Neural Network Technologies in Predicting the Operating Status of Agricultural Enterprises
All agricultural facilities in Russia are currently going through digital transformation. However, the process needs a unified approach for the entire agricultural sector. Neural network methods have already proved extremely effective in various areas of IT. The authors used neural networks to analy...
| Main Author: | |
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| Format: | Article |
| Language: | Russian |
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Kemerovo State University
2023-12-01
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| Series: | Техника и технология пищевых производств |
| Subjects: | |
| Online Access: | https://fptt.ru/en/issues/22269/22259/ |
| _version_ | 1797346883126951936 |
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| author | Aleksandr V. Grachev |
| author_facet | Aleksandr V. Grachev |
| author_sort | Aleksandr V. Grachev |
| collection | DOAJ |
| description | All agricultural facilities in Russia are currently going through digital transformation. However, the process needs a unified approach for the entire agricultural sector. Neural network methods have already proved extremely effective in various areas of IT. The authors used neural networks to analyze statistic data and assess the performance of agricultural infrastructure. This study involved technical data from the production cycle of agro-industrial enterprises, namely packaging and greenhouses. The data obtained were analyzed using artificial neural networks. The procedure included identifying a set of factors that described an agro-industrial complex or some of its properties that corresponded to a specific task. These data were used in planning and making managerial decisions. The program identified five factors that described the state of an agricultural enterprise. These factors were used to build a model while its elements served as output data for the neural network. The model calculated the future state of the object. Trials were run on a limited data set on a multilayer perceptron. The neural network showed reliable results for a small data set. The root mean square error of was 0.1216; the mean modulus deviation was 0.0911. In this research, modern neural network technologies demonstