Diseño e implementación de un sistema IOT para la detección de afectaciones tipo Damper y Mildiu en cultivos de brócoli mediante el procesamiento de imágenes.

Abstract

The plagues affected broccoli crops in Colta, a key area for the cultivation of this vegetable. Their detrimental impact on growth resulted in small fruits and dead stems, weakening the production. Therefore, this study focused on predicting and detecting DAMPER and MILDEW pests in crops, emphasizing broccoli cultivation. The purpose was to employ advanced technologies and software tools to mitigate economic decline in agriculture. The Node-RED platform played a crucial role in this project, enabling comprehensive management of the IoT system. This tool facilitated real-time visualization of agricultural data and the execution of commands to anticipate and detect pests. Additionally, Python was used to handle images, enhancing early detection of pests in their initial phase. Real-time data acquisition was carried out through IoT sensors measuring key parameters such as humidity, radiation, and temperature. Node-RED acted as the control and monitoring center, making informed decisions to prevent crop damage. The process involved several stages: real-time data collection, use of Node-RED for control, and capturing images when pests were suspected. The images were stored on a cloud server, where image processing software identified the presence of DAMPER and MILDEW pests. The findings of this study emphasize the importance of adopting IoT-based technological solutions and image processing to predict and detect pests in broccoli. Integrating sensors, software, and real-time analysis is recommended, which is essential for taking preventive measures and effectively safeguarding agricultural production.