Aerial remote sensing system to control pathogens and diseases in broccoli crops with the use of artificial vision

Broccoli is one of Ecuador's main agricultural products and is exported worldwide. To ensure high-quality production, routine inspections are necessary to counteract pathogens and diseases. This study presents an aerial remote sensing system to monitor broccoli crops using pre-programmed flight plans to assess crop health and enable timely treatments. The system leverages the YOLO v5x algorithm for deep learning under various production conditions. An autonomous drone, equipped with GPS for grid flight planning, captures high-definition images every 2 seconds. These images, tagged with geolocation data, are processed through a Python-based graphical interface. The results are stored in a database to improve the system's accuracy in detecting false positives and negatives. The aerial remote sensing system successfully monitored broccoli crops, identifying areas affected by pathogens and diseases. The YOLO v5x algorithm demonstrated high accuracy in image analysis, reducing false detections. The system's autonomous drone efficiently covered large crop areas, providing precise geolocation data for targeted interventions. The collected data, stored in a centralized database, facilitated continuous improvement of the detection algorithm, ensuring reliable pathogen control and maintaining high production quality.