## 1 Introduction  
Advances in artificial intelligence, particularly the successful development of computer vision (CV) neural networks over the past decade, present new opportunities for the agricultural industry, potentially leading to significant changes in the organization of agricultural enterprises (farms). Computer vision neural networks, capable of detecting and recognizing objects in photos and videos with high accuracy, are increasingly employed to monitor processes and objects in agricultural production. In addition to the traditional tasks of monitoring and controlling facilities and production areas, agricultural enterprises have specific needs that deep neural networks can address. Notably, the timely detection of plant diseases or pests is crucial in greenhouses and urban farms, where CV neural networks can be effectively utilized [1, 2, 3, 4, 5]. Another application of CV involves automatically determining product quality and calculating volume [6, 7]. The operational assessment of the volume of products grown is essential for ensuring the economic efficiency of agricultural enterprises. Unlike long-term forecasts, which utilize machine learning methods [8, 9], operational assessments of agricultural production volumes facilitate planning for the enterprise's operations and expected revenues in the medium and short term. This capability enables ordering necessary volumes and types of containers for transporting products to consumers, organizing deliveries and optimal transportation routes and concluding additional supply contracts. However, review papers [3, 5, 10] indicate that the tasks of detecting plant diseases and pests have become more popular among researchers than the equally important tasks of counting products. These tasks are vital for key business processes and can be integrated into specialized software systems that support the activities of agricultural enterprises. In our study, we address a set of tasks to develop a predictive assessment tool for the volume of products grown in greenhouses, using tomatoes as a case study. Tomatoes are among the most popular agricultural products produced in many regions worldwide, necessitating special attention to storage and transportation conditions to prevent damage, which makes short-term (operational) planning for collection, packaging and delivery even more critical. Modern authors have frequently explored the potential of deep learning technologies to combat diseases or pests affecting tomatoes [10], and there are numerous examples of research related to yield estimation for this crop [11, 12]. Researchers' experience has highlighted several relevant features for training neural network models. Specifically, it is important to consider both the specifics of the applied problem and the conditions and location of tomato cultivation across different regions and farms. In other words, neural networks can achieve sufficient accuracy for real-world applications when training on data that closely reflects these operating conditions. Relying solely on well-known, universal training datasets does not yield high accuracy for solving users' applied tasks [5]. Significantly higher accuracy rates are obtained when the neural network model is trained using images from specific locations and growing conditions. However, creating such datasets, evaluating them and verifying their suitability remains time-consuming, requiring specialized expertise and considerable time and effort. In our study, we propose a framework that employs a systematic tuning process for neural network models. This process begins with fundamental general-purpose models (Task-Agnostic Learning) and progresses to specialize the model for specific product types and tasks (Domain-Task Adaptation). Finally, we customize the model to meet the end user's usage conditions (Client-Specific Fine-Tuning). To achieve this, we incorporate studying the end user's needs and the conditions and features of greenhouse use at the target agricultural enterprise into the development and training of the model.