INFORMATION CHANGE THE WORLD

International Journal of Information Engineering and Electronic Business(IJIEEB)

ISSN: 2074-9023 (Print), ISSN: 2074-9031 (Online)

Published By: MECS Press

IJIEEB Vol.10, No.2, Mar. 2018

Automated Agricultural Field Analysis and Monitoring System Using IOT

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Author(s)

Kajol R, Akshay Kashyap K, Keerthan Kumar T G

Index Terms

IoT;Cloud computing;image processing;line follower;Raspberry pi;sensor

Abstract

Smarter world is the resultant of the smarter technology. Agriculture was implemented in the nine of sedentary human civilization and it is the backbone of our Indian Economy. But the same traditional techniques like manual field monitoring, water feeding, pest detection, soil testing, etc., we are using for monitoring the field and frequently applying pesticides with or without having the knowledge of quantity to be used to control pests that affect the crops. So it is very important to enhance the agricultural production by making use of technology to overcome the damages being done. Our aim is to provide smart monitoring system using the current technologies like IoT, cloud computing and image processing. To address the above problems the authors of this paper are coming up with a model named “AAFAMS”( Automated Agricultural Field Analysis and Monitoring System Using IOT) which is used not only for monitoring the field but also to suggest the farmers about the moisture content in soil, detecting pest and the type of crop suited for the soil. In AAFAMS, a line follower robot is developed by using a hardware kit called Raspberry pi, which monitors the soil moisture level at every 100m distance using a soil moisture sensor and the information obtained from the sensor will be sent to cloud for storage. A camera will be connected to the AAFAMS which will detect the pests. After complete survey of field AAFAMS retrieves all stored data from cloud and SQLite database to provide a detailed report of Moisture content and Pests information and suggests farmer with the required pesticide. AAFAMS runs either on batteries or solar panel by utilizing the solar energy available and thereby helping farmers to monitor their fields effectively.  

Cite This Paper

Kajol R, Akshay Kashyap K, Keerthan Kumar T G," Automated Agricultural Field Analysis and Monitoring System Using IOT", International Journal of Information Engineering and Electronic Business(IJIEEB), Vol.10, No.2, pp. 17-24, 2018. DOI: 10.5815/ijieeb.2018.02.03

Reference

[1]Dlodlo, N., & Kalezhi, J. (2015). The internet of things in agriculture for sustainable rural development 2015 International Conference on Emerging Trends in  Networks and Computer Communications (ETNCC). doi:10.1109/etncc.2015.7184801 

[2]Rupanagudi, S. R., S., R. B., Nagaraj, P., Bhat, V. G., & G, T. (2015). A novel cloud computing based smart farming system for early detection of borer insects in tomatoes. 2015 International Conference on Communication, Information & Computing Technology  (ICCICT). doi:10.1109/iccict.2015.7045722

[3]Hiary, H. A., Ahmad, S. B., Reyalat, M., Braik, M., & Alrahamneh, Z. (2011). Fast and Accurate Detection and Classification of Plant Diseases. International Journal of Computer Applications, 17(1), 31-38. doi:10.5120/21832754.  

[4]Tuli, A., Hasteer, N., Sharma, M., & Bansal, A. (2014). Framework to leverage cloud for the modernization of the Indian agriculture system. IEEE International Conference on Electro/Information Technology.  doi:10.1109/eit.2014.6871748  

[5]Singhpannu, G., Ansari, M. D., & Gupta, P. (2015). Design and Implementation of Autonomous Car using Raspberry Pi. International Journal of Computer Applications, 113(9), 22-29. doi:10.5120/19854-1789 

[6]Bashish, D. A., Braik, M., & Bani-Ahmad, S. (2011). Detection and Classification of Leaf Diseases using Kmeans-based Segmentation and Neural-networks-based Classification. Information Technology Journal, 10(2), 267275. doi:10.3923/itj.2011.267.275 

[7]Patil, P., H., V., Patil, S., & Kulkarni, U. (2011). Wireless Sensor Network for Precision Agriculture. 2011 International Conference on Computational Intelligence and Communication Networks. doi:10.1109/cicn.2011.169

[8]Kaewmard, N., & Saiyod, S. (2014). Sensor data collection and irrigation control on vegetable crop using smart phone. 2014 IEEE Conference on Wireless Sensors (ICWiSE). doi:10.1109/icwise.2014.7042670 

[9]Miranda, J. L., Gerardo, B. D., & Iii, B. T. (2014). Pest Detection and Extraction Using Image Processing Techniques. International Journal of Computer and Communication Engineering,3(3), 189-192. doi:10.7763/ijcce.2014.v3.317 

[10]https://www.growwater.org/plantneeds.html, March 2017. 

[11]www.nbpgr.ernet.in, March 2017.

[12]Megha P Arakeri, Malavika Arun, Padmini R  K“Analysis of Late Blight Disease in Tomato Leaf Using Image Processing Techniques I.J. I.J. Intelligent Systems and Applications, 2016, 9, 56-61 Published Online September 2016 in MECS (http://www.mecs-press.org/) DOI: 10.5815/ijisa.2016.09.07

[13]Kaushik Bhagawati*, Rupankar Bhagawati and Doni Jini ICAR Research Complex for NEH Region, Arunachal Pradesh Cen “Intelligence and its Application in Agriculture: Techniques to Deal with Variations and Uncertainties“I.J. Intelligent Systems and Applications, 2016, 9, 56-61 Published Online September 2016 in MECS (http://www.mecs-press.org/) DOI: 10.5815/ijisa.2016.09.07

[14]“Forecasting Cloudlet Development on Mobile Computing Clouds” Rashid G. Alakbarov, Fahrad H. Pashaev, Oqtay R. Alakbarov  I.J. Education and Management Engineering, 2017, 5, 35-44 Published Online September 2017 in MECS DOI: 10.5815/ijitcs.2017.11.03

[15]EduCloud: A Dynamic Three Stage Authentication Framework to Enhance Security in Public Cloud G. Kumaresan, N.P. Gopalan I.J. Education and Management Engineering, 2017, 5, 35-44 Published Online September 2017 in MECS DOI: 10.5815/ijem.2017.06.02

[16]Development of Cloud Based Incubator Monitoring System using Raspberry Pi  Mala Sruthi B, S. Jayanthy I.J. Education and Management Engineering, 2017, 5, 35-44 Published Online September 2017 in MECS DOI: 10.5815/ijeme.2017.05.04