## 5 Conclusions  
Computer vision technologies open up new opportunities for improving the operations of agricultural enterprises, farms and greenhouses. In addition to the well-known tasks of detecting plant diseases, the operational evaluation of production volumes is becoming increasingly important. This evaluation helps optimize the production processes of agricultural enterprises. For instance, the widely cultivated tomato requires careful monitoring and timely planning for harvesting and packaging, as well as organizing supplies to consumers. The large-scale implementation of computer vision systems in greenhouses is feasible with approaches and technologies that reduce the cost, time and complexity of implementation, particularly in creating training data and preparing neural network models. However, using publicly available computer vision models pre-trained on universal datasets, such as YOLO, often does not provide the necessary accuracy for applied tasks. Creating datasets for further training of models and preparing these models is a complex process. It requires high qualifications, significant time and effort, especially when developing methods to calculate production volumes while considering the specificities of greenhouse cultivation. This study proposes an approach aimed at addressing this issue. The primary contributions of this work are outlined below. A method for sequential training on detection models was developed, which includes the Domain-Task Adaptation stage and the Client-Specific Fine-Tuning stage. During domain adaptation, the model is trained on a large, likely public, specialized dataset that is tailored to solve a specific task or set of tasks, particularly the detection of tomatoes. At the fine-tuning stage, a small custom dataset is created, containing images obtained directly from the future operational site of the model. This allows for a more accurate consideration of all the features of the images, growing conditions, plant appearances and other specifics. As a result, the lightweight YOLO1n model achieves high validation accuracy for tomato detection (mAP50 > 0.83, Precision > 0.75, Recall > 0.73), while also reducing the requirements for computing resources needed for training and operating the model. Thus, based on a single specialized model, customized models can be prepared quickly and with minimal effort for implementation in different greenhouses and farms. A custom training and test dataset was developed for counting tomatoes grown in a greenhouse. Unlike publicly available datasets, which often feature images taken in laboratory settings, the created datasets reflect the real specifics of growing tomatoes in a greenhouse, including dense and tall vegetation, occlusions and the presence of significantly different-sized and small-scale objects. These datasets can be used to train and validate YOLO models for detecting tomatoes grown in similar greenhouse environments. An algorithm for counting tomatoes based on a detection model was developed, which operates with video frames obtained during the monitoring of plants. This algorithm can identify tomatoes located in the front row of plants in the greenhouse and only count those, making it suitable for organizing video surveillance from a mobile platform that moves between rows of cultivated plants. To further develop this approach, we plan to create new pipelines where the detection results will be utilized to address other applied problems, such as detecting and accounting for illiquid products, classifying tomatoes by ripeness and identifying fruit diseases. Conceptualization, I.G. and D.G. ; methodology, I.G., A.G. and D.G. ; formal analysis, I.G. ; software, A.G.; data curation, D.G., A.G. and T.C. ; investigation, I.G. and A.G.; writing--original draft preparation, I.G. ; writing--review and editing, I.G., T.C. and D.G. ; visualization, I.G., A.G. and D.G. ; funding acquisition, I.G. All authors have read and agreed to the published version of the manuscript.