Mini-UAV Based Sensory System for Measuring Environmental Variables in Greenhouses

This paper describes the design, construction and validation of a mobile sensory platform for greenhouse monitoring. The complete system consists of a sensory system on board a small quadrotor (i.e., a four rotor mini-UAV). The goals of this system include taking measures of temperature, humidity, luminosity and CO2 concentration and plotting maps of these variables. These features could potentially allow for climate control, crop monitoring or failure detection (e.g., a break in a plastic cover). The sensors have been selected by considering the climate and plant growth models and the requirements for their integration onboard the quadrotor. The sensors layout and placement have been determined through a study of quadrotor aerodynamics and the influence of the airflows from its rotors. All components of the system have been developed, integrated and tested through a set of field experiments in a real greenhouse. The primary contributions of this paper are the validation of the quadrotor as a platform for measuring environmental variables and the determination of the optimal location of sensors on a quadrotor.

[1]  Francesco Grimaccia,et al.  Architecture and Methods for Innovative Heterogeneous Wireless Sensor Network Applications , 2012, Remote. Sens..

[2]  Huan Liu,et al.  Building a distributed robot garden , 2009, 2009 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[3]  Aleksandrov.  OPTIMAL GAP DISTANCE BETWEEN ROTORS OF MINI QUADROTOR HELICOPTER , 2012 .

[4]  Antonio Barrientos,et al.  An Air-Ground Wireless Sensor Network for Crop Monitoring , 2011, Sensors.

[5]  Pilar Barreiro,et al.  A Review of Wireless Sensor Technologies and Applications in Agriculture and Food Industry: State of the Art and Current Trends , 2009, Sensors.

[6]  P. Zarco-Tejada,et al.  Fluorescence, temperature and narrow-band indices acquired from a UAV platform for water stress detection using a micro-hyperspectral imager and a thermal camera , 2012 .

[7]  Francisco Rodríguez,et al.  Simulation of Greenhouse Climate Monitoring and Control with Wireless Sensor Network and Event-Based Control , 2009, Sensors.

[8]  H. S. Wolff,et al.  iRun: Horizontal and Vertical Shape of a Region-Based Graph Compression , 2022, Sensors.

[9]  Jens Bange,et al.  First application of the meteorological Mini-UAV 'M2AV' , 2007 .

[10]  E. V. Henten,et al.  Greenhouse climate management : an optimal control approach , 1994 .

[11]  Maohua Wang,et al.  Wireless sensors in agriculture and food industry — Recent development and future perspective , 2005 .

[12]  Irene Marzolff,et al.  Unmanned Aerial Vehicle (UAV) for Monitoring Soil Erosion in Morocco , 2012, Remote. Sens..

[13]  F. Tadeo,et al.  Temperature and humidity control in greenhouses using the Takagi-Sugeno fuzzy model , 2006, 2006 IEEE Conference on Computer Aided Control System Design, 2006 IEEE International Conference on Control Applications, 2006 IEEE International Symposium on Intelligent Control.

[14]  Cecilia Stanghellini,et al.  A model of humidity and its applications in a greenhouse , 1995 .

[15]  Kimon P. Valavanis,et al.  Advances in Unmanned Aerial Vehicles: State of the Art and the Road to Autonomy , 2007 .

[16]  Morgan Quigley,et al.  ROS: an open-source Robot Operating System , 2009, ICRA 2009.

[17]  Antonio Barrientos,et al.  Near-optimal coverage trajectories for image mosaicing using a mini quad-rotor over irregular-shaped fields , 2012, Precision Agriculture.

[18]  Sahar Asadi,et al.  Autonomous Gas-Sensitive Microdrone: Wind Vector Estimation and Gas Distribution Mapping , 2012, IEEE Robotics & Automation Magazine.

[19]  Francisco Rodríguez,et al.  Mechatronic Description of a Laser Autoguided Vehicle for Greenhouse Operations , 2013, Sensors.

[20]  Marco Buschmann.  MMAV—a miniature unmanned aerial vehicle (Mini-UAV) for meteorological purposes , 2004 .

[21]  Ning Wang,et al.  Review: Wireless sensors in agriculture and food industry-Recent development and future perspective , 2006 .

[22]  M. Fladeland,et al.  Greenhouse gas analyzer for measurements of carbon dioxide, methane, and water vapor aboard an unmanned aerial vehicle , 2012 .

[23]  Aníbal Ollero,et al.  The autonomous mobile robot AURORA for greenhouse operation , 1996, IEEE Robotics Autom. Mag..

[24]  Alessandro Matese,et al.  A flexible unmanned aerial vehicle for precision agriculture , 2012, Precision Agriculture.

[25]  A. Tsourdos,et al.  Contaminant Cloud Boundary Monitoring Using Network of UAV Sensors , 2008, IEEE Sensors Journal.

[26]  Bruce Wiggins,et al.  Validation of a quad-rotor helicopter Matlab/Simulink and solidworks models , 2013 .

[27]  Qian Zhang,et al.  A wireless solution for greenhouse monitoring and control system based on ZigBee technology , 2007 .

[28]  M. Lieberman,et al.  Influence of Plant Hormones on Ethylene Production in Apple, Tomato, and Avocado Slices during Maturation and Senescence. , 1977, Plant physiology.

[29]  Ian F. Akyildiz,et al.  Wireless sensor networks: a survey , 2002, Comput. Networks.

[30]  L. H. Hernández,et al.  Identificación y control en cascada mediante inversión de no linealidades del cuatrirrotor para el Concurso de Ingeniería de Control CEA IFAC 2012 , 2013 .

[31]  Raphael Linker,et al.  Greenhouse temperature modeling: a comparison between sigmoid neural networks and hybrid models , 2004, Math. Comput. Simul..

[32]  Sung-Eon Cho,et al.  A Study on Greenhouse Automatic Control System Based on Wireless Sensor Network , 2011, Wirel. Pers. Commun..

[33]  Gareth Halfacree,et al.  Raspberry Pi User Guide , 2012 .

[34]  Pascual Campoy,et al.  Identification and cascade control by nonlinearities reversion of a quadrotor for the Control Engineering Competition CEA IFAC 2012 , 2013 .

[35]  Kang Sun,et al.  Low Power Greenhouse Gas Sensors for Unmanned Aerial Vehicles , 2012, Remote. Sens..