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In order to provide effective data for yield estimation of orchard, a positioning method for apple fruits based on information fusion was proposed. An information acquisition system which consists of a Microsoft Kinect, a GPS (M600 GNSS receiver, Sinan corp.) receiver and an attitude sensor (MPU6050, Inven Sense corp.)was designed, and the world coordinates calculation method for apple fruits in a RGB image was developed according to this system. In this information acquisition system, the Microsoft Kinect (Kinect for Windows V2 i¼Œ Microsoft) is used to acquire both the RGB image and depth information image of apple trees in the apple orchard, the GPS receiver is used to get the position information of the Kinect, and the attitude sensor is used to obtain the attitude information of the Kinect. First, RGB image and depth image is fused to calculate the coordinates of centers of apples using relative positioning model. Secondly, the position and attitude information of the camera are added to establish an absolute positioning model to calculate the world coordinates of apples based on three-dimension coordinate converting method. Thus, each apple gets only one space position in the world. The experiment results showed that the mean error of the relative positioning reached to 3.75cm; and for absolute positioning, the mean distance error of the longitude was 117mm i¼Œ that of the latitude was 437mm i¼Œ and that of the altitude was 145mm.

It is significant to estimate the growth of crops during their growth stages rapidly and non-destructively, the spectral analysis technology could satisfy the requirement and it is one of the important techniques to support in the precision agriculture management. In the article, a system was developed for estimating the growth of crops in greenhouse, which was based on spectrum and WSN (wireless sensor network) technology. The system consisted of three parts: the intelligent sensor nodes, intelligent gateway and the software in remote server. The ZigBee wireless communication modules were embedded in the intelligent sensor nodes and gateway to create WSN. The intelligent sensor nodes contained four optical channels, which allowed these instruments work at the wavelengths of 550, 650, 766 and 850 nm. They were used to collect spectral data and transmit to the intelligent gateway by ZigBee wireless network. The intelligent gateway worked as a coordinator of the whole wireless network waiting for sensor nodes to join in and receiving all the spectral data from sensor nodes, and transmitting the data to remote server by GPRS. The software in remote server analyzed the spectral data, calculated the NDVI (Normalized Difference Vegetation Index), by which, the growth of the plants can be detected effectively. In order to test the effectiveness and stability of the system, the strawberry of greenhouse was detected. Several experiments were carried out in the greenhouse of ZhuoZhou Experiment Station in Hebei. Results showed that the canopy spectral data of strawberries could be acquired by the intelligent sensor nodes stably, and the intelligent gateway could connect the sensor nodes and the remote server effectively. The spectral data can be received by the remote server normally as well. The system provides a kind of technical support and theoretical basis for crop growth estimation in greenhouse.

In this study, a new type of leaf surface temperature and humidity wireless monitoring system was designed. The system is composed by several sensor nodes. The communication among the nodes is ZigBee. The node has three modules: wireless communication module, leaf temperature and humidity measurement module and battery power supply module. The temperature measurement module was made up with a new type high integration infrared contactless temperature measuring sensor A2TPMI334-L5.5OAA300. The sensor with integrated A2TPMI ASIC and 5.5mm lens optics with 5° field of view adapted to an object temperature range –20…+300°C , respectively. The output voltage of the sensor is sent to MCU after the filtering by the operational amplifier. The humidity sensor probe is made by Honeywell HIH-4000-003 humidity sensor. The HIH-4000 Series Humidity Sensors are designed specifically for high volume OEM users. The RH sensor is a laser trimmed, thermoset polymer capacitive sensing element with on-chip integrated signal conditioning.The two channels of voltage signal of the leaf temperature and humidity will be sent to PDA by wireless communication module which has already embedded in MCU. The MCU is JN5139, it satisfies the requirements of self-connecting network and low-power consumption in the field. According to the experiments of corn plants, the results showed that the temperature accuracy of the node reached 0.1℃ after calibration. The humidity accuracy of the node reached ±5%.The communication between nodes and PDA is stable.

