Automatic Leaf Health Monitoring with an IoT Camera System based on Computer Vision and Segmentation for Disease Detection

Abstract

Manual identification of diseases in crops is costly and subjective, driving the need for automated systems for accurate detection in the field. This requires the use of technologies based on the integration of IoT and deep learning models to improve the assessment capacity of crop health and leaf disease, with continuous monitoring. The literature review highlights technological solutions that include weed and disease detection using artificial intelligence and autonomous systems, as well as semantic segmentation algorithms to locate diseases in field images whose processes can be improved with systems based on microcontrollers and sensors. This research implements a leaf health monitoring system using IoT and AI technologies, with the development of an IoT device with a camera, the configuration of an MQTT broker in NODE-Red, and the implementation of a script in Python for leaf instance segmentation and image display. As a result, it is highlighted that image analysis, with the Python tool, allowed obtaining valuable information for precision agriculture, while the visualization or messaging interface allows health monitoring and management of crops. In conclusion, the System adequately performs image capture, processing, and transmission, being a contributes to precision agriculture solutions, considering that this can be improved with the integration of more complex deep learning algorithms to increase precision.