On the need for a paradigm shift in agricultural research (message two)

Analysis of the agricultural research directions related to the prevailing modern paradigm and the fourth agricultural revolution is presented. The next stage in the development of digitalization of agriculture is smart farming, which involves the use of various technological innovations, including machine learning, computer vision, remote sensing, geo-information modeling, and the Internet of Things. The peculiarities of using digital technologies and methods of artificial intelligence on farming systems blocks are considered, which are expedient to apply in planning scientific research and analyzing the obtained results. Formation of crop rotations is carried out by modeling their productivity using various artificial intelligence approaches based on time series of crop yields and remote sensing data. Selection of the main tillage system is possible with the help of predictive models of cultivated crop yields and other basic parameters of its efficiency with the help of machine learning. The development of recommendations on timing, doses and methods of fertilizer application is carried out with the help of artificial intelligence-based models. Synchronization of fertilizer application with soil properties, weather conditions and cultivated crops is regulated through various digital management approaches. Protection of crops from pests is realized in the system of forecasting their development on the basis of weather data, control actions and other types of data. Predictive models of crop yields in agricultural research should solve the problems of crop simulation and control actions under office-compiled conditions. Based on the results of virtual models, programs and field study plans are developed to validate these models. Selection and support of agro-technologies are implemented in the system of registration and analysis of the interaction of a wide range of conditions and factors by means of proximal and remote sensing (monitoring) with subsequent modeling of the processes and objects for the creation of a decision support system in the form of DFMS (digital farming management system). In order to scale and adapt the innovations, it is advisable to utilize the capabilities of citizen science and Web-based networking platforms. 

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