Abstract:
Because of the rising urbanization and strong demand for farm products, the agriculture sector has expanded rapidly, particularly in Africa. Agriculture sector in Kenya is one of the main economic activities that provide employment roughly to two-thirds of the working population, contributing 33 percent of the country's GDP on average. Potato farming in Kenya comes as a second food crop after maize, and a source of income that helps to improve many livelihoods. However, smallholder potato farmers continue to face environmental challenges, which threaten the country's food security. In Kenya, most smallholder potato farmers use traditional methods (visual observation) to monitor the environmental parameters of potato crop, due to the high cost of monitoring tools available on the market. In addition, the available tools in the market such as rain gauge are limited to a specific use. Therefore, this study proposes the use of smart potato system to monitor environmental parameters which employ the use of IoT and ML technologies to address environmental challenges that smallholder potato farmers face which has huge negative effect on potato crop; high or low temperatures interferes with the growth of potato tubers and damage potato leaves, high humidity causes late blight, slow tuber development and high moisture leads to tuber rot, high or low soil pH results to interference of the nutrients absorption. This study developed a prototype system to monitor the environmental parameters. We deployed the prototype system in the potato field to monitor environmental parameters, and the data collection process was completed successfully. The study designed a web application for potato farmers to store environmental data collected from the field and a prediction system that utilizes machine learning (ML) algorithms to recommend a course of action when the environmental parameters under observation drop or rise above the ecological recommended threshold. This system will help farmers in better decision-making, improve efficiency, minimize resource wastage and lower production costs.
