## 1 Introduction  
Tea is a widely cultivated crop that possesses significant economic and social value [1]. The production of tea involves multiple stages, with the prevention and control of tea diseases being one of the crucial factors affecting tea quality [2]. Tea diseases refer to various diseases that occur in the process of tea tree growth, such as tea red leaf spot, tea white leaf spot, tea brown blight, etc. These diseases can lead to reduced tea yield, decreased quality, and even pose threats to human health [3]. Therefore, the prevention and control of tea diseases hold great significance in ensuring the safety and health of tea production and consumption [4]. Tea disease recognition based on deep learning and image processing has gradually become a hot topic [5]. Deep learning technology, with its characteristics of automation, efficiency, and accuracy, can automatically extract the disease characteristics of tea by training a large number of tea disease images, thus achieving automatic detection of tea diseases [6]. Among them, deep learning methods based on the Convolutional Neural Network (CNN) have been extensively applied in tea disease detection. For example, AlexNet [7], VGG [8], Inception [9], ResNet [10], and so on. The conventional approach to disease detection primarily depends on visual inspection and experiential judgment [11; 12; 13; 14; 15; 16]. Farmers and professionals diagnose and identify diseases based on the appearance and characteristics of tea trees, which is a laborious and lengthy process. Spectroscopic detection methods identify and differentiate different diseases by utilizing the spectral characteristics of plants. For example, [PERSON] et al. [11] conducted principal component analysis on multispectral images and proposed an automated detection method for tomato spotted wilt and pepper powdery mildew disease. This method requires the acquisition of extensive spectral datasets and subsequent preprocessing and feature extraction operations. However, plant spectral data are easily influenced by various interfering factors such as lighting conditions and plant surface morphology, making it difficult to guarantee the quality and reliability of the data. Furthermore, tea disease detection can be performed through image processing and computer vision methods. For example, [PERSON] et al. [12] developed a real-time disease detection method utilizing Otsu thresholding, color space transformation, and a Convolutional Neural Network (CNN) to perform feature extraction, segmentation, and classification of diseases in cucumber, corn, and rice leaves. In addition, non-destructive testing techniques can be utilized to assess and examine the physical characteristics of plants to determine if they have been affected by diseases. [PERSON] et al. [13] introduced a sensor-based method for identifying plant diseases, which collects environmental data such as humidity and temperature using sensors and detects diseases through data analysis. However, the accuracy and stability of temperature and humidity measurements may be influenced by limitations of the measuring equipment, environmental interferences, and measurement errors. This can lead to significant errors and uncertainties in disease detection, making accurate disease detection challenging. Lastly, other detection algorithms, such as Single Shot MultiBox Detector (SSD) [14], represent single-stage approaches with rapid detection speeds but limited localization precision and reduced accuracy. In contrast, two-stage detection algorithms like Fast R-CNN [15] and FPN [16] offer improved localization and overall accuracy at the cost of slower detection speeds. To tackle the aforementioned challenges of low efficiency and limited accuracy in tea disease detection, this study introduces an improved RT-DETR algorithm. The key contributions are as follows:  
(1) Faster-LTNet Integration: The study incorporates Faster-LTNet into the backbone network to enhance computational efficiency by reducing memory access and redundant calculations.