Internet of Things in Aquaculture. Daoliang Li

Transcription

1 Internet of Things in Aquaculture Daoliang Li Beijing Engineering Research Center for Internet of Things in Agriculture, China Agricultural University

2 2 Main Contents 1 Background 2 Internet of Things 3 Internet of things for aquaculture 4 Future Works

3 Background fresh water Sea water /7/15 3

4 Traditional aquaculture to modern aquaculture

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7 Background Farm scale will be larger and larger Labor cost increasing Automatic control and mechanization are initial required by farm Water resources and environment Logistics and quality traceability Intensive, healthy and sustainable aquaculture are the trends of aquaculture development

8 Modern Aquaculture

9 Modern Aquaculture

10 Modern aquaculture Intensive High productivity Automatic operation Facility controlled Ecological and safety

11 Background-Conclusions Modern Aquaculture needs the suport of ICTs Smart sensor for the water quality Fish behavior and equipment mornitoring Automatic control and information management Facility management and fault diagnosis Logistics and fishery products quality traceability

12 12 Main Contents 1 Background 2 Internet of Things 3 Internet of things for aquaculture 4 Future Works

13 What s the Internet of Things Definition (1) The term "Internet of Things" has come to describe a number of technologies and research disciplines that enable the Internet to reach out into the real world of physical objects IoT 2008 (2) Things having identities and virtual personalities operating in smart spaces using intelligent interfaces to connect and communicate within social, environmental, and user contexts IoT in 2020

14 What s the Internet of Things The Internet of Things(IoT) is a technological revolution of computing and communications. It is defined as a world-wide network of smart interconnected underwater objects with a digitalentity. These devices sense, interpret and react to the environment due to the combination of the Internet, powerful tracking technologies and embedded sensors.

15 What s the Internet of Things From any time,any place connectivity for anyone, we will now have connectivity for anything!

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17 Internet of Things Integrated Technologies Comprehensive sensing RFID,Tag Sensor GPS Reliable telecommunication IP,WiFi,2G,3G,4G Intelligent Information processing Optimization, prediction, control, intelligent reasoning, cloud computing

18 18 Main Contents 1 Background 2 Internet of Things 3 Internet of things for aquaculture 4 Future Works

19 Internet of Things in aquaculture PC Internet Mobile phone GSM/GPRS PDA Wireless collection node... Relay node Relay node Wireless monitoring node Wireless monitoring node ph sensor EC sensor DO sensor Aerator Water level sensor Temperature sensor

20 Intelligent Sensors-Circuit TEDS RS485 Sensor NTC Signal Transmitter Circuit MCU MSP430F149 Power Supply Power Management Self-Tuning Self-Calibration Self-Compensation

21 Self-Calibration and self-compensation f(x 1,X 2 ) D(1) D(2) D( n) i j p C [ ][ ] [ ] i, j, px 1 H 1 X 2 H 2 X n H n i= 0 j= 0 p= 0 X 2 的 分 段 X 2 X 1 的 分 段 单 元 X 1 f ( X = C 1 00, X 2 + C ) 01 ( X 1 i= 0 j= = H C 1 ij [ X ) + C 1 10 H ( X 2 1 ] i [ X H 2 2 H ) + C 11 2 ] j ( X 1 H 1 )( X 2 H 2 )

22 water quality smart sensors / Transducer First Generation ph EC DO WL

23 Moisture solar radiation temperature water quality smart sensors / Transducer 2nd Generation DO ph Chlorophyll WL Turbidity ph

24 Smart sensor-second Generation DO PH EC Temperature Turbidity Water level

25 3 rd generation Temperature conductivity Compensation ; Self-washing Copper protector Lower power

26 4 th Generation Sensors C- WTS101(Temperatur e chain) C-DO102(DO) C-PH101(pH) C-TD101(water level and

27 Sensors and sensor nodes C-EC101 (salinity ) C-MT101(soil moisture Model 9100(sensor node for fish farming) Model 902(sensor node for livestock )

