Plant Health Monitoring Using Bubble Identification Technique

Recent years, advancements in technology such as Internet of Things (IoT), Unmanned Aerial Vehicles (UAVs), and Deep learning in agriculture has revolutionized conventional farming methods into precision faming. For real-time plant health monitoring, smart irrigation systems are preferred over conventional techniques. However, the subjectivity, low sensitivity, intrusive approaches, resource-intensiveness, scaling problems, and spatial/temporal limits of the current plant health monitoring technologies make them less successful at identifying and treating vascular problems such embolisms. As a result, more sensitive, objective, non-invasive, and scalable methods are required. In order to improve plant health monitoring, this research suggests a smart irrigation system that uses the bubble identification technique to analyze vascular health. The proposed method automatically detects and analyzes bubbles in images by using geometric modelling and image processing techniques. The study's findings give farmers and agricultural researchers important new information about plant health monitoring, allowing them to make well-informed decisions about fertilization, irrigation, and crop management in general.