{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,2]],"date-time":"2026-04-02T10:17:48Z","timestamp":1775125068736,"version":"3.50.1"},"reference-count":24,"publisher":"Ultrasound Technology Center of Altai State Technical University","issue":"6(46)","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"abstract":"<jats:p>\u0410\u0432\u0442\u043e\u0440\u0430\u043c\u0438 \u0440\u0430\u0441\u0441\u043c\u0430\u0442\u0440\u0438\u0432\u0430\u0435\u0442\u0441\u044f \u0432\u043e\u0437\u043c\u043e\u0436\u043d\u043e\u0441\u0442\u044c \u0438\u0441\u043f\u043e\u043b\u044c\u0437\u043e\u0432\u0430\u043d\u0438\u044f \u043d\u0435\u0439\u0440\u043e\u0441\u0435\u0442\u0435\u0432\u043e\u0439 \u043c\u043e\u0434\u0435\u043b\u0438 (FFNN \u2013 \u043d\u0435\u0439\u0440\u043e\u043d\u043d\u0430\u044f \u0441\u0435\u0442\u044c \u043f\u0440\u044f\u043c\u043e\u0433\u043e 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\u0441\u0435\u043b\u044c\u0441\u043a\u043e\u0445\u043e\u0437\u044f\u0439\u0441\u0442\u0432\u0435\u043d\u043d\u043e\u0433\u043e \u043f\u0440\u043e\u0438\u0437\u0432\u043e\u0434\u0441\u0442\u0432\u0430 \u0440\u0430\u0441\u0442\u0435\u043d\u0438\u0435\u0432\u043e\u0434\u0447\u0435\u0441\u043a\u043e\u0439 \u043f\u0440\u043e\u0434\u0443\u043a\u0446\u0438\u0438 \u0432 \u0438\u0437\u043c\u0435\u043d\u044f\u044e\u0449\u0438\u0445\u0441\u044f \u043f\u043e\u0433\u043e\u0434\u043d\u043e-\u043a\u043b\u0438\u043c\u0430\u0442\u0438\u0447\u0435\u0441\u043a\u0438\u0445 \u0443\u0441\u043b\u043e\u0432\u0438\u044f\u0445 \u043d\u0430 \u0442\u0435\u0440\u0440\u0438\u0442\u043e\u0440\u0438\u0438 \u043b\u0435\u0441\u043e\u0441\u0442\u0435\u043f\u0438 \u041f\u0440\u0438\u043e\u0431\u044c\u044f.<\/jats:p>\n                                                                                            <jats:p>The authors consider the possibility of using a neural network model (FFNN \u2013 feed forward neural network) to predict the yield of spring wheat in the forest-steppe of Western Siberia. The study involved materials from long\u2013term field experiments of SibNIIZiH, a structural subdivision of the SFSCA RAS, conducted in the northern forest-steppe of the Ob region, as well as data on meteorological indicators of the Novosibirsk meteorological observation post for 2001-2018. The work was carried out using publicly available data for the universality of the system when it is used in various natural and agricultural conditions. Qualitative factors (the tillage system, the previous crop, the placement of the crop after steam, the use of intensification means) and meteorological indicators (average decadal air temperatures and precipitation amounts) that determine the crop yield in the study area are identified as predictors. A model has been constructed that allows forecasting the yield of spring wheat for the future growing season, depending on the specified parameters. The coefficient of determination of the model was 0.93, and the mean absolute error varied within 0.05\u00b10.03, which is a fairly high result of the accuracy of predictive models in constantly changing conditions with a combination of abiotic factors and control action. The theoretical and practical results obtained in the course of the work can be used in the development of decision support systems, as well as in planning and evaluating the effectiveness of the placement of agricultural production of crop production in changing weather and climatic conditions on the territory of the Ob region.<\/jats:p>","DOI":"10.25699\/sssb.2022.46.6.053","type":"journal-article","created":{"date-parts":[[2023,1,15]],"date-time":"2023-01-15T08:00:01Z","timestamp":1673769601000},"page":"333-338","source":"Crossref","is-referenced-by-count":2,"title":["FORECASTING THE YIELD OF SPRING WHEAT BASED ON THE USE OF A NEURAL NETWORK IN THE CONDITIONS OF THE FOREST-STEPPE OF THE OB REGION"],"prefix":"10.25699","author":[{"given":"\u041c\u0430\u043a\u0441\u0438\u043c\u043e\u0432\u0438\u0447,","family":"\u041a.\u042e.","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"\u0424\u0435\u0434\u043e\u0440\u043e\u0432,","family":"\u0414.