# LieTom-RTDETR: A Lightweight Real-Time Tomato Detection System for Plant Factories
## 1 Introduction
Researchers have recently focused on developing lightweight network architectures with significantly enhanced model deployment adaptability to edge computing scenarios, including mobile and embedded devices. [PERSON] et al. [16] reduced computational costs by replacing standard convolutions with lightweight grouped spatial convolutions and enhanced feature extraction using a triplet attention mechanism in a YOLOv8 backbone network. Furthermore, they optimized multi-scale feature fusion by substituting the original path aggregation network-feature pyramid network with a bidirectional feature pyramid network. The experimental results demonstrated an identification accuracy of 94.4% and reductions in computational cost, parameters, and model size of 32.1%, 47.8%, and 44.4%, respectively, compared with those of the baseline model. [PERSON] et al. [17] optimized the backbone network using partial convolution and efficient multiscale attention to reduce computational overhead. Furthermore, the original cross-stage partial bottleneck with two convolutions module in the neck structure was replaced with generalized sparse convolution and variety-of-view group shuffle cross stage partial connections modules; an auxiliary head was also incorporated to improve small-target recognition. This model achieved an accuracy of 93.4% and reduced the computational cost, parameters, and model size by 22.9%, 20%, and 50%, respectively, compared to the original model. Furthermore, [PERSON] et al. [18] reduced computational costs by replacing the original backbone network with EfficientNet-B0 and enhanced feature retention using a mobile inverted bottleneck convolution module. They also optimized the loss and activation functions using the Distance Intersection over Union and Hardswish, respectively, to improve convergence and inference speed. Their experimental results indicated an accuracy of 95.9% with a 55.1% reduction in computational cost, 48.9% smaller model, and 59.3% faster inference speed compared to the baseline model. [PERSON] et al. [PERSON] et al. (2019) adopted a lightweight FasterNet backbone to reduce the computational overhead and introduced cascaded group attention and high-level screening-feature fusion pyramid networks to address the feature fusion challenges in complex backgrounds. Furthermore, they implemented the Focaler Intersection over Union loss function to enhance edge detection precision. The resulting model achieved an accuracy of 96.4% with a 16.1% smaller computational cost, 18.2% fewer parameters, and 10.1% faster inference speed compared to the original model. These results of previous research have clearly demonstrated iterative advancements in object recognition and detection technologies by improving critical technical characteristics, including detection accuracy, inference speed, and model size. Although innovative methodological frameworks and engineering solutions have been developed to advance these domains, few studies have evaluated the use of RTDETR-based lightweight models for tomato classification and recognition. To overcome these dual limitations of accuracy degradation and inefficient computation, RTDETR's NMS-free architecture serves as the foundational framework. Its inherent attention mechanism provides robust occlusion reasoning capabilities, though structural optimization remains essential for agricultural edge deployment. Therefore, this study experimentally investigated the recognition performance of a proposed Lightweight Tomato Real-Time Detection Transformer (LiteTom-RTDETR) on images of tomato samples cultivated in plant factory laboratories in three growth stages (immutative, semi-mature, mature) under three supplemental lighting conditions (blue, red, and white). The proposed LiteTom-RTDETR model is based on an RTDETR architecture that was improved to maintain high recognition accuracy while significantly reducing computational complexity. This approach provides a theoretical foundation for the deployment of agricultural vision systems on resource-constrained mobile devices.