Yan YU⁴, Dong AN⁵, Tong ZHANG⁶(), Jianwei WU¹^,²()

^1.Information Technology Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100971, China
^2.Beijing PAIDE Science and Technology Development Co. , Ltd. , Beijing 100097, China
^3.Intelligent Equipment Technology Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China
^4.Shanghai Longken Information Technology Co. , Ltd. , Shanghai 20094, China
^5.College of Information and Electrical Engineering, China Agricultural University, Beijing 100083, China
^6.School of Computer and Control Engineering, Yantai University, Shandong Yantai 264005, China

Received:2023-12-16 Accepted:2024-01-19 Online:2024-07-15 Published:2024-07-12
Contact: Tong ZHANG,Jianwei WU

摘要/Abstract

摘要：

农业物联网、大数据、人工智能等技术的飞速发展为温室蔬菜生产的数据采集、分析、调控提供了有力支撑。为满足温室环境数据智能直报场景需求，制定了温室环境传感器的部署规范，设计并对比分析了基于LSTM、CNN-LSTM及CNN-LSTM-Attention的 3种温室调控温度预测模型。基于CNN-LSTM-Attention预测模型的性能最好，其MSE、MAE、R² 分别为0.457 0、0.319 5和0.987 3。设计了基于ARIMA的温室传感器数据纠错方法，实现土壤湿度预测数据值与实际测量差异不显著。整合了常见温室果蔬种植作物对环境信息的参数阈值模型，并开发了温室数据直报与智能调控系统移动端，能够指导温室环境传感器的规范部署、温度预测与纠错及常见温室果蔬种植辅助决策。研究结果为温室数据直报场景的数据采集、业务分析、温室调控提供技术手段，有助于智慧温室蔬菜产业高质量发展。

关键词: 传感器, 数据直报, 温室应用场景, 温度预测, 数据纠错

Abstract:

The rapid development of agricultural internet of things， big data， artificial intelligence and other technologies have provided strong support for data collection， analysis and regulation of greenhouse vegetable production. In order to meet the requirements of intelligent direct reporting scenarios for greenhouse environmental data， this study developed the deployment specifications of greenhouse environmental sensors. 3 greenhouse control temperature prediction models based on LSTM， CNN-LSTM， and CNN-LSTM-Attention were designed and compared. Among them， CNN-LSTM-Attention prediction model had the best performance， with MSE， MAE and R² of 0.457 0， 0.319 5 and 0.987 3， respectively. The ARIMA-based sensor data error correction method was designed and the difference between the predicted soil moisture data and the actual measurement was not significant. The parameter threshold model of environmental information of common greenhouse fruit and vegetable crops was integrated， and the mobile end of greenhouse data direct reporting and intelligent regulation system was developed. Thus， it could guide the standardized deployment of greenhouse environmental sensors， temperature prediction and error correction， and auxiliary decision-making for common greenhouse fruit and vegetable cultivation. Above results provided technical means for data collection， business analysis， and greenhouse control in greenhouse data direct reporting scenarios， and contributed to the high-quality development of the smart greenhouse vegetable industry.

Key words: sensors, data direct reporting, greenhouse application scenario, temperature prediction, data error correction

中图分类号:

S126

熊晓菲, 吴文茜, 霍洪彦, 张馨, 于艳, 安冬, 张同, 吴建伟. 基于传感器的农业温室数据直报系统与智能调控研究[J]. 中国农业科技导报, 2024, 26(7): 93-102.

Xiaofei XIONG, Wenqian WU, Hongyan HUO, Xin ZHANG, Yan YU, Dong AN, Tong ZHANG, Jianwei WU. Research on Sensor-based Agricultural Greenhouse Data Direct Reporting System and Intelligent Control[J]. Journal of Agricultural Science and Technology, 2024, 26(7): 93-102.

