Smart Multi-Crop Care System Design using Bipartite Matching

Abstract

This paper presents a solution to optimize water consumption for a multi-crop care system with the help of artificial intelligence and optimization graph algorithms. The proposed system takes advantage of object detection models such as YOLOv5 to identify crop types and their requirements while using moisture sensors to provide real-time data about soil moisture conditions. A key contribution is the implementation of a weighted bipartite graph to optimize watering patterns in order to ensure required soil conditions and reduce water resource usage according to the crop type. The system represents a path forward in precision agriculture by offering a scalable and adaptable approach to smart irrigation, considering different factors of a crop’s environment (like moisture level in soil, distance from the sprinkler, etc.) and their associated weights to control water levels in the crop care system for different types of crops.