## 3 Results
### Datasets  
In the pre-trained model (block 1 of Figure 2), we used Yolo11n. At the Domain-Task Adaptation stage (block 2 of Figure 2), we utilized a dataset from Kaggle [22] (block B of Figure 2). We selected a dataset that contains a relatively small number of 895 annotated images of tomatoes. To increase diversity, this set was supplemented with our own photographs of bushes with fruits at different levels of ripeness. These photos were taken at the agro-biotechnical complex of Tyumen University. The total size of the obtained dataset amounted to 1300 images. Examples of tomato images from a specialized dataset are shown in Figure 4. Training on this dataset allowed us to adapt the pre-trained Yolo11n model to the task of tomato detection.  
The resulting model was then ready for further narrow specialization for implementation in agricultural enterprises, where the images of tomatoes grown could differ significantly from both the images of a specialized dataset and from each other.  
Among the main factors that give rise to such differences are the place of cultivation (for example, modular structures on urban farms or rows of plants in greenhouses), the height of bushes, the degree of density and number of fruits in one image, their size, the presence of other objects in the image, etc. In addition, it should be taken into account how the plants will be monitored, in particular, at what height and at what viewing angle the video or camera will be installed.  
Figure 5 shows examples of images of cultivated tomatoes that were obtained in one of the greenhouses in Western Siberia.  
It can be seen that the images of tomatoes obtained in real greenhouse conditions have a number of features that affect the complexity of the detection task. The images contain tomatoes of significantly different sizes, including a large number of small objects. In addition, occlusion is common in photographs when tomatoes are partially hidden by leaves, stems or other objects.  
This creates additional difficulties, since the model must be able to select target objects, even if they are only partially visible. There is also noise against the background of vegetation, especially among dense leaves and stems, and there is a similarity in the colour characteristics of the background and target objects, especially green tomatoes. Images often contain areas with shadows and bright highlights, which makes it difficult to recognize objects. In addition, the image may contain other objects that are visually similar to the  
Figure 4: Examples of tomato images from a specialized dataset for the Domain-Task Adaptation of the YOLO model: (**a,b**) Examples of images from a dataset from Kaggle; (**c**) An example of an image from the agro-biotechnical complex of Tyumen University.  
Figure 5: Examples of images in real-world observation conditions in a greenhouse. (**a,b**) Examples of images where there are extraneous objects that may affect the detection results; (**c**) An example of an image where there are tomatoes belonging to the current row.  
target objects--tomatoes. Another feature was the presence of blurred tomato fruits in the images, which occurs when working with a moving video camera.  
From the collected images (Figure 5), a custom dataset was formed to fine-tune a specialized YOLO model for further implementation at this agricultural enterprise.  
In order to reduce labour costs and to fully reflect the existing operating conditions of the neural network model, the images were not processed. The total number of annotated images of the custom training dataset was 500 examples, and the custom testing dataset was 50 examples. The features of these datasets are shown in Table 2.