SMART FARMING IN AGRICULTURAL INDUSTRY: MOBILE TECHNOLOGY PERSPECTIVE
The aim of this research is to examine key indicators that are necessary for the implementation and development of smart farming concepts in the agricultural industry, especially from the applied mobile technology point of view. Accordingly, the authors used a neural network based software solution to determine the correlation, relationship structure and partial contribution of indicators for the mobile technology development in agricultural industries in selected countries. The validity of the input-output model in a neural network based software solution was evaluated using the Minkowski error and Quasi-Newton method through several iterations/epochs. The neural network structure has shown the importance of particular indicators for adopting a mobile technology perspective in the agricultural industry, where the application of Information and Communications Technologies (ICT) in agriculture is most emphasized. Only those countries that invest the most in the ICT in the agricultural sector can achieve greater efficiency and productivity by applying smart farming.
2. Balducci, F., Impedovo, D., & Pirlo, G. (2018). Machine Learning Applications on Agricultural Datasets for Smart Farm Enhancement. Machines, 6(3), 1-22. doi: https://doi.org/10.3390/machines6030038
3. Bannerjee, G., Sarkar, U., Das, S., & Ghosh, I. (2018). Artificial Intelligence in Agriculture: A Literature Survey. International Journal of Scientific Research in Computer Science Applications and Management Studies, 7(3), 1-6.
4. Charania, I., & Li, X. (2019). Smart Farming: Agriculture’s Shift from a Labor Intensive to Technology Native Industry. Internet of Things, 1-15. doi: https://doi.org/10.1016/j.iot.2019.100142
5. Christiansen, N.H., Voi, P.E.T., Winther, O., & Høgsberg, J. (2014). Comparison of Neural Network Error Measures for Simulation of Slender Marine Structures. Journal of Applied Mathematics, 1-11. doi: https://doi.org/10.1155/2014/759834
6. Daum, T., Buchwald, H., Gerlicher, A., & Birner, R. (2018). Smartphone apps as a new method to collect data on smallholder farming systems in the digital age: A case study from Zambia. Computers and electronics in agriculture, 153, 144-150. doi: https://doi.org/10.1016/j.compag.2018.08.017
7. Dursun, M., & Ozden, S. (2011). A wireless application of drip irrigation automation supported by soil moisture sensors. Scientifc Research and Essays, 6(7), 1573- 1582. doi: https://doi.org/10.5897/SRE10.949
8. FAO (2018). Status of Implementation of e-Agriculture in Central and Eastern Europe and Central Asia – Insights from selected countries. Retrieved from http://www.fao.org/3/I8303EN/i8303en.pdf (February 18, 2020)
9. Ferentinos, K.P. (2018). Deep learning models for plant disease detection and diagnosis. Computers and Electronics in Agriculture, 145, 311-318. doi: https://doi.org/10.1016/j.compag.2018.01.009
10. Gondchawar, N., & Kawitkar, R.S. (2016). IoT based smart agriculture. International Journal of Advanced Research in Computer and Communication Engineering, 5(6), 838–842. doi: https://doi.org/10.17148/IJARCCE.2016.56188
11. IoT Analytics 2016 Global Overview (2016). Retrieved from https://iot-analytics.com/global-overview-640-enterprise-iot-use-cases/ (February 17, 2020)
12. Jha, K., Doshi, A., Patel, P., & Shah, M. (2019). A comprehensive review on automation in agriculture using artificial intelligence. Artificial Intelligence in Agriculture, 2, 1-12. doi: https://doi.org/10.1016/j.aiia.2019.05.004
13. Jurjević, Ž., Bogićević, I., Đokić, D., & Matkovski, B. (2019). Information Technology as a Factor of Sustainable Development of Serbian Agriculture. Strategic management, 24(1), 41-46. doi: https://doi.org/10.5937/StraMan1901041J
14. Liakos, K.G., Busato, P., Moshou, D., Pearson, S., & Bochtis, D. (2018). Machine Learning in Agriculture: A Review. Sensors, 18(8), 1-29. doi: https://doi.org/10.3390/s18082674
15. Mahant, M., Shukla, A., Dixit, S., & Patel, D. (2012). Use of ICT in Agriculture. International Journal of Advanced Computer Research, 2, 2277-7970.
