## 3 Results  
Results were obtained for the developed hybrid model that combined CNNs and dual 2D matrices for the classification and PAFs. The results of the visualization of the tomato detection and positioning method within the DL and the image processing for subsequent robotic harvesting systems are presented in Figure 5. The figure shows unripe tomatoes with color-coded labels that indicate key analysis points: the fruit centers (1: green dot), sepals (2: red dot), and abscission zones (3: sky blue dot), and the branching points of the branches (4: black dot). These labels help to identify and evaluate the plant structure for accurate robotic harvesting. The supporting images (a, b, c, and d) show various image-processing and -analysis steps, such as segmentation and contour detection, with the identification of the points in the center of attachment. The results suggest the high accuracy of object recognition achieved by the model, which indicates its application potential in real agricultural conditions. Errors in key point detection were more common in the images with small or clustered fruits; however, overall, the model demonstrated a high level of performance and provided useful data for robotic control. Figure 5: Results of deep learning and the evaluation of the effect of jointly using the branching point to estimate the tomato pose and center-of-gravity point, (**a**–**d**): Evaluation of threshold preprocessing method for feature extraction in image analysis. Figure 6 shows six tomato-brush images that demonstrate various stages of tomato ripening and arrangement, not including indistinguishable tomato key points. In each image, the tomato plants are marked using colored dots and connected by lines to highlight key anatomical points and their relationships. The lines that connect these points are represented in their respective colors, which indicate the anatomical relationships between these elements. Figure 6 illustrates the methods used to identify the positions of the tomatoes and their anatomical parts, which has significant applications for the development of robotic harvesting algorithms. The images show both successful and difficult feature recognition caused by overlapping or visually merging plant parts, which highlights the importance of using an integrated approach to visual information recognition and processing for the automation of agricultural processes. In our study, the accuracy of the key point localization and the part-detection errors were quantified for the proposed method. The key-point-localization errors are presented in Table 1, where they are categorized into types: center-of-gravity point, calyx, abscission zone, and branching point. The errors were measured at the pixel level and ranged from 5.4-10.6 pixels horizontally and 5.8-13.1 pixels vertically. On average, the errors for all the key points were 5.3 pixels on the \(x\)-axis and 6.2 pixels on the \(y\)-axis. The smallest error  
Figure 6: Results of recognizing tomato samples that included indistinguishable key points (parts). in pixels, approximately 2.6 pixels, was observed for the calyx, and the largest error was observed for the branching point (10.6-13.1 pixels), which was three times larger than the other key points. This high error rate at the branching point occurred due to its low visibility, often resulting from its merging with stem parts in adjacent areas. Despite this, the calyx areas and the tomato's center of gravity point had the lowest pixel errors, which highlights their geometric importance in enabling robotic systems to accurately identify harvest points. The last row in Table 1 presents the evaluation of the key point detection for a normalized distance. For this metric, the key point is considered to have been accurately detected if the error is less than half the length of the bounding box diagonal. An error in this context means the Euclidean distance between the detected point and the real point.