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Neha Kailash Nawandar
Visvesvaraya National Institute of technology Nagpur
India
Vishal Satpute
Visvesvaraya National Institute of technology Nagpur
India
Vol. 8 No. 2 (2019), Articles, pages 75-85
DOI: https://doi.org/10.14201/ADCAIJ2019827585
Accepted: Feb 24, 2020
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Abstract

India is an agricultural country with an ample amount of arable land that produces wide variety of crops. Growing population and urbanization puts up challenges: more and quality yield in limited area, effective utilization of water resources, inculcating technology with traditional mechanisms, to be faced. A crop irrigation management system with sensor data fetch, transfer and operate functionalities is proposed to meet the expectations. The system comprises of: sensing, data processing and actuator sections, with a network of ambient temperature and humidity at a height and, soil moisture sensor placed at the root zone of the subject. The sensor generated data is compressed and then sent to an FTP server for processing. At the server, a 2-layer Neural Network with 4-Inputs, plant growth, temperature, humidity and soil moisture is used for decision making that controls water supply, fertilizer spray, etc. and a plant is used as the test object. Results show that there is tolerable error in the reconstructed data and 62.5% and 67.5% compression is achieved for ambient temperature, humidity and soil moisture respectively. The decisions are only 2% erroneous when done using Neural Networks using this data. Thus, due to its good data handling, decision making capabilities for precise water usage, being portable and user-friendly, this system proves beneficial in home gardens, greenhouses.

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References

Abdullah, A., Al Enazi, S., and Damaj, I., 2016. AgriSys: A smart and ubiquitous controlled-environment agriculture system. In Big Data and Smart City (ICBDSC), 2016 3rd MEC International Conference on, pp. 1-6. IEEE.

Abrol, I., 2000. Agriculture in India. Centre for Advancement of Sustainable Agriculture, Retrieved from http://www. google. co. in/url.

Ahmed, N., Natarajan, T., and Rao, K. R., 1974. Discrete cosine transform. Computers, IEEE Transactions on, 100(1):90-93.

Alemu, D. and Negash, S., 2015. Mobile information system for small-scale rural farmers. In Technological Innovation in ICT for Agriculture and Rural Development (TIAR), 2015 IEEE, pp. 79-83. IEEE.

Awate, A., Deshmankar, D., Amrutkar, G., Bagul, U., and Sonavane, S., 2015. Fruit disease detection using color, texture analysis and ANN. In Green Computing and Internet of Things (ICGCIoT), 2015 International Conference on, pp. 970-975. IEEE.

Binswanger, H. P. and Khandker, S. R., 1995. The impact of formal finance on the rural economy of India. The Journal of Development Studies, 32(2):234-262.

Coale, A. J. and Hoover, E. M., 2015. Population growth and economic development. Princeton University Press. Davenport, T. H. and Dyché, J., 2013. Big data in big companies. International Institute for Analytics, p. 3.

Dursun, M. and Ozden, S., 2011. A wireless application of drip irrigation automation supported by soil moisture sensors. Scientific Research and Essays, 6(7):1573-1582.

Feuer, L., 1995. Soil moisture sensor. US Patent 5,445,178.

Floyd, R. E., 2015. RFID in Animal-Tracking Applications. Potentials, IEEE, 34(5):32-33.

Gao, L., Zhang, M., and Chen, G., 2013. An Intelligent irrigation system based on wireless sensor network and fuzzy control. Journal of Networks, 8(5):1080-1087.

Gupta, S. and Deshpande, R., 2004. Water for India in 2050: first-order assessment of available options. Current science, 86(9):1216-1224.

Gutierrez, J., Villa-Medina, J. F., Nieto-Garibay, A., and Porta-Gándara, M. Á., 2014. Automated irrigation system using a wireless sensor network and GPRS module. Instrumentation and Measurement, IEEE Transactions on, 63(1):166-176.

Gutierrez Jaguey, J., Villa-Medina, J. F., Lopez-Guzman, A., and Porta-Gandara, M. A., 2015. Smartphone Irrigation Sensor. Sensors Journal, IEEE, 15(9):5122-5127.

Harun, A. N., Kassim, M. R. M., Mat, I., and Ramli, S. S., 2015. Precision irrigation using Wireless Sensor Network. In Smart Sensors and Application (ICSSA), 2015 International Conference on, pp. 71-75. IEEE.

Juels, A., 2006. RFID security and privacy: A research survey. IEEE journal on selected areas in communications, 24(2):381-394.

Kaloxylos, A., Eigenmann, R., Teye, F., Politopoulou, Z., Wolfert, S., Shrank, C., Dillinger, M., Lampropoulou, I., Antoniou, E., Pesonen, L. et al., 2012. Farm management systems and the Future Internet era. Computers and electronics in agriculture, 89:130-144.

