# Greenhouse Ventilation Equipment Monitoring for Edge Computing
###### Abstract  
Digital twins based on real-world scenarios are heavily reliant on extensive on-site data, representing a significant investment in information technology. This study aims to maximize the capabilities of visual sensors, like cameras in controlled-environment agriculture, by acquiring more target-specific information at minimal additional cost. This approach not only reduces investment but also increases the utilization rate of existing equipment. Utilizing YOLOv7, this paper introduces a system with rotatable pan-tilt cameras for the comprehensive monitoring of large-scale greenhouse ventilation systems. To mitigate the computational load on edge servers at greenhouse sites caused by an abundance of video-processing tasks, a Region of Interest (ROI) extraction method based on tracking is adopted. This method avoids unnecessary calculations in non-essential areas. Additionally, we integrate a self-encoding approach into the training phase, combining object detection and embedding to eliminate redundant feature extraction processes. Experimental results indicate that ROI extraction significantly reduces the overall inference time by more than 50%, and by employing LSTM to classify the state of the fan embedding sequences, a 100% accuracy rate was achieved. YOLOv7; autoencoders; state recognition; edge computing +
Footnote †: journal: _Sci._  
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the utilization of an image classification network to recognize the status of these detected objects. For instance, [PERSON] et al. [7] employed YOLOv5 for bolt detection in images and PIPNet to detect key points for assessing bolt status and identifying defects. While utilizing deep learning in both phases enhances accuracy and generalizability, it demands considerable computational resources. This poses challenges in achieving optimal performance on edge computing [8; 9] devices, such as the RK3399 Pro platform discussed in this study. In recent years, a significant body of research has developed which focuses on enhancing real-time object detection or its integration with other tasks. These studies aim to optimize the balance between detection efficiency and precision within the constraints of limited resources. Object detection methodologies are principally categorized into two-stage networks, exemplified by Faster R-CNN, and one-stage networks, typified by YOLO. The pursuit of real-time capabilities predominantly employs one-stage networks. The strategies employed to augment detection speed are varied, encompassing the adoption of lighter network architectures, the implementation of multi-task learning for multiple tasks, and the design of more straightforward features. [PERSON] et al. [10] conducted a comparative analysis of object detection networks within the context of forest smoke detection, discovering that YOLOv3 delivers the most rapid detection alongside commendably high precision. To address the inefficiencies of Non-Maximum Suppression (NMS) in one-stage methodologies, [PERSON] et al. [11] introduced an innovative end-to-end detection algorithm that obviates the need for NMS. By redefining the loss function, treating the instance with minimal loss as the positive sample (and all others as negative), and selecting the top k results in the post-processing phase, they refined the training process. [PERSON] et al. [12] embarked on research into pedestrian detection and action recognition on Unmanned Aerial Vehicles (UAVs) and designed a model that jointly trains two tasks to achieve the transfer of deep features. Their methodology initiates with the detection of pedestrians through an object detection network, followed by the extraction of deep feature maps for the integration of temporal data, using BNLSTM for action detection. [PERSON] et al.