A Process Design for Monitoring and Predicting Smart Agriculture with High Accuracy in the Context of Thai Agriculture
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Abstract
The purposes of this research were 1) to study and analyze a process for monitoring and predicting smart agriculture with high accuracy and 2) to design a process for monitoring and predicting smart agriculture. The research process consists of 3 steps: 1) Study and data collection, 2) Data analysis, and 3) Research summary. In this research, data was collected from a total of 132 samples of smart agriculture users. They were classified as follows: 88 samples (66.67%) from individual farmers, 15 samples (11.36%) from educational institutions, 13 samples (9.85%) from collaborations between educational institutions and farmers, 11 samples (8.33%) from private companies, and 5 samples (3.97%) from other government agencies. Then, a framework was created for the process of smart agriculture assessment and prediction, which demonstrated high accuracy in the context of Thailand.
The framework consists of three steps: 1) Step input, 2) Step process, and 3) Step output. Then, the framework was used to create guidelines for the implementation of a process for monitoring and predicting smart agriculture in the context of Thailand. This framework represents the components of smart agriculture that integrate agricultural knowledge and technology. It consists of seven components: 1) Environmental data collection and plant growth factors using IoT technology, 2) Cloud computing, 3) Monitoring and Control system, 4) Farm management platform, 5) Model Development, 6) Data analysis system, and 7) Planning system. The results of applying a process for monitoring and predicting smart agriculture in the context of Thailand to create a smart agriculture model using plant growth factor data and machine learning technology with linear regression data analysis technique showed that the model achieved a performance evaluation accuracy of 90%. This indicates a very high level of model accuracy.
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C. Giua, V. C. Materia, and L. Camanzi. “Smart farming technologies adoption: Which factors play a role in the digital transition,” Technology in Society, Vol. 68, 101869, 2022.
M. K. Sott, L. B. Furstenau, L. M. Kipper, F. D. Giraldo, J. R. Lopez-Robles, M. J. Cobo, and M. A. Imran. “Precision techniques and agriculture 4.0 technologies to promote sustainability in the coffee sector: state of the art, challenges and future trends,” IEEE Access, Vol. 8, pp. 149854-149867, 2020.
R. Dagar, S. Som, and S. K. Khatri. “Smart farming–IoT in agriculture,” In 2018 International Conference on Inventive Research in Computing Applications (ICIRCA), IEEE, pp. 1052-1056, July 2018.
T. Panudech, W. Atthaphon, and P. Anupong. “A Cloud-Based AIoT Applicationin Smart Building,” RMUTL Engineering Journal, Vol. 7, No. 1, pp. 52-61, 2022.
A. A. Laghari, K. Wu, R. A. Laghari, M. Ali, and A. A. Khan. “A review and state of art of Internet of Things (IoT),” Archives of Computational Methods in Engineering, pp. 1-19, 2012.
M. Lombardi, F. Pascale, and D. Santaniello. “Internet of things: A general overview between architectures protocols and applications,” Information, Vol. 12, No. 2, 2021.
W. Attavanich, S. Chantarat, J. Chenphuengpawn, P. Mahasuweerachai, and K. Thampanishvong. “Farms farmers and farming: a perspective through data and behavioral insights”, Puey Ungphakorn Institute for Economic Research, No. 122, 2019.
K. Pongsakorn, K. Somkit, P. Chalermchai, and T. Suriyajaras. “An Analysis of Young Farmer’s Development Policies into Action for Food Security Upper Northern Region of Thailand,” Journal of Agricultural Production (JAP), Vol. 4. No. 3, pp. 75-92, 2022.
T. Pothong, P. Mekarun, and S. Choosumrong. “Development of Smart Farming Service System for Smart Farmer using FOSS4G and IoT,” Naresuan Agriculture Journal, Vol. 16, No. 2, pp. 10-17, 2019.
J. Doshi, T. Patel, and S. Kumar Bharti. “Smart Farming using IoT a solution for optimally monitoring farming conditions,” Procedia Computer Science, Vol. 160, pp. 746-751, 2019.
D. Pivoto, P. D. Waquil, E. Talamini, C. P. S. Finocchio, V. F. Dalla Corte, and G. de Vargas Mores. “Scientific development of smart farming technologies and their application in Brazil,” Information processing in agriculture, Vol. 5, No. 1, pp. 21-32, 2018.
A. L. Virk, M. A. Noor, S. Fiaz, S. Hussain, H. A. Hussain, M. Rehman, and W. Ma. “Smart farming: an overview,” Smart Village Technology, pp. 191-201, 2020.
A. T. Balafoutis, B. Beck, S. Fountas, Z. Tsiropoulos, J. Vangeyte, T. van der Wal, and S. M. Pedersen. “Smart farming technologies description taxonomy and economic impact,” In Precision agriculture: technology and economic perspectives, pp. 21-77, 2017.
R. Dagar, S. Som, and S. K. Khatri. “Smart farming–IoT in agriculture,” In 2018 International Conference on Inventive Research in Computing Applications (ICIRCA), IEEE, pp. 1052-1056, July 2018.
M.R.M. Kassim. “IoT Applications in Smart Agriculture: Issues and Challenges,” In 2020 IEEE Conference on Open Systems (ICOS), IEEE, pp. 19-24, November 2020.
IAT. Hashem, I. Yaqoob, NB. Anuar, S. Mokhtar, and A. Gani. “The rise of big data on cloud computing: Review and open research issues,” Information systems, Vol. 47, pp. 98-115, 2015.
G. S. Patel, A. Rai, N. N. Das, and R. P. Singh, (Eds). Smart Agriculture: Emerging Pedagogies of Deep Learning Machine Learning and Internet of Things, CRC Press. 2021.
M.R.M. Kassim. “IoT Applications in Smart Agriculture: Issues and Challenges,” In 2020 IEEE Conference on Open Systems (ICOS), IEEE, pp. 19-24, November 2020.
M. Amiri-Zarandi, R. A. Dara, E. Duncan, and E. D. Fraser. Big data privacy in smart farming: a review. Sustainability, Vol. 14, No. 15, 9120, 2022.
G. Suresh, A.S. Kumar, S. Lekashri, and R. Manikandan. “Efficient crop yield recommendation system using machine learning for digital farming,” International Journal of Modern Agriculture, Vol. 10, No. 1, pp. 906-914, 2021.
K. Intichit, N. Houngtim, and A. Phimparat. “Development of Decision-making System for Plant Agricultural Crops in the North Eastern Region: A Case Study of Sisaket Province,” Journal of Applied Information Technology, Vol. 7, No. 1, pp. 7-17, 2021.
C. Rodmorn, M. Panmuang, and W. Jonglakha. “Application with the Wireless Sensor Network in Smart Farms,” Rajamangala University of Technology Srivijaya Research Journal, Vol. 13, No. 2, pp. 315-329, 2021.
I. Markoulidakis, G. Kopsiaftis, I. Rallis, and I. Georgoulas. “Multi-Class Confusion Matrix Reduction method and its application on Net Promoter Score classification problem,” In The 14th pervasive technologies related to assistive environments conference, pp. 412-419, 2021.