Autonomous Real-Time Monitoring of Crops in Controlled Environment Agriculture

In this work, we develop a system that can be used for real-time monitoring of multiple important areas in controlled environment agriculture (and in particular greenhouses) using an autonomous ground vehicle (AGV). To model the greenhouse layout, as well as the tasks that should be accomplished by the AGV, we generate two weighted directed graphs. Based on those graphs, an algorithm is then proposed for finding the optimal (in the sense of traveled distance) trajectory of the vehicle with the goal of precisely monitoring important areas in the greenhouse. Furthermore, a data collection system and image processing algorithm is proposed and implemented so that the vehicle: (i) can capture images and detect changes that have occurred on the crops in real time, and (ii) construct (if needed) a map of the plant rows, when arriving at each one of the important areas. Based on this work, the images can either be stitched onboard the vehicle and then sent to a server or be sent directly to the server and then processed (stitched) there. Both simulation and experimental results are provided to demonstrate the effectiveness and performance of the proposed system.