Enhancing Agricultural Support for Small Scale Farmers in Kenya: An IoT-based Mini Weather Station as a Machine Learning Data Collector

Abstract

This paper presents a mini weather station device as a data collector for a machine learning model, aiming to support the Kenyan agricultural sector through small-scale farmers. Most farmers in Kenya practice small-scale farming and often face challenges in accessing timely and accurate weather information, which deters them from making informed decisions on what kind of crops to grow and the type of resources to allocate to their farms. We propose designing and deploying an affordable IoT mini weather station that collects real-time weather data to address this issue. The device has sensors that collect meteorological parameters such as humidity, temperature, light intensity, and atmospheric pressure. The collected data is transmitted to a cloud server and can be used as input for AI-powered machine learning models for forecasting and advisory systems to personalize recommendations to farmers, such as optimal planting time, irrigation schedules, and pest management strategies based on the prevailing weather.

Description

Keywords

Climate Change, Smallholder Farming, Weather Prediction, Internet of Things, Artificial Intelligence

Citation

Itotia, S., Muriithi, B., Gitahi, S., Korir, P., Murigi, M., Kimutai, R., ... & Sevilla, J. (2023, December). Enhancing Agricultural Support for Small Scale Farmers in Kenya: An IoT-based Mini Weather Station as a Machine Learning Data Collector. In 2023 IEEE Global Conference on Artificial Intelligence and Internet of Things (GCAIoT) (pp. 66-71). IEEE.

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