28 Optical sensor Hardware structure of the transducer

29 Optical sensors for DO and turbidity DO, Turbidity and Chlorophyll 光 学 溶 氧 传 感 器 结 构 浊 度 传 感 器

30 On going research sensors Optical Do Chlorophyll Turbidity Ammonia Wave pressure Integrated sensor

31 Main Characteristics of water quality smart sensor Built-in calibration curves, a direct output of engineering parameters; Built-in temperature sensor to achieve ultra-low-voltage power supply temperature, conductivity of self-calibration,low-power (3.6V, 15mW), RS485 output, automatic identification sensor types

32 Wireless Sensor Network-Topology Invent the large-scale, distributed, low power, low cost wireless sensor network for aquaculture and fishery, which realize the remote dynamic monitoring and controlling of water quality.

33 Wireless Sensor Network-Topology The wireless sensor network system consists of four parts: wireless sensor nodes, which detect the water quality parameters, such as PH, DO, Electrical Conductivity (EC), water temperature (T); routing nodes, which transfer the collected water quality parameter; on-site monitoring center, which assist the onsite administrator to monitor the water quality; remote monitoring center, which provide decision support.

34 Wireless Sensor Network-Sensor Node Multi-mode power supply low-power chip (2.7 V) Multi-mode communications(rf GPRS CDMA) maintenance-free battery Power supply Zigbee wireless protocol RF module microprocessor module data storage module RS485 Power supply water temperature sensors

35 Wireless Sensor Network-Control Node Real-time Remote Control Intelligent Control Energy Saving

36 Intelligent Sensors node/control node-products

37 Sensor node Model 801(data collector) Model 701 Model 602 WSN Model 402 M2M node

38 actutor Model 102(actutor) Outdoor Water monitoring platform Online monitoring Indoor Water monitoring

39 Two-GPRS-based wireless data nodes

40 wireless sensor node and control node

41 Intelligent Information Processing intelligent Prediction, warning and controlling of Water quality Precision feeding decisions Disease early warning and remote fidiagnostics

42 Intelligent Information Processing Early warning and forecasting model for the aquaculture water quality. Automatic control method based on fuzzy PID for water quality. Intelligent management system for water quality.

43 Intelligent Information Processing Optimization model and intelligent feeding decision-making technologies to solve the problem of precision feeding problem. 摄 食 率 与 环 境 参 数 关 系 Relation between eating rate and water quality parameters

44 Intelligent Information Processing Early warning and remote diagnosis technology for fish disease diagnosis to solve the problem of disease Control and prevention.

45 Fish behavior identification

46 System Integration Software Platform

47 System Integration Software Platform

48 Mobile System and platform

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54 Yixing Crab DO monitor and Control system 2012

55 Application demonstration -Recirculation Aquaculture in Tianjin

56 Application and demonstration Abalone white pomfret Crab Cynoglossus semilaevis Sea cucumber Tilapia Penaeus vannamei Scallops Puffer

57 Some case studies all over China 湖 北 大 明 水 产 盘 锦 光 合 山 东 景 明 水 产 北 京 通 州 江 苏 宜 兴 天 津 海 发 山 东 寻 山 集 团 浙 江 象 山 福 建 宁 德

58 58 Main Contents 1 Background 2 Internet of Things 3 Internet of things for aquaculture 4 Future Works

59 Future works-user needs Reduce costs-cheap products High reliability-extend the service life Easy to Maintain-Lower operating costs Easy to use- Widely Spread

60 Field research base

61 Fabrication base in Fujian

62 Future works Nanosensor Biosensor Smart-WSN Cloud computing and big data processing Computer version and behavior research Robots

63 Future works Parameters which are difficult acquired Nanosensor biosensor Metal Ammonia Bacteria

64 Future works Mobile Internet

65 Future works Big data

66 Future works Big data Multi-source data fusion Data mining Behavior analysis Water quality control Facility management and fault diagnosis Logistics and quality traceability

67 Thank you for your attention!