Soil data collection using wireless sensor networks and offsite visualization: case study of the innovative solutions for digital agriculture project in Kenya
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A Dissertation Submitted in Partial Fulfillment of the Requirements for the Degree of Masters of Science in Embedded and Mobile Systems of the Nelson Mandela African Institution of Science and Technology
The applications of Wireless Sensor Networks (WSN) and Internet of Everything (IoE) has changed how we obtain and consume information. Traditional farming has come a long way in accepting scientific methods to improve production. Smart Agriculture is one of the ways technology has greatly contributed to maximized crop production. Centrally placed labs and mobile soil labs have played a key role in this improved way of farming. Soil samples are collected from farms and analyzed to provide data to farmers, extension workers, and policymakers. This process takes time and is costly to implement. In addition, the definition of trends is difficult as replication of sampling requires more funding. This study proposes to connect end devices in an IoE system bringing in real-time data and at low-cost and also providing local data at local stations. The system is built incrementally to have a minimum viable product (MVP) using a combination of Agile and Waterfall methods of development. The system presents a pilot remote sensor module in a WSN using a Raspberry Pi minicomputer as an end node and three sensors collecting information on soil humidity and temperature, air humidity and temperature and soil pH values in real-time. A cloud-based data analysis and visualization are used. The system supports the ongoing work by soil labs by collecting information that is close to real-time. The study brings on board real-time data relaying of farm parameters that make it easier for small scale farm owners, extension officers, soil labs and other stakeholders to make instant informed decisions.
The applications of Wireless Sensor Networks (WSN) and Internet of Everything (IoE) has changed how we obtain and consume information. Traditional farming has come a long way in accepting scientific methods to improve production. Smart Agriculture is one of the ways technology has greatly contributed to maximized crop production. Centrally placed labs and mobile soil labs have played a key role in this improved way of farming. Soil samples are collected from farms and analyzed to provide data to farmers, extension workers, and policymakers. This process takes time and is costly to implement. In addition, the definition of trends is difficult as replication of sampling requires more funding. This study proposes to connect end devices in an IoE system bringing in real-time data and at low-cost and also providing local data at local stations. The system is built incrementally to have a minimum viable product (MVP) using a combination of Agile and Waterfall methods of development. The system presents a pilot remote sensor module in a WSN using a Raspberry Pi minicomputer as an end node and three sensors collecting information on soil humidity and temperature, air humidity and temperature and soil pH values in real-time. A cloud-based data analysis and visualization are used. The system supports the ongoing work by soil labs by collecting information that is close to real-time. The study brings on board real-time data relaying of farm parameters that make it easier for small scale farm owners, extension officers, soil labs and other stakeholders to make instant informed decisions.
Keywords
Internet-of-Everything, Real-Time-Data, Wireless-Sensor-Networks, Smart-Agriculture, Sensor-Systems, Cloud-Computing