# LieTom-RTDETR: A Lightweight Real-Time Tomato Detection System for Plant Factories
## 1 Introduction  
Tomatoes are well-suited for multicyclic cultivation in greenhouse plant factories because of their short growth cycle, which enables multiple harvests annually for a high economic return [1]. Currently, tomato harvesting is a labor-intensive and time-consuming process [2] primarily because tomato plants yield a high fruit density in an uneven vertical distribution and their dense foliage often obstructs access to fruit clusters. As these challenges make finding fruits difficult and thereby hinder the effective implementation of mechanical automation, realizing the rapid and accurate identification of ripe tomatoes represents a crucial step toward automating the harvesting process. Indeed, object recognition plays a crucial role in the vision systems of tomato-harvesting robots. Rapid advances in deep learning have introduced revolutionary computer vision methodologies, with modern object detection algorithms transitioning from traditional handcrafted feature extraction approaches to data-driven paradigms based on neural networks [3, 4]. Traditional image processing techniques primarily include color feature identification, edge detection, and texture analysis, whereas deep-learning-baseddetectors widely employ convolutional neural networks (CNNs) to realize feature extraction [5; 6]. For example, [PERSON] et al. [7] achieved an 86.3% recognition accuracy for ripe tomatoes in complex scenarios by analyzing edge contour features and improving the Hough circle transformation algorithm, which significantly enhanced computational efficiency. Furthermore, [PERSON] [8] combined image saliency with a CNN model to extract and classify features, achieving an accuracy of 95.6% on a self-assembled dataset containing 26 classes of fruits and vegetables. The recognition accuracy of most CNNs can be improved by increasing the network depth and stacking additional convolutional layers to enhance feature extraction [9]. However, this approach requires significant computational resources and storage overhead, potentially leading to training inefficiencies, excessive parameters, and challenges in meeting practical deployment requirements for mobile and embedded devices. Various implementations of object recognition in agricultural produce detection systems have been achieved through representative CNN-based single-stage detectors, particularly the single-shot multibox detector (SSD) [10] and you only look once (YOLO) [11] approaches. The operational superiority of these models is derived from their sustained real-time inference speeds and energy-efficient computation, which satisfy the stringent requirements for resource-constrained edge deployment in precision agriculture scenarios. Notably, [PERSON] et al. [12] replaced the backbone network of an SSD with a lightweight MobileNetV3 network and enhanced its original model framework using depthwise separable convolutions to achieve a recognition accuracy of 92.5%, a 7.9% improvement over that of the baseline model. [PERSON] et al. [13] enhanced the original YOLOv8 model by integrating a Large Separable Kernel Attention mechanism, dynamic upsampling layer, and Inner Intersection over Union loss function. The modified model achieved a recognition accuracy of 99.4% on a test dataset, demonstrating a 3.3% higher precision than that provided by a YOLOv5 model. Although CNN-based models typically exhibit satisfactory detection accuracy, two critical steps--threshold filtering and non-maximum suppression (NMS)--dictate their robustness and inference speed [14]. The Real-Time Detection Transformer (RTDETR) [15] is a novel detection method that eliminates the need for NMS post-processing, thereby reducing optimization complexity and enhancing robustness. Nevertheless, challenges persist in RTDETR implementation, including redundant backbone architectures and suboptimal data flow between modules.