Abstract:
Agriculture contributes to the economies of developing countries. Tea is the most popular crop in Kenya as it contributes majorly to her economy. Among the various stages of processing tea, fermentation is the most important as it determines the final quality of the processed tea. Presently, the process of monitoring is done manually by tea tasters by tasting, smelling, and touching tea which compromises the quality of tea. In this paper, a deep learner dubbed "TeaNet" is deployed in Edge and Fog environments for real-time monitoring of tea fermentation. The system is powered by a Photovoltaic (PV) energy source to overcome the challenge of unreliable power supply from the grid. Further, the energy consumption of the solution is reduced by applying duty cycling where idle components are designed to sleep. We used One-way ANOVA and Post-hoc for data analysis. From the results, Edge registered the lowest latency compared to the Cloud and Fog environments. During deployment of the energy-optimized model, 50.6559Wh energy was saved. This study recommends that the task offloading model proposed in this study be explored in offloading tasks in other fields.