Monitoreo de variables agroambientales en un cultivo de café aplicando tecnología IoT y técnicas de análisis descriptivas y diagnosticas para optimizar el proceso de toma de decisiones

This study implements 4.0 technologies, specifically the Internet of Things (IoT), alongside descriptive and diagnostic analysistechniques to monitor agro-environmental variables affecting coffee cultivation at Las Acacias farm, Salento, Quindío. Theobjective was to optimize the management of grain production processes through an intelligent system for data collection andanalysis. A star-topology sensor network was designed, consisting of two nodes located at a maximum distance of 120 meters fromthe Gateway. The selected sensors DHT22 (temperature and humidity), FC-28 (soil moisture), and EC-5 (electrical conductivity)were evaluated based on accuracy, reliability, ease of configuration, and cost. Data transmission was carried out using theLoRaWAN protocol and managed via the IoT platforms The Things Network (TTN) and TAGOIO, with measurement intervals setevery 10 minutes.The results indicated that high maximum temperatures (38.1°C in September) and low relative humidity (21.7% in August)triggered thermal and water stress conditions in coffee plants, affecting key physiological processes. A decline in soil water contentand electrical conductivity revealed a water deficit and potential nutrient leaching, highlighting the need to adjust fertilizationpractices through irrigation systems and agronomic management strategies. Finally, this study demonstrates that the integration ofIoT sensors with data analysis techniques enables timely and well-founded decision-making, fostering the adoption of precisionagriculture, crop sustainability, and improved coffee productivity.