ISSN

2231-3915 (Online)
2231-3907 (Print)


Author(s): Bridgitte Owusu-Boadu

Email(s): gosunyani@gmail.com

DOI: 10.52711/2231-3915.2021.00001   

Address: Bridgitte Owusu-Boadu
Brivink consult and Technology, Ghana.
*Corresponding Author

Published In:   Volume - 11,      Issue - 1,     Year - 2021


ABSTRACT:
Farming in low and medium countries such as Ghana is seen as one of the pillars that support the economy. However, most smallholder farms within these countries face several challenges such as irregular rain pattern, access to adequate information, inadequate agricultural extension agents, bush fires destroying crops pest and diseases, and more, which affect low productive and food security. These challenges encountered by small scale farmers (SSF) in these counties make it impossible to achieve the millennium development goals (MDGs) of diminishing hunger, and food security is rooted in increasing agricultural productivity, especially from the crop farming. In a way to overcome these challenges facing SSF, this paper proposed a theoretical Framework for Smart Farming based on IoT and Machine Learning Techniques. It is anticipated that the successful implementation of the proposed framework will increase productivity in crop farming, hence help achieve the MDGs.


Cite this article:
Bridgitte Owusu-Boadu. A proposed conceptual framework based on machine learning techniques and IoT services for smart farming in developing countries. International Journal of Technology. 2021; 11(1):1-5. doi: 10.52711/2231-3915.2021.00001

Cite(Electronic):
Bridgitte Owusu-Boadu. A proposed conceptual framework based on machine learning techniques and IoT services for smart farming in developing countries. International Journal of Technology. 2021; 11(1):1-5. doi: 10.52711/2231-3915.2021.00001   Available on: https://ijtonline.com/AbstractView.aspx?PID=2021-11-1-1


REFERENCES:
1.    Danso-Abbeam, G., Ehiakpor, D. S. and Aidoo, R. Agricultural extension and its effects on farm productivity and income: insight from Northern Ghana. Agric. Food Secur. 7, 74 (2018).
2.    Cotter, M. et al. Creating the data basis to adapt agricultural decision support tools to new environments , land management and climate change — A case study of the RiceAdvice App. 423–432 (2020) doi:10.1111/jac.12421.
3.    Goap, A., Sharma, D., Shukla, A. K. and Krishna, C. R. An IoT based smart irrigation management system using Machine learning and open source technologies. Comput. Electron. Agric. 155, 41–49 (2018).
4.    Van Oort, P. A. J. and Zwart, S. J. Impacts of climate change on rice production in Africa and causes of simulated yield changes. Glob. Chang. Biol. 24, 1029–1045 (2018).
5.    Rose, D. C. et al. Decision support tools for agriculture : Towards effective design and delivery. AGSY 149, 165–174 (2016).
6.    Asiamah, D. A. and Darko, E. T. Part I: Why Ghana’s agricultural growth has slowed down. https://farmerline.co/2016/11/01/part-i-why-ghanas-agricultural-growth-has-slowed-down/#:~:text=Inadequate finances%2C climate change%2C poor,declining fortunes of the sector. (2016).
7.    Brown, M. E. et al. Climate Change, Global Food Security, and the U.S. Food System. BMC Public Health vol. 5 https://ejournal.poltektegal.ac.id/index.php/siklus/article/view/298%0Ahttp://repositorio.unan.edu.ni/2986/1/5624.pdf%0Ahttp://dx.doi.org/10.1016/j.jana.2015.10.005%0Ahttp://www.biomedcentral.com/1471-2458/12/58%0Ahttp://ovidsp.ovid.com/ovidweb.cgi?T=JSandP (2015).
8.    Bullappa, H. and Shivkumar, K. The Impact of Irrigation on Poverty with Special Reference to-Asian Countries. Asian J. Manag. 9, 1109 (2018).
9.    Balasubramanian, M., Anandh, K. S. and Kumar, T. P. Study and Assessment of Rural Roads Network in Agriculture Productivity. Int. J. Technol. 5, 125 (2015).
10.    Tawale, N. and Pote, G. R. Design and Development of Optimized Solar Powered Water Pump for Farming and Irrigation. Int. J. Technol. 4, 265–282 (2014).
11.    Amaliya, N. K. and Kumar, S. P. Status of Pond Waters for Irrigation in Kanyakumari District, Tamilnadu, India. Asian J. Res. Chem. 8, 253 (2015).
12.    Chandraju, S., Thejovathi, C. and Chidan Kumar, C. S. Impact of distillery spentwash irrigation on sprouting and growth of tagetes (Asteraceae) flowering plant. J. Chem. Pharm. Res. 3, 376–381 (2011).
13.    Singh, H., Das, A. and Singh, S. Superabsorbent Polymers-A Potential Solution for Irrigation in Agriculture. Res. J. Pharm. Technol. 12, 2566 (2019).
14.    Rajkanna, U., Karthickkumar, T., Jayaraman, L. and Mathankumar, M. Senna crop irrigation. Res. J. Pharm. Technol. 11, 2656–2658 (2018).
15.    Sharma, H. L. and Thakur, V. Feasibility of organic farming of fruits in Himachal Pradesh. Int. J. Technol. 6, 278 (2016).
16.    Mohapatra, R. K., Mohapatra, B. N. and Panda, P. P. Application and Security in Internet of Things (IOTs). Int. J. Technol. 9, 1 (2019).
17.    Nti, I. K., Adekoya, A. F. and Weyori, B. A. A systematic review of fundamental and technical analysis of stock market predictions. Artif. Intell. Rev. 53, 3007–3057 (2019).
18.    Nti, I. K., Adekoya, A. F. and Weyori, B. A. Predicting Stock Market Price Movement Using Sentiment Analysis: Evidence From Ghana. Appl. Comput. Syst. 25, 33–42 (2020).
19.    Nti, I. K., Adekoya, A. F. and Weyori, B. A. Efficient Stock-Market Prediction Using Ensemble Support Vector Machine. Open Comput. Sci. 10, 153–163 (2020).
20.    Nti, I. K., Adekoya, A. F. and Weyori, B. A. A comprehensive evaluation of ensemble learning for stock-market prediction. J. Big Data 7, 20 (2020).
21.    Nti, I. K., Adekoya, A. F. and Weyori, B. A. Random Forest Based Feature Selection of Macroeconomic Variables for Stock Market Prediction. Am. J. Appl. Sci. 16, 200–212 (2019).
22.    Adekoya, A. F. and Nti, I. K. The COVID-19 outbreak and effects on major stock market indices across the globe: A machine learning approach. Indian J. Sci. Technol. 13, 3695–3706 (2020).
23.    Akyeramfo-Sam, S., Addo Philip, A., Yeboah, D., Nartey, N. C. and Nti, I. K. A Web-Based Skin Disease Diagnosis Using Convolutional Neural Networks. Int. J. Inf. Technol. Comput. Sci. 11, 54–60 (2019).
24.    Nti, I. K. and Quarcoo, J. A. Self-motivation and Academic Performance In Computer Programming Language Using a Hybridised Machine Learning Technique. Int. J. Artif. Intell. Expert Syst. 8, 12–30 (2019).
25.    Nti, I. K., Adekoya, A. F., Opoku, M. and Nimbe, P. Synchronising Social Media into Teaching and Learning Settings at Tertiary Education. Int. J. Soc. Media Interact. Learn. Environ. 6, 1 (2020).
26.    Mohamed, A., Rizaner, A. and Hakan, A. Using data Mining to Predict Instructor Performance. Procedia - Procedia Comput. Sci. 102, 137–142 (2016).
27.    Rodríguez, F., Florez-Tapia, A. M., Fontán, L. and Galarza, A. Very short-term wind power density forecasting through artificial neural networks for microgrid control. Renew. Energy 145, 1517–1527 (2020).
28.    Zhang, J. et al. A reinforcement learning based approach for on-line adaptive parameter extraction of photovoltaic array models. Energy Convers. Manag. 214, 112875 (2020).
29.    Nti, I. K. et al. Predicting Monthly Electricity Demand Using Soft-Computing Technique. Int. Res. J. Eng. Technol. 06, 1967–1973 (2019).
30.    Nti, K. I., Eric, G. and Samuel, Y. Detection of Plant Leaf Disease Employing Image Processing and Gaussian Smoothing Approach. Int. J. Comput. Appl. 162, 20–25 (2017).

Recomonded Articles:

Author(s): Pallavi Kale, Mohini T. Bherwani

DOI: 10.5958/2231-3915.2017.00010.4         Access: Open Access Read More

Author(s): Avinash Dhole, Mohan Awasthy, Sanjay Kumar

DOI:         Access: Open Access Read More

Author(s): Shishir Shrivastava, Ajay Kumar Akasapu, Lokesh Sharma

DOI:         Access: Open Access Read More

Author(s): Rashmita Kumari Mohapatra, Badri Narayan Mohapatra, Prangya Prava Panda

DOI: 10.5958/2231-3915.2019.00001.4         Access: Open Access Read More

Author(s): Ritesh Kumar Dewangan, Manoj Kumar Sahoo, Manas Patnaik

DOI:         Access: Open Access Read More

Author(s): Rajeshri Lanjewar, Tripti Sharma

DOI:         Access: Open Access Read More

Author(s): Amit Kumar Jain, Aashish Khaira, Amit Suhane

DOI:         Access: Open Access Read More

Author(s): Rajdeep Chowdhury, Saikat Ghosh

DOI:         Access: Open Access Read More

Author(s): Snusha R. Dharmik, Radharaman Shaha

DOI:         Access: Open Access Read More

Author(s): Anuj Rai,Piyush Singh

DOI: 10.5958/2231-3915.2016.00004.3         Access: Open Access Read More

Author(s): Srinivas Angadi, M Padmavathi, Raja Sekhar Mamillapalli

DOI: 10.5958/2231-3915.2015.00035.8         Access: Open Access Read More

Author(s): Bhavana Narain, Ankit Kumar

DOI: 10.5958/2231-3915.2020.00012.7         Access: Open Access Read More

Author(s): Nutan Singh, Mukesh Kashyap, Sanjay Kumar, V. K. Patle

DOI: 10.5958/2231-3915.2020.00015.2         Access: Open Access Read More

Author(s): Siddharth Nayak, Shivani Verma, Deepak Kumar Deshmukh

DOI: 10.5958/2231-3915.2020.00025.5         Access: Open Access Read More

Author(s): Manisha Dewangan

DOI: 10.5958/2231-3915.2020.00008.5         Access: Open Access Read More

International Journal of Technology (IJT) is an international, peer-reviewed journal, research journal aiming at promoting and publishing original high quality research in all disciplines of engineering sciences and technology...... Read more >>>

RNI: Not Available                     
DOI: 10.5958/2231-3915 


Recent Articles




Tags