The intelligent identification of tea diseases is crucial for ensuring tea quality and reducing economic losses in the tea industry. However, the deployment of deep learning models on edge devices remains challenging due to the conflict between detection accuracy and computational overhead. To address this, we propose CA-YOLOv8n, a lightweight object detection model tailored for tea disease diagnosis. Specifically, we introduce a Path-Decoupling strategy to streamline the network structure and integrate the Coordinate Attention (CA) mechanism to enhance the model's spatial awareness of subtle pathological features. Experimental results demonstrate that the proposed model achieves a mean Average Precision (mAP@50) of 98.89% while reducing the parameter count by 32.6% and FLOPs by 24.1% compared to the baseline YOLOv8n. The model was integrated into a diagnostic platform with an automated reporting interface, demonstrating that real-time tea disease identification is feasible on commodity CPU hardware in resource-constrained agricultural environments.
Video summarization aims to identify the important segments of a video and form a concise representation, enabling users to quickly grasp the core information. Existing graph-based video summarization methods suffer from insufficient modeling of multi-scale feature interactions a…
Bio-logging is a popular method for data collection in animal research, especially for hard-to-observe animals. Newer bio-logger generations utilise WiFi technology, enabling researchers to collect high-resolution data at the cost of higher energy expenditure of the devices. In t…
Social media has evolved into a socio-technical infrastructure that shapes public attention, social interaction, and information governance. Understanding how user engagement behaviors, such as retweets, comments, and likes, collectively influence information diffusion is importa…
BACKGROUND AND OBJECTIVE: The heterogeneous response of cancer cells to radiation necessitates comprehensive investigations into cell survival, which are often conducted using clonogenic assays. These assays involve laborious and subjective manual enumeration of cell colonies. Th…
Multi-view subspace clustering has progressed significantly by using deep neural networks to handle nonlinear data representations. A recent advancement, the Multi-view Self-Expressive Subspace Clustering (MSESC) network, achieves markedly higher computational efficiency by subst…
The rapid growth of customized demand and geographically distributed manufacturing resources has increased the need for integrated decision-making in cloud manufacturing systems. In such environments, scheduling, logistics, pricing, and quality decisions are highly interrelated a…