Intelligent Control System to Irrigate Orchids Based on Visual Recognition and 3D Positioning

Abstract

This work develops a novel automatic irrigation system to implement the customized and accurate watering for an individual seedling. The system integrates the modules of visual recognition of the stem-leaf junction, identification of the stem-root junction as the watering point, and control of the spraying nozzle. The model of YOLOv3 is employed to screen the stem-leave junction of an orchid seedling, whose depth map then acquired by the method of Semi-Global Block Matching (SGBM) extracts the three-dimensional (3D) coordinates of the junction center. Next, the concept of leaf vector is introduced to identify the stem-root junction of the orchid seedling as the accurate watering point, which the spraying nozzle is controlled to reach for supplement of the specific amount of water. A number of experiments were conducted to verify the proposed irrigation system for orchid seedlings at different locations with various heights. The experimental results show that the rates of successful watering are 82% and 83.3% for the uni-pot and multi-pot orchid seedlings, respectively.

Keywords

• automatic irrigation system
• orchid seedling
• object detection
• stereo vision
• depth map