The turbidity and chlorophyll content of water are very important parameters in aquaculture. An integrated portable optical detector was developed to measure turbidity and chlorophyll content of water body. This instrument is developed based on the comprehensive measurement method by combining scattering light and transmission light to measure turbidity and the transmission method to measure chlorophyll content. It uses AT89S52 microcontroller as its control chip and consists of the light emission unit, detection unit and control unit. Wherein the light emission unit comprises four sets of LED light source (wavelength of 645nm, 663nm and 860nm) and the corresponding collimating lens; the detection unit comprises four photoelectric sensors; the control unit includes the light source driving circuit, the two-stage amplification circuit and the A/D conversion circuit. The Integrated detector was calibrated in laboratory and carried out field experiments in-situ. The experiments showed that the water detector achieved a high accuracy. To the turbidity measurement, we built the sectionalized model, its calibration R L 2 was 0.9921 and R h 2 was 0.9736, and the validation R L 2 was 0.9996 and R h 2 was 0.9838, with the resolution of 0.01NTU and the accuracy of ± 2%. To the chlorophyll content measurement, after excluding several abnormal points, the calibration R 2 was 0.9652 and the validation R 2 was 0.8118, with the resolution of 0.01mg/L and the accuracy of ± 2%. The results showed that this optical detector reached the practical level.

It is very convenient for farmers to control the devices located in greenhouses remotely and precisely. A smart greenhouse monitoring system was developed based on WSN, 3G and PLC technologies. It consists of a WSN, a 3G communication unit, a PLC control unit and devices (air fans and roller shutter door) in each greenhouse and a monitoring application running on the greenhouse server. The system manages greenhouse for each registered farmer. The WSN is developed using ZigBee technology and each WSN node integrates environment humidity sensor and temperature sensor, and the power for each sensor can be disenabled by the wireless node itself after sending data for 10 times and enabled when receiving the data collecting command from the coordinator. There is a ZigBee coordinator in each greenhouse and it is integrated with a 3G module and a PLC unit. Furthermore, the MCU in ZigBee coordinator is used as the CU for the integrated setup. The devices installed in each greenhouse are connected with the PLC unit and can be triggered and controlled by the PLC module. The WSN collects all of data in given interval. The 3G module is responsible for establishing the connection with the greenhouse server, uploading the collected data to the server and downloading the commands from the server. The PLC unit receives the commands forwarded by the CU and controls each device to execute the specific action.

In this research, samples were collected from JiangSu Province of China. The visible and near infrared spectral reflectance of the water samples were measured. At the same time, the Chlorophyll-a content of water for each sample was measured in the laboratory. Then the spectral characteristics of the water samples were analyzed and the results showed that with chlorophyll-a concentration increasing, spectral transmittance decreased gradually. There was an apparent transmission valley at 676nm. Then multiscale spectral analysis aimed at predicting the chlorophyll-a content in water were conducted, which were consisted of all-wavebands prediction and specific sensitive wavebands prediction. For all-wavebands spectral analysis, this research adopted the SVM technology to establish the regression model based on the all-bands transmittance between 331~900nm. For the specific sensitive wavebands prediction, spectral correlation coefficient analysis was carried out to analyze the sensitive transmittance band of Chlorophyll-a in water. Comprehensive observation on the correlation coefficient curve, the significant correlation could be seen at around 365nm, 550nm and 856nm. It implies that water chlorophyll-a concentration linear forecasting model can be established by the sensitive wavebands selected above. After comparing, the results indicated that, 1) the accuracy of all-wavebands regression model reached to a quite high level, because there is no losing information from the spectra. 2) the sensitive wavebands regression model could also predict the chlorophyll-a content in water practically, and the model was more suitable for embedding in the detector and applying to the agricultural production.

Wireless sensor network (WSN) is an important tool for large-scale field monitoring. It can acquire field information with various sensor nodes at high temporal and spatial resolution. In this paper, in order to accurately get the soil water deficit and then execute effective and water-saving irrigation, a WSN of soil moisture is established and the remote management platform is developed for data collection, storage, and analysis. The WSN of soil moisture system includes twenty sensor nodes, two sink nodes, and a high resolution web-camera. The sensor nodes are located in a corn field and send the data of soil moisture every 30 minutes to the sink node, which connected to a GPRS module. The GPRS module is used to forward the data to the remote management platform using TCP-IP standard protocol. The platform can be browsed in any computer connected with Internet. The distribution of the sensor nodes and all measured data can be read in the platform, which provides an irrigation prescription according to the measurements of soil moisture. The web-camera is used to monitor the corn field to provide images for precision management of the crop. Through a long-time test in field, it is proved that the whole system is stable and reliable, and can be used to support precision irrigation system.

