MOSCOW, July 15A neural network computer vision system for increasing the yield of garden strawberries was developed by Tyumen State University scientists. Using artificial intelligence, the system counts berries, assesses their ripeness, identifies diseases, and also counts leaves and whiskers. The development can be used on smart farms, city farms and conventional greenhouses. The results were published in the journal Bulletin of Russian Agricultural Science.
Today, one of the main directions of agribusiness development is the development of smart farming systems that can automate plant care, increase the profitability and environmental friendliness of agricultural production. Intelligent agricultural complexes, known as smart farms, are high-tech systems where plants are grown using automated control of operating modes, nutrition and microclimate.
Computer vision neural networks open up new possibilities for smart farms, allowing them to continuously monitor their produce and participate in tasks that have not yet been automated, such as detecting and recognizing plant diseases or pests.
The development of scientists from Tyumen State University (TSU) is aimed at comprehensively assessing the condition of plants and making informed decisions to optimize their cultivation.
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"Computer vision models operate automatically and serve as sources of data for decision-making on a smart farm. For example, discovered whiskers should be cut off, since the plant enters the reproduction stage, and part of the plant’s nutrition is spent on their development. Consequently, the number and size of berries are reduced,” noted one of the developers of the system, graduate student of the School of Computer Science Dmitry Glukhikh.
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According to him, analysis of data on the number and ripeness of berries makes it possible to predict the harvest, adjust the irrigation regime and the composition of the nutrient solution. When identifying plant diseases using a neural network, it is possible not only to diagnose them, but also to determine the type of disease, which ensures timely adoption of treatment measures.
The scientist believes that the introduction of computer vision models on smart farms will increase the autonomy of such complexes.
In addition, as Dmitry Glukhikh noted, combining computer vision models with decision support systems can make smart farms even smarter. 
“Such systems not only detect diseases and calculate yields, but also provide farmers with recommendations for optimizing production depending on the current situation,” he said, emphasizing that potential benefits include reducing the time for decision-making and yield forecasting, reducing the risk of crop loss due to diseases by 40-70%, as well as reducing the requirements for the qualifications of service personnel.
The experiments were carried out on garden strawberries, which are grown on modules of an urban farm deployed at the Tyumen State University Agrobiotechnical Complex.
During the study, scientists used pretrained neural network computer vision model YOLOv8. The system currently contains an ensemble of eight trained models. Each of them performs its own task, and two separate neural networks control the work of the others. This approach, according to scientists, reduces the probability of error by 30%.
The research was carried out within the framework of a grant in the form of a subsidy from the federal budget to provide state support for world-class scientific and educational centers — the West Siberian Research Center.
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