PSDNet: Plant Status Detection Network Utilized in an Intelligent Bougainvillea Glabra Sensing and Watering System

Bougainvillea glabra, commonly planted landscape flowers in Macau, requires the precise water management during whole growing process. Conventional timed irrigation systems, however, fail to offer the required level of precision. Sensor-based watering systems have been employed, utilizing soil moisture and temperature sensors to monitor plant conditions. Nonetheless, these sensors are proved to be unreliable due to various soil factors, such as the presence of stones. Thus, plants still require botanical experts to judge the status based on plant photos. To address this challenge and provide a dependable evaluation of the plant’s status, this study proposes a novel approach: a Plant Status Detection Net (PSDNet) for Bougainvillea sensing, which uses computer vision technology to replace botanical experts in decision-making. Different from directly using image classification, this paper combines object detection to extract plant leaves, and then makes decisions based on leaves. By utilizing the Regions of Interest (RoI) structure from a pre-trained module called Leaf Regions with CNN Features (Leaf-RCNN), the proposed PSDNet effectively extracts leaf regions and their corresponding feature maps from captured images. To further improve accuracy, a specialized decoupling head and a position embedding module are integrated into the network to enable the extraction of relative position information between leaves. Finally, in a pilot project at Macau Keang Peng Middle School (MKPMS), an automated watering system was implemented using PSDNet, leading to a substantial increase in the flowering rate. The anticipated adoption of this watering system is projected to significantly advance landscaping practices in Macau and beyond.