This paper is an overview of a large knowledge based operations optimization system that is partially in operation and in part under development for implementing deep knowledge schemes as well as adapting protocols and hardware to comply with the expected wave on IOT. With the development of sensing, wireless communication, and Internet technologies, we are now living in a world that is filled with various smart things – the Internet of Things. This paper introduces and prospects an emerging research area – the Embedded Intelligence (EI) for agricultural operations. This field, in its humanistic scope, aims at revealing the individual behaviors, spatial contexts, as well as social patterns and urban dynamics by mining the digital traces left by people while interacting with Internet of Smart Things (cameras, smart cars, smart cards, etc). In the agricultural sector we add mining of existing technology (books, articles, blue prints) to generate high depth Ontologies for reasoning and making pro-active decisions. We further include Intelligent Data Analysis to discover new knowledge from data records. The paper discusses the research history, characteristics, general architecture, major applications, and research issues of EI and exemplifies an application of the team in IOT Smart Irrigation. In this paper, a greenhouse management system based on knowledge supporting technology is designed. It includes three parts: a wireless sensor network (WSN), a service platform operating in Internet, and a decision-making support system (DMSS) based on knowledge supporting technology. The wireless sensor network is used to monitor and acquire all types of environmental information for the crop growth, such as air temperature, air relative humidity, CO2 concentration, substrate temperature, soil water content, etc. All data collected by WSN is sent to the service platform via Internet, and then the data are analyzed through DMSS based on cloud service technology. In the DMSS, the data from WSN are sent to the service platform where processed with expert’s knowledge, and then derive an optimal decision to send back to ground equipment to control the greenhouse. This process would realize high resolution optimization management in greenhouse. The efficiency of the whole system based on IoT is expected to succeed in many aspects from environmental indices (IPM) to economic sustainability, based on targets set to the service by the grower.

The need for automatic guidance of farm vehicles has been recognized for a considerable time. Automatic guidance of agricultural vehicles must have some effective method for determining their position in the working environment in order to operate appropriately. Recently, Global Positioning System (GPS) receivers have become available for world-wide and all-weather outdoor localization. This paper presents a review of automatic guidance of agricultural vehicles based on stand-off Global Positioning System (GPS) or the integration of GPS and dead-reckoning system.

An intelligent yield monitor for grain combine harvester was developed. The harvested crop was wheat, and the harvesting combine used to equip the monitor was JL1065, a typical machine with 4-meter swath width in northern China. The monitor can collect four analog signals, grain flow, grain moisture content, grain temperature, and header up/down signal, and two digital signals, ground speed and elevator speed. Two digital signals were sensed by Hall effect elements. The monitor can also synchronously receive DGPS signal. A liquid crystal display and a touch screen were integrated as an I/O interface. Field tests showed a linear relationship between actual yield and the output of the yield monitor. The error between measurement and prediction was less than 3%. It is concluded that the developed intelligent yield monitor is practical.

Variable rate fertilization (VRF), a kind of precision management, was attempted in an experiment farm of winter wheat. The farm was divided into 48 test cells, which the level of the field nutrient varied in different cells in 2004. The hyperspectral reflectance and the remote sensing images of the farm were acquired using a handheld spectroradiometer and an unmanned aircraft respectively, and then the characteristics of the hyperspectral data and the aerial images were calculated. A yield map was obtained by using a combine with yield monitor and DGPS. Soil total nitrogen of each test cells was analyzed by grid-sampling method. The vegetation indices and the color characteristics calculated denoted fertilization status and growing condition of winter wheat. The reflectance showed obvious negative correlation with soil total nitrogen and yield in 520-700 nm and positive correlation with R/(R+G+B). The grain yield showed negative correlation with R/(R+G+B). Soil total nitrogen had a great effect on yield when total nitrogen was less than 200 kg/hm2 at elongating stage of winter wheat. After performing VRF management, the difference of the yield was reduced and the total yield was increased. The efficiency of the fertilizer was improved and the environment pollution was decreased. It could be concluded that the precision management based on the aerial images and the hyperspectral data could promote farming production.