16. Mahbub, M. (2020). A Smart Farming Concept Based on Smart Embedded Electronics, Internet of Things and Wireless Sensor Network. Internet of Things, 9, 1-30. doi: https://doi.org/10.1016/j.iot.2020.100161
17. Milovanović, S. (2014). The role and potential of information technology in agricultural improvement. Economics of Agriculture, 61(2), 471-485. doi: https://doi.org/10.5937/ekoPolj1402471M
18. Mohanraj, I., Ashokumar, K., & Naren, J. (2016). Field monitoring and automation using IOT in agriculture domain. Procedia Computer Science, 93, 931-939. doi: https://doi.org/10.1016/j.procs.2016.07.275
19. Mykulskyi, V. (2019). Branch Features of the Automated Accounting Organization at Agricultural Enterprise. Accounting and Finance, 4, 37-44.
20. Nema, M.K., Khare, D., & Chandniha, S.K. (2017). Application of artificial intelligence to estimate the reference evapotranspiration in sub-humid Doon valley. Applied Water Science, 7(7), 3903-3910. doi: https://doi.org/10.1007/s13201-017-0543-3
21. Neural Designer, Retrieved from https://www.neuraldesigner.com/learning/tutorials/training-strategy (February 15, 2020)
22. Pawar, S.B., Rajput, P., & Shaikh, A. (2018). Smart irrigation system using IOT and raspberry pi. International Research Journal of Engineering and Technology, 5(8), 1163-1166.
23. Poppe, K.J., Wolfert, S.,Verdouw, C., & Verwaart, T. (2013). Information and communication technology as a driver for change in agri-food chains. EuroChoices, 12(1), 60-65. doi: https://doi.org/10.1111/1746-692X.12022
24. Ray, P.P. (2017). Internet of things for smart agriculture: Technologies, practices and future direction. Journal of Ambient Intelligence and Smart Environments, 9(4), 395-420. doi: https://doi.org/10.3233/AIS-170440
25. Savitha, M., & UmaMaheshwari, O.P. (2018). Smart crop field irrigation in IOT architecture using sensors. International Journal of Advanced Research in Computer Science, 9(1), 302-306.
26. Shekhar, Y., Dagur, E., Mishra, S., & Sankaranarayanan, S. (2017). Intelligent IoT based automated irrigation system. International Journal of Applied Engineering Research, 12(18), 7306-7320.
27. Song, H. (2018). Nature-Inspired VLSI Circuits - From Concept to Implementation. Lulu.com, USA.
28. Suakanto, S., Engel, V.J., Hutagalung, M., & Angela, D. (2016). Sensor networks data acquisition and task management for decision support of smart farming. In 2016 International Conference on Information Technology Systems and Innovation (ICITSI), IEEE, 1-5. doi: https://doi.org/10.1109/ICITSI.2016.7858233
29. Wasilewski, A., & Wasilewska, A. (2019). Financing the research and development activity for the agri-food sector and rural areas. Western Balkan Journal of Agricultural Economics and Rural Development, 1(1), 29-39.
30. Wolfert, S., Ge, L., Verdouw, C., & Bogaardt, M.J. (2017). Big Data in Smart Farming – a review. Agricultural Systems, 153, 69-80. doi: https://doi.org/10.1016/j.agsy.2017.01.023
31. Zhai, Z., Martinez, J.F., Beltran, V., & Martinez, N.L. (2020). Decision support systems for agriculture 4.0: Survey and challenges. Computers and Electronics in Agriculture, 170. doi: https://doi.org/10.1016/j.compag.2020.105256