### Literature Review  
In agricultural research, much of the literature emphasizes the importance of accurate fruit detection and localization [8; 9; 10; 11; 12; 13; 14; 15]. [PERSON] et al. [16], demonstrated the use of CNNs for fruit identification from visual data and emphasized the need for robust preprocessing to ensure consistency in image brightness and resolution. Preprocessing techniques, such as normalization and pixel-intensity adjustment, are crucial for providing a consistent baseline for subsequent computational analyses, which is reflected in a wide range of image analysis research. [PERSON] et al. [17] emphasized the growing relevance of innovative technologies in the agri-food sector and highlighted artificial intelligence as a cornerstone of modern agricultural practices. The deep learning (DL) approach has the ability to infer complex patterns and representations from data, and it has been identified as a key driver of innovation, particularly in image-based applications. [PERSON] et al. [18] presented a novel approach to overcoming the limitations of traditional robotic vision systems in occluded agri-food environments using multiview perception and three-dimensional multi-object tracking. This method significantly improved object representation and localization accuracy, and achieved maximum errors of only 5.08% when estimating the number of tomatoes and up to 71.47% when tracking tomatoes in highly occluded environments. [PERSON] et al. [19] demonstrated the effectiveness of DL, particularly the MaskRCNN algorithm, in detecting and counting tomatoes in greenhouse images. Their results outperformed those obtained in previous studies in laboratory conditions or from higher-resolution images and demonstrated the ability of MaskRCNN to implicitly learn object depth to eliminate the background. [PERSON] et al. [20] presented an innovative computer-vision system designed to automate the detection and localization of fruits in a tomato crop in Mediterranean greenhouses. They designed the system to solve three main tasks: first, the detection of ripe tomatoes; second, the determination of their position in the XY plane of the image; and third, the localization of tomato-flower stalks for the same coordinates. To solve the first two problems, the authors used a full set of digital-image-processing tools, including enhancement, edge detection, segmentation, and detailed tomato characterization, which is very important for distinguishing the degree of ripeness and other visual factors under growing conditions. [PERSON] et al. [21] developed an improved YOLOv5s-tomato model that successfully detects the four maturity stages of greenhouse tomatoes with an accuracy of 95.58% and a mean accuracy (mAP) of 97.42%. This model, featuring improved Mosaic data and efficient IoU losses, outperforms the original YOLOv5s model in both accuracy and speed, making it suitable for precision-tomato-picking machine operations. [PERSON] et al. [22] developed a novel algorithm framework for automated fruit picking, focusing on greenhouse tomatoes, which combines a lightweight tomato-instance-segmentation model, YOLO-TomatoSeg, with an accurate tomato localization approach. This framework, integrating RAFT-Stereo disparity estimation and least-squares point cloud fitting, addresses the limitations of current neural-network-based fruit-recognition algorithms and traditional stereo matching methods, enhancing both segmentation accuracy and localization precision for tomato-picking robots. [PERSON] et al. [23] developed a robust tomato-recognition algorithm for autonomous harvesting robots, designed to operate in complex agricultural environments with disturbances like variable illumination and overlapping fruits. Utilizing a*-component and I-component feature images from Lab* and YIQ color spaces, fused through wavelet transformation and refined with an adaptive threshold algorithm, this method efficiently segments tomatoes from backgrounds, demonstrating a high recognition rate suitable for low-cost robotic harvesting.