\u0421.","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"\u041a\u0430\u043b\u0438\u0447\u043a\u0438\u043d,","family":"\u0412.\u041a.","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"\u0412\u0430\u0441\u0438\u043b\u044c\u0435\u0432\u0430,","family":"\u041d.\u0412.","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"\u0413\u0430\u043b\u0438\u043c\u043e\u0432,","family":"\u0420.\u0420.","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"\u041a\u0438\u0437\u0438\u043c\u043e\u0432\u0430,","family":"\u0422.\u0410.","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"\u0420\u0438\u043a\u0441\u0435\u043d,","family":"\u0412.\u0421.","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"30408","published-online":{"date-parts":[[2022,12,20]]},"reference":[{"key":"1","doi-asserted-by":"crossref","unstructured":"Walter, A., et al, \u201cOpinion: Smart farming is key to developing sustainable agriculture,\u201d Proceedings of the National Academy of Sciences, vol. 114, \u2116 24, pp. 6148-6150, 2017.","DOI":"10.1073\/pnas.1707462114"},{"key":"2","doi-asserted-by":"crossref","unstructured":"Zhai, Z., et al, \u201cDecision support systems for agriculture 4.0: Survey and challenges,\u201d Computers and Electronics in Agriculture, vol. 170, pp. 105256, 2020.","DOI":"10.1016\/j.compag.2020.105256"},{"key":"3","doi-asserted-by":"crossref","unstructured":"Boote K. J., et al, \u201cThe role of crop systems simulation in agriculture and environment,\u201d International Journal of Agricultural and Environmental Information Systems (IJAEIS), vol. 1, \u2116 1, pp. 41-54, 2010.","DOI":"10.4018\/jaeis.2010101303"},{"key":"4","doi-asserted-by":"crossref","unstructured":"\u0418\u043c\u0438\u0442\u0430\u0446\u0438\u043e\u043d\u043d\u0430\u044f \u043c\u043e\u0434\u0435\u043b\u044c \u0430\u0433\u0440\u043e\u044d\u043a\u043e\u0441\u0438\u0441\u0442\u0435\u043c\u044b \u043a\u0430\u043a \u0438\u043d\u0441\u0442\u0440\u0443\u043c\u0435\u043d\u0442 \u0442\u0435\u043e\u0440\u0435\u0442\u0438\u0447\u0435\u0441\u043a\u0438\u0445 \u0438\u0441\u0441\u043b\u0435\u0434\u043e\u0432\u0430\u043d\u0438\u0439 \/ \u0411\u0430\u0434\u0435\u043d\u043a\u043e \u0412. \u041b. 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\u0444\u043e\u0440\u043c\u0438\u0440\u043e\u0432\u0430\u043d\u0438\u0438 \u043f\u0440\u043e\u0434\u0443\u043a\u0442\u0438\u0432\u043d\u043e\u0441\u0442\u0438 \u043a\u043e\u043b\u043e\u0441\u043e\u0432\u044b\u0445 \u043a\u0443\u043b\u044c\u0442\u0443\u0440 \u0432 \u043b\u0435\u0441\u043e\u0441\u0442\u0435\u043f\u0438 \/ \u0412. \u0415. \u0421\u0438\u043d\u0435\u0449\u0435\u043a\u043e\u0432 \/\/ \u0410\u041f\u041a \u0420\u043e\u0441\u0441\u0438\u0438. \u2013 2018. \u2013 \u0422. 25. \u2013 \u2116. 3. \u2013 \u0421. 455-460."},{"key":"22","doi-asserted-by":"crossref","unstructured":"\u0421\u0438\u043d\u0435\u0449\u0435\u043a\u043e\u0432, \u0412. \u0415. \u042d\u043a\u043e\u043d\u043e\u043c\u0438\u0447\u0435\u0441\u043a\u0430\u044f \u044d\u0444\u0444\u0435\u043a\u0442\u0438\u0432\u043d\u043e\u0441\u0442\u044c \u043f\u0440\u043e\u0438\u0437\u0432\u043e\u0434\u0441\u0442\u0432\u0430 \u0437\u0435\u0440\u043d\u0430 \/ \u0412. \u0415. \u0421\u0438\u043d\u0435\u0449\u0435\u043a\u043e\u0432, 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\u043f\u0440\u043e\u0434\u0443\u043a\u0442\u0438\u0432\u043d\u043e\u0441\u0442\u044c \u044f\u0440\u043e\u0432\u043e\u0439 \u043f\u0448\u0435\u043d\u0438\u0446\u044b \/ \u0410. \u041d. \u0412\u043b\u0430\u0441\u0435\u043d\u043a\u043e [\u0438 \u0434\u0440.] \/\/ \u0421\u0438\u0431\u0438\u0440\u0441\u043a\u0438\u0439 \u0412\u0435\u0441\u0442\u043d\u0438\u043a \u0441\u0435\u043b\u044c\u0441\u043a\u043e\u0445\u043e\u0437\u044f\u0439\u0441\u0442\u0432\u0435\u043d\u043d\u043e\u0439 \u043d\u0430\u0443\u043a\u0438. \u2013 2013. \u2013 \u2116. 5. \u2013 \u0421. 5-9."},{"key":"24","unstructured":"Patterson DW Artificial neural networks \u2013 theory and applications. 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