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http://dx.doi.org/10.9708/jksci.2022.27.09.049

Remote Multi-control Smart Farm with Deep Learning Growth Diagnosis Function  

Kim, Mi-jin (Dept. of Telecommunication Eng. Jeju National University)
Kim, Ji-ho (Dept. of Telecommunication Eng. Jeju National University)
Lee, Dong-hyeon (Dept. of Telecommunication Eng. Jeju National University)
Han, Jung-hoon (Dept. of Telecommunication Eng. Jeju National University)
Publication Information
Journal of the Korea Society of Computer and Information / v.27, no.9, 2022 , pp. 49-57 More about this Journal
Abstract
Currently, the problem of food shortage is emerging in our society due to climate problems and an increase population in the world. As a solution to this problem, we propose a multi-remote control smart farm that combines artificial intelligence (AI) and information and communication technology (ICT) technologies. The proposed smart farm integrates ICT technology to remotely control and manage crops without restrictions in space and time, and to multi-control the growing environment of crops. In addition, using Arduino and deep-learning technology, a smart farm capable of multiple control through a smart-phone application (APP) was proposed, and Ai technology with various data securing and diagnosis functions while observing crop growth in real-time was included. Various sensors in the smart farm are controlled by using the Arduino, and the data values of the sensors are stored in the built database, so that the user can check the stored data with the APP. For multiple control for multiple crops, each LED, COOLING FAN, and WATER PUMP for two or more growing environments were applied so that the user could control it conveniently. And by implementing an APP that diagnoses the growth stage through the Tensor-Flow framework using deep-learning technology, we developed an application that helps users to easily diagnose the growth status of the current crop.
Keywords
Smart farm; Arduino; sensor; deep learning; database; multi-control;
Citations & Related Records
Times Cited By KSCI : 3  (Citation Analysis)
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1 Dae-yong Hong, Woon-hak Kang, Sang-won Lee, "Implementation of PHP extension for Altibase database server," Proceedings of the Korea Information Processing Society Conference, vol. 19, No. 2, pp. 1392-1395, Nov. 2012. DOI: https://kiss.kstudy.com/thesis/thesis-view.asp?key=3873994
2 Sung-Soo Park, Ji-Won Baek, Sun-Moon Jo, Kyungyong Chung. "Motion Monitoring using Mask R-CNN for Articulation Disease Management," Journal of the Korea Convergence Society, vol. 10, no. 3, pp. 2, 2019. DOI: https://doi.org/10.15207/JKCS.2019.10.3.001   DOI
3 S. Albawi, T. A. Mohammed and S. Al-Zawi, "Understandung of a convolutional neural network," 2017 International Conference on Engineering and Technology (ICET), pp. 2-3, 2017 DOI: https://doi.org/10.1109/ICEngTechnol.2017.8308186   DOI
4 Yong-hyun Park, "Resolving the food shortage through the popularization of smart farms," Joong-bu Daily Newspaper, DOI: http://www.jbnews.com/news/articleView.html?idxno=1362542
5 Jae-kyung Lee, Byung-moon Seol "Intelligent Smart Farm A Study on Productivity: Focused on Tomato farm Households" Asia-Pacific Journal of Business Venturing and Entrepreneurship Vol.14 No.3 pp.185-199. DOI: 10.16972/apjbve.14.3.201906.185   DOI
6 Yeon-joong Kim, Ji-yeon Park, and Young-goo Park, "An Analysis of the Current Status and Success Factors of Smart Farms," Korea Rural Economic Research Institute - Other Research Report, pp. 1-74, 2016. DOI: https://doi.org/10.23000/TRKO201700008983   DOI
7 Munir, M. Safdar, Imran Sarwar Bajwa, and Sehrish Munawar Cheema. "An intelligent and secure smart watering system using fuzzy logic and blockchain," Computers & Electrical Engineering, vol. 77, pp. 109-119, 2019. DOI: https://doi.org/10.1016/j.compeleceng.2019.05.006   DOI
8 Kitae Kim, Bomi Lee, and Jongwoo Kim. "Feasibility of Deep Learning Algorithms for Binary Classification Problems," Journal of Intelligence and Information Systems, vol. 23, no. 1, pp. 96-98, 2017. DOI: https://doi.org/10.13088/jiis.2017.23.1.095   DOI
9 Kwon, O. H., Kang, I. C., Min, D. S., Im, H. B., & Park, Y. W. (2021). A Study on the Smart Farm Characteristics Using Multiple Sensors. The Journal of the Korea institute of electronic communication sciences, 16(4), 719-724. DOI: http://dx.doi.org/10.13067/JKIECS.2021.16.4.719   DOI
10 JashDoshi, Tirthkumar Patel, Santosh kumar Bharti, "Smart Farming using IoT, a solution for optimally monitoring farming conditions," Procedia Computer Science, Vol. 160 pp. 746-751, 2019 DOI: https://doi.org/10.1016/j.procs.2019.11.016   DOI
11 Akshatha, Y., and A. S. Poornima. "IoT Enabled Smart Farming: A Review," 2022 6th International Conference on Intelligent Computing and Control Systems (ICICCS), IEEE, 2022. DOI: 10.1109/ICICCS53718.2022.9788149   DOI
12 Yogendra Singh Parihar, "Internet of things and nodemcu," Journal of Emerging Technologies and Innovative Research, vol. 6, pp. 1085-1088, June. 2019 DOI: https://doi.org/10.13140/RG.2.2.34456.75525   DOI
13 Muangprathub, Jirapond, et al. "IoT and agriculture data analysis for smart farm," Computers and electronics in agriculture, vol. 156, pp. 467-474, 2019. DOI: https://doi.org/10.1016/j.compag.2018.12.011   DOI
14 Ignacio A. Quiroz, Germen H. Alferez "Image recognition of Legacy blueberries in a Chilean smart farm through deep learning" Computer and Electronics in Agriculture 168(2020) 105044, pp. 1-2, 2020 DOI: https://doi.org/10.1016/j.compag.2019.105044   DOI