Raspberry Pi as Visual Sensor Nodes in Precision Agriculture: A Study

Wireless sensor network applications in the agricultural sector are gaining popularity with the advancement of the Internet of Things technology. Predominantly, wireless sensor networks are used in agriculture to sense the important agricultural field parameters, such as temperature, humidity, soil moisture level, nitrite content in the soil, groundwater quality, and so on. These sensed parameters will be sent to a remote station, where it will be processed and analyzed to build a decision support system. This paper describes the implementation of a wireless visual sensor network for precision agriculture to monitor paddy crop for weeds using Raspberry Pi. Bluetooth 4.0 was used by visual sensor nodes to send the data to the base station. Base station forwarded the data to the remote station using IEEE 802.11 a/b/g/n standard. The solar cell battery was used to power up the sensor nodes and the base station. At the remote station, images were preprocessed to remove soil background and different shape features were extracted. Random forest and support vector machine classifiers were used to classify the paddy crop and weed based on the shape features. The results and observations obtained from the experimental setup of the system in a small paddy field are also reported. This system could be expected to enhance the crop production by giving timely advice to the crop producers about the presence of weeds so that steps can be taken to eradicate weeds.

[1]  Ngan Nguyen Le,et al.  Wireless sensing modules for rural monitoring and precision agriculture applications , 2018, J. Inf. Telecommun..

[2]  Christopher G. Harris,et al.  A Combined Corner and Edge Detector , 1988, Alvey Vision Conference.

[3]  David G. Michelson,et al.  RSSI-Based Distributed Self-Localization for Wireless Sensor Networks Used in Precision Agriculture , 2015, IEEE Transactions on Wireless Communications.

[4]  Leo Breiman,et al.  Random Forests , 2001, Machine Learning.

[5]  T. V. Prabhakar,et al.  Commonsense net: A wireless sensor network for resource-poor agriculture in the semiarid areas of developing countries , 2007 .

[6]  L. P. Deshmukh,et al.  Monitoring the soil parameters using IoT and Android based application for smart agriculture , 2018 .

[7]  Faisal Ahmed,et al.  Classification of crops and weeds from digital images: A support vector machine approach , 2012 .

[8]  Qinjie Chu,et al.  Echinochloa crus-galli genome analysis provides insight into its adaptation and invasiveness as a weed , 2017, Nature Communications.

[9]  N. Golmie,et al.  Bluetooth adaptive frequency hopping and scheduling , 2003, IEEE Military Communications Conference, 2003. MILCOM 2003..

[10]  F. Pedersini,et al.  Automatic monitoring of pest insects traps by Zigbee-based wireless networking of image sensors , 2011, 2011 IEEE International Instrumentation and Measurement Technology Conference.

[11]  S. Barrett.  Crop mimicry in weeds , 1983, Economic Botany.

[12]  Fridrun Podczeck,et al.  A shape factor to assess the shape of particles using image analysis , 1997 .

[13]  Giuseppe Lo Re,et al.  A wireless sensor network for vineyard management in Sicily (Italy) , 2013 .

[14]  Federico Viani,et al.  Low-Cost Wireless Monitoring and Decision Support for Water Saving in Agriculture , 2017, IEEE Sensors Journal.

[15]  Arnold W. M. Smeulders,et al.  Vector code probability and metrication error in the representation of straight lines of finite length , 1982, Comput. Graph. Image Process..

[16]  Jason Weston,et al.  A user's guide to support vector machines. , 2010, Methods in molecular biology.

[17]  D. Proffitt,et al.  Metrication errors and coding efficiency of chain-encoding schemes for the representation of lines and edges , 1979 .

[18]  Hyun Yoe,et al.  Study on an Agricultural Environment Monitoring Server System using Wireless Sensor Networks , 2010, Sensors.

[19]  Bernhard E. Boser,et al.  A training algorithm for optimal margin classifiers , 1992, COLT '92.

[20]  Farzad Kiani,et al.  Wireless Sensor Network and Internet of Things in Precision Agriculture , 2018 .

[21]  Larry S. Davis,et al.  A Corner-Finding Algorithm for Chain-Coded Curves , 1977, IEEE Transactions on Computers.

[22]  Narendra Singh Raghuwanshi,et al.  Wireless sensor networks for agriculture: The state-of-the-art in practice and future challenges , 2015, Comput. Electron. Agric..

[23]  Abdolabbas Jafari,et al.  Evaluation of support vector machine and artificial neural networks in weed detection using shape features , 2018, Comput. Electron. Agric..

[24]  Aline Baggio,et al.  Wireless sensor networks in precision agriculture , 2005 .

[25]  Guy Lapalme,et al.  A systematic analysis of performance measures for classification tasks , 2009, Inf. Process. Manag..

[26]  Jaime Lloret,et al.  A Wireless Sensor Network for Vineyard Monitoring That Uses Image Processing , 2011, Sensors.

[27]  Eldert J. van Henten,et al.  Improved vegetation segmentation with ground shadow removal using an HDR camera , 2018, Precision Agriculture.

[28]  Karim Foughali,et al.  Monitoring system using web of things in precision agriculture , 2017, FNC/MobiSPC.

[29]  Tsvetelina Georgieva,et al.  Design of Wireless Sensor Network for Monitoring of Soil Quality Parameters , 2016 .

[30]  Gianfranco Manes,et al.  Development of Wireless Sensor Networks for Agricultural Monitoring , 2012 .

[31]  Jörn Ostermann,et al.  A Crop/Weed Field Image Dataset for the Evaluation of Computer Vision Based Precision Agriculture Tasks , 2014, ECCV Workshops.

[32]  Lei Tian,et al.  A promising trend for field information collection: An air-ground multi-sensor monitoring system , 2018, Information Processing in Agriculture.

[33]  Luiz Affonso Guedes,et al.  The Coverage Problem in Video-Based Wireless Sensor Networks: A Survey , 2010, Sensors.

[34]  Rosdiadee Nordin,et al.  Energy-Efficient Wireless Sensor Networks for Precision Agriculture: A Review , 2017, Sensors.

[35]  Lothar Thiele,et al.  A Benchmark for Low-power Wireless Networking , 2016 .

[36]  Ming-Kuei Hu,et al.  Visual pattern recognition by moment invariants , 1962, IRE Trans. Inf. Theory.

[37]  D. Y. Wu,et al.  Design of Agricultural Internet of Things Monitoring System Based on Zigbee , 2016 .

[38]  Leila Azouz Saidane,et al.  Context Aware Wireless Sensor Network Suitable for Precision Agriculture , 2016 .

[39]  S. N. Merchant,et al.  Data mining and wireless sensor network for agriculture pest/disease predictions , 2011, 2011 World Congress on Information and Communication Technologies.

[40]  Junho Bang,et al.  The State-of-the-Art of Knowledge-Intensive Agriculture: A Review on Applied Sensing Systems and Data Analytics , 2018, J. Sensors.