图/表 8

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作物 Crop	物候期 Phenological period	空气温度 Air temperature/℃	空气湿度 Air humidity/%	光照强度 Illumination/lx	二氧化碳含量 Carbon dioxide content/（mg·m^-3）	土壤温度 Soil temperature/℃	土壤湿度 Soil moisture/%
茄子 Eggplant	发芽期 Germination stage	15~30	55~80	8 000~30 000	898~3 592	15~20	60~85
	幼苗期 Seedling stage	15~33	60~80	8 000~30 000	898~3 592	15~20	60~80
	开花结果期 Flowering and fruiting stage	12~34	60~80	8 000~30 000	898~3 592	15~20	60~80
黄瓜 Cucumber	发芽期 Germination stage	10~33	65~85	8 000~20 000	898~3 592	20~25	70~90
	幼苗期 Seedling stage	10~33	65~90	8 000~20 000	898~3 592	20~25	70~80
	开花结果期 Flowering and fruiting stage	10~33	65~85	8 000~20 000	898~3 592	20~25	80~90
豇豆 Cowpea	发芽期 Germination stage	15~34	65~80	8 000~20 000	898~3 592	25~30	60~70
	幼苗期 Seedling stage	15~34	65~80	8 000~20 000	898~3 592	25~30	60~70
	开花结果期 Flowering and fruiting stage	15~34	65~80	8 000~20 000	898~3 592	25~30	60~70
菠菜 Spinach	播种期 Seeding stage	7~26	80~90	8 000~20 000	898~3 592	8~20	70~80
菠菜 Spinach	营养生长期 Vegetative stage	7~26	80~90	8 000~20 000	898~3 592	8~20	70~80
草莓 Strawberry	萌芽期 Germination stage	7~28	70~80	25 000~60 000	539~3 592	15~20	70~80
	现蕾期 Budding stage	7~28	70~80	25 000~60 000	539~3 592	15~20	70~80
	开花期 Flowering stage	7~28	≤60	25 000~60 000	539~3 592	15~20	70~80
	果实膨大期 Fruit formation stage	6~28	70~80	25 000~60 000	539~3 592	15~20	60~80
	收获期 Harvesting stage	5~28	70~80	25 000~60 000	539~3 592	15~20	60~80

作物 Crop	物候期 Phenological period	空气温度 Air temperature/℃	空气湿度 Air humidity/%	光照强度 Illumination/lx	二氧化碳含量 Carbon dioxide content/（mg·m^-3）	土壤温度 Soil temperature/℃	土壤湿度 Soil moisture/%
茄子 Eggplant	发芽期 Germination stage	15~30	55~80	8 000~30 000	898~3 592	15~20	60~85
	幼苗期 Seedling stage	15~33	60~80	8 000~30 000	898~3 592	15~20	60~80
	开花结果期 Flowering and fruiting stage	12~34	60~80	8 000~30 000	898~3 592	15~20	60~80
黄瓜 Cucumber	发芽期 Germination stage	10~33	65~85	8 000~20 000	898~3 592	20~25	70~90
	幼苗期 Seedling stage	10~33	65~90	8 000~20 000	898~3 592	20~25	70~80
	开花结果期 Flowering and fruiting stage	10~33	65~85	8 000~20 000	898~3 592	20~25	80~90
豇豆 Cowpea	发芽期 Germination stage	15~34	65~80	8 000~20 000	898~3 592	25~30	60~70
	幼苗期 Seedling stage	15~34	65~80	8 000~20 000	898~3 592	25~30	60~70
	开花结果期 Flowering and fruiting stage	15~34	65~80	8 000~20 000	898~3 592	25~30	60~70
菠菜 Spinach	播种期 Seeding stage	7~26	80~90	8 000~20 000	898~3 592	8~20	70~80
菠菜 Spinach	营养生长期 Vegetative stage	7~26	80~90	8 000~20 000	898~3 592	8~20	70~80
草莓 Strawberry	萌芽期 Germination stage	7~28	70~80	25 000~60 000	539~3 592	15~20	70~80
	现蕾期 Budding stage	7~28	70~80	25 000~60 000	539~3 592	15~20	70~80
	开花期 Flowering stage	7~28	≤60	25 000~60 000	539~3 592	15~20	70~80
	果实膨大期 Fruit formation stage	6~28	70~80	25 000~60 000	539~3 592	15~20	60~80
	收获期 Harvesting stage	5~28	70~80	25 000~60 000	539~3 592	15~20	60~80

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基于传感器的农业温室数据直报系统与智能调控研究

Research on Sensor-based Agricultural Greenhouse Data Direct Reporting System and Intelligent Control

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