The processing of coffee beans after harvest relies heavily on drying, which significantly affects the final product quality. Despite its importance, smallholder farmers in developing countries often resort to using patios and sun exposure for drying, exposing the process to numerous uncontrollable variables that may compromise the quality of coffee. Furthermore, coffee farmers typically employ subjective methods to determine moisture content, which can lead to inaccurate measurement results. This study proposes an Internet-of-Things (IoT)-based technology for monitoring and analyzing the coffee drying process. By utilizing a portable set of sensors and data analytics, this technology collects real-time data on various parameters of the drying process, including grain moisture and air temperature and humidity. Once registered in the system, users can access and analyze the data using their cell phones. The study employed a three-phase methodology: first, designing and developing an IoT framework to capture and analyze data related to the coffee drying process; second, implementing the IoT system, including software development for data transmission, processing, and visualization; and third, conducting a case study with a female smallholder coffee farmer association in G & eacute;nova, Quindio, Colombia, to deploy the technology, evaluate its usability, and analyze its impact on the drying process. The results of the case study show that the proposed technology allows users to visualize trends and patterns in the drying process based on previously entered information. This offers valuable insights into the drying process, enabling farmers to make informed decisions and take appropriate action to improve the quality of their coffee beans.