Kaloxylos, A., Wolfert, J., Verwaart, T., Terol, C. M., Brewster, C., Robbemond, R., and Sundmaker, H., 2013. The use of Future Internet Technologies in the agriculture and Food sectors: Integrating the Supply Chain. Procedia Technology, 8:51-60.

Kaur, A. and Kaur, J., 2012. comparison of DCT and DWT of Image Compression Techniques. International Journal of Engineering Research and Development, 1(4):49-52.

Keller, M. S., 1999. Take command: cron: Job scheduler. Linux Journal, 1999(65es):15.

Madanayake, A., Cintra, R. J., Dimitrov, V., Bayer, F., Wahid, K. A., Kulasekera, S., Edirisuriya, A., Potluri, U., Madishetty, S., and Rajapaksha, N., 2015. Low-Power VLSI Architectures for DCT/DWT: Precision vs Approximation for HD Video, Biomedical, and Smart Antenna Applications. IEEE Circuits and Systems Magazine, 15(1):25-47. ISSN 1531-636X. doi:10.1109/MCAS.2014.2385553.

Mahlein, A.-K., Oerke, E.-C., Steiner, U., and Dehne, H.-W., 2012. Recent advances in sensing plant diseases for precision crop protection. European Journal of Plant Pathology, 133(1):197-209.

Manyika, J., Chui, M., Brown, B., Bughin, J., Dobbs, R., Roxburgh, C., and Byers, A. H., 2011. Big data: The next frontier for innovation, competition, and productivity.

McAfee, A., Brynjolfsson, E., Davenport, T. H., Patil, D., and Barton, D., 2012. Big data. The management revolution. Harvard Bus Rev, 90(10):61-67.

Meher, P. K., Valls, J., Juang, T.-B., Sridharan, K., and Maharatna, K., 2009. 50 years of CORDIC: Algorithms, architectures, and applications. IEEE Transactions on Circuits and Systems I: Regular Papers, 56(9):1893-1907.

Milind, P. K. and Bhaskar, P. Microcontroller Based Adaptive Irrigation System Using WSN for Variety Crops and Development of Insect Avoidance System for Better Yield. IJRET: International Journal of Research in Engineering and Technology eISSN, pp. 2319-1163.

Muñoz-Carpena, R. and Dukes, M. D., 2005. Automatic irrigation based on soil moisture for vegetable crops.ABE356.

Parameswaran, G. and Sivaprasath, K., 2016. Arduino Based Smart Drip Irrigation System Using Internet of Things. International Journal of Engineering Science, 5518.

Putjaika, N., Phusae, S., Chen-Im, A., Phunchongharn, P., and Akkarajitsakul, K., 2016. A control system in an intelligent farming by using arduino technology. In Student Project Conference (ICT-ISPC), 2016 Fifth ICT International, pp. 53-56. IEEE.

Roy, A. B., Dey, D., Mohanty, B., and Banerjee, D., 2012. Comparison of FFT, DCT, DWT, WHT compression techniques on electrocardiogram and photoplethysmography signals. In IJCA Special Issue on International Conference on Computing, Communication and Sensor Network CCSN, pp. 6-11.

Rumpf, T., Mahlein, A., Steiner, U., Oerke, E., and Dehne, H. Early detection and classification of plant diseases with support vector machines based on hyperspectral reflectance. Computers and Electronics in Agriculture, 74(1):9.

Rupanagudi, S. R., Ranjani, B., Nagaraj, P., Bhat, V. G., and Thippeswamy, G., 2015. A novel cloud computing based smart farming system for early detection of borer insects in tomatoes. In Communication, Information & Computing Technology (ICCICT), 2015 International Conference on, pp. 1-6. IEEE.

Sankaran, S., Mishra, A., Ehsani, R., and Davis, C., 2010. A review of advanced techniques for detecting plant diseases. Computers and Electronics in Agriculture, 72(1):1-13.

Simeon, M. I., Mohammed, A. S., and Adebayo, S. E., 2013. Development and preliminary testing of an electronic pest repeller with automatic frequency variation. Development, 2(1).

Wang, Y. and Chi, Z., 2016. System of wireless temperature and humidity monitoring based on arduino uno platform. In Instrumentation & Measurement, Computer, Communication and Control (IMCCC), 2016 Sixth International Conference on, pp. 770-773. IEEE.

Wang, Z., Fu, Z., Chen, W., and Hu, J., 2010. A RFID-based traceability system for cattle breeding in China. In Computer Application and System Modeling (ICCASM), 2010 International Conference on, volume 2, pp. V2-567. IEEE.

Xia, F., Yang, L. T., Wang, L., and Vinel, A., 2012. Internet of things. International Journal of Communication Systems, 25(9):1101.