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Xiaofeng Li

Bio: Xiaofeng Li is an academic researcher from University of Science and Technology of China. The author has contributed to research in topics: Greenhouse & Wireless sensor network. The author has an hindex of 3, co-authored 3 publications receiving 28 citations.

Papers
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Proceedings ArticleDOI
01 Jul 2017
TL;DR: The experimental results show that the system can realize wireless collection of greenhouse environment data and the control of main equipment in the greenhouse, and the data packet loss rate is zero, which is applicable for small greenhouse monitoring and control with good accuracy and scalability.
Abstract: The cable acquisition and control system of the traditional greenhouse has many disadvantages such as complicated wiring, high cost and lack of accuracy and expansibility. In order to solve those problems, a greenhouse acquisition and control system based on ZigBee wireless sensor network (WSN) is designed in this paper. The ZigBee with temperature sensor DS18B20 and humidity sensor AM2301 is used as the sensor node, the fan and the wet curtain formed as two controller nodes which are respectively connected with the ZigBee module. The coordinator receives environmental data collected by the sensor nodes, and then sends the control commands to the controller nodes after processing and analyzing the data, which changes the execution status of the fan and the wet curtain, so as to adjust temperature and humidity inside the greenhouse, providing suitable growth environment for crops. The experimental results show that the system can realize wireless collection of greenhouse environment data and the control of main equipment in the greenhouse. The greenhouse acquisition and control system based on WSN with low cost and low power consumption has reduced labour intensity, and the data packet loss rate is zero, which is applicable for small greenhouse monitoring and control with good accuracy and scalability.

19 citations

Proceedings ArticleDOI
27 Jul 2016
TL;DR: This system designs a wireless sensor and control system based on ZigBee to achieve the wireless collection and control of the greenhouse, which can effectively save the cost of wiring and human resources, and provide an intelligent solution for the Modern Greenhouse to be wireless and intelligent.
Abstract: Collection of environmental parameters and control of equipment in greenhouse are the main contents of the greenhouse management. Aiming at the defect of traditional wired monitoring system, this system designs a wireless sensor and control system based on ZigBee. With CC2530 wireless network chip as the core, its peripheral interface has been integrated with AM2301, DS18B20 temperature and humidity sensors. The underlying program is compiled to build wireless network to collect greenhouse temperature and humidity data. So we can measure and transmit the data, and realize the wireless control of the adjusting device. The experimental results show that the system can achieve the wireless collection and control of the greenhouse, which can effectively save the cost of wiring and human resources, and provide an intelligent solution for the Modern Greenhouse to be wireless and intelligent.

9 citations

Proceedings ArticleDOI
27 Jul 2016
TL;DR: In this article, two modeling approaches, partial least square regression (PLSR) and back-propagation neural network (BPNN), are respectively adopted to predict greenhouse inside temperature based on the environmental information obtained including outside temperature, humidity(inside and outside), solar radiation, wind speed and direction.
Abstract: To achieve the precise control of the greenhouse, further enhance energy efficiency, it is necessary to clearly understand the greenhouse temperature system's characteristics. This work is concerned with the greenhouse temperature system in the typical summer climate under east-central China conditions. Firstly, fuzzy control algorithm is used to obtain reasonable greenhouse inside temperature range on the Android client. Secondly, two modeling approaches which are partial least-square regression (PLSR) and back-propagation neural network (BPNN) are respectively adopted to predict greenhouse inside temperature based on the environmental information obtained including outside temperature, humidity(inside and outside), solar radiation,wind speed and direction. At last, the predicted inside temperature value is compared with the measured value, and the experiment results show that the prediction accuracy of BPNN is better than PLSR around the premise of both methods achieving the ideal effects.

6 citations


Cited by
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Journal ArticleDOI
TL;DR: In this paper, the authors present the research efforts from researchers in Smart Farming, who apply innovative technology trends in various crops around Europe and provide and analyze the most significant projects in Europe in the area of Smart Farming.

58 citations

Proceedings ArticleDOI
01 Aug 2018
TL;DR: In this article, a Raspberry Pi 3 based circuit is used to continuously monitor soil quality with the help of wireless sensor nodes in order to get maximum development of plant in greenhouse environment.
Abstract: As we know greenhouse parameters monitoring and controlling plays a very important role for quality production of crops. The purpose of this system is to design a simple Raspberry Pi 3 based circuit to continuously watch & read the values of Soil moisture, Humidity, Temperature and light of the environment that are constantly changed and controlled in order to get maximum development of plant. In this paper we present a system to monitor soil quality with the help of wireless sensor nodes. The data is acquired from each sensor used in this system. In past Attention was needed for a farmer to protect his field from different disasters caused either by human or by nature. Efforts of human are not sufficient and also farmer has to pay for manpower. Here we are using few sensors to monitor the field are Temperature sensor, Humidity Sensor, Soil Moisture sensor to check whether the field is dry or wet and a LDR to verify the lighting at that place. This system maintains the soil quality which is required to grow the particular crop properly. By using this project the farmer can Predict &Analyze the greenhouse parameters. Tomatoes & Brinjals these two crops are selected for the prediction & Analysis. Two samples of crops are taken and the system had been verified for these crops in greenhouse environment. Finally total power consumption and total expenditures consumed per year is estimated for controlling devices. Because of this the farmers will be able to predict the total amount for controlling action of crops for next year. By using this system it is seen that with controlling action the product quality & quantity is increased than crops grows without controlling action

35 citations

Proceedings ArticleDOI
01 Oct 2017
TL;DR: The proposed OWM system uses continue data sampling with higher sampling rate on local main station system and can monitor water quality in real-time and historically both on the local and online system.
Abstract: A water monitoring is required to maintain water quality for human and animal life. T o verify and monitor the water quality in a large area such as lake, river, and aquaculture requires an online water monitoring (OWM) system. The OWM system usually consists of some quantitative measurements such as temperature, pH, Dissolved Oxygen, Turbidity, Conductivity, TDS, Salinity, etc. Most of the used OWM system use discontinue data sampling that the sampling rate is longer than one minute and one hour for local measurement and online measurement, respectively. There is no historical measurement record to trace the initial problem when the water quality getting worse. In this paper, the proposed OWM system uses continue data sampling with higher sampling rate on local main station system. This system is also integrated with WIFI. Therefore, the measurement by the local main station can be done in real-time. In this system, the old data measurement can be analyzed by using historical view feature. The experimental result verifies that the system can monitor water quality in real-time and historically both on the local and online system.

22 citations

Journal ArticleDOI
TL;DR: The paper proposed a promising type of hybrid robotic fish (HRF), which can realize two kinds of motion modes on the sea surface, and proposed a velocity prediction algorithm based on back-propagation neural network (BPNN), which has higher accuracy and efficiency compared with other prediction algorithms.
Abstract: Marine unmanned vehicle is a novel robot widely used in ocean observation, and its accurate control is of significance to their path planning We want to find a method to predict the velocity and course of this robot, which can help us realize the accurate control of it The paper proposed a promising type of hybrid robotic fish (HRF), which can realize two kinds of motion modes on the sea surface Firstly, the configuration and dynamic model of the HRF were analyzed elaborately Then, to realize accurate velocity prediction under two kinds of motion modes of HRF, the influence factors are presented in a complex marine environment Based on the influence factors to its maneuverability, such as wind or wave parameters, a velocity prediction algorithm based on back-propagation neural network (BPNN) was introduced However, BPNN has the disadvantages of extended learning and training time, easily falling into local optimum Then we found that genetic algorithm (GA), which is a kind of evolutionary algorithm, is suitable for our problem Therefore, the accuracy and efficiency of the prediction algorithm were improved by adopting the genetic algorithm to optimize the weight and threshold of BPNN Taking the experimental data from the pool test, the back-propagation neural network with genetic algorithm (GA-BPNN) forecasting model was established Besides, the other prediction methods were compared and evaluated under the same assessment criterion to validate the proposed forecasting model The experimental results demonstrate that the GA-BPNN model has higher accuracy and efficiency compared with other prediction algorithms, which verifies the feasibility of the velocity prediction model for hybrid robotic fish in complex ocean environments

19 citations

Proceedings ArticleDOI
01 Jul 2017
TL;DR: The experimental results show that the system can realize wireless collection of greenhouse environment data and the control of main equipment in the greenhouse, and the data packet loss rate is zero, which is applicable for small greenhouse monitoring and control with good accuracy and scalability.
Abstract: The cable acquisition and control system of the traditional greenhouse has many disadvantages such as complicated wiring, high cost and lack of accuracy and expansibility. In order to solve those problems, a greenhouse acquisition and control system based on ZigBee wireless sensor network (WSN) is designed in this paper. The ZigBee with temperature sensor DS18B20 and humidity sensor AM2301 is used as the sensor node, the fan and the wet curtain formed as two controller nodes which are respectively connected with the ZigBee module. The coordinator receives environmental data collected by the sensor nodes, and then sends the control commands to the controller nodes after processing and analyzing the data, which changes the execution status of the fan and the wet curtain, so as to adjust temperature and humidity inside the greenhouse, providing suitable growth environment for crops. The experimental results show that the system can realize wireless collection of greenhouse environment data and the control of main equipment in the greenhouse. The greenhouse acquisition and control system based on WSN with low cost and low power consumption has reduced labour intensity, and the data packet loss rate is zero, which is applicable for small greenhouse monitoring and control with good accuracy and scalability.

19 citations