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
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://www.ijtonline.com/AbstractView.aspx?PID=2021-11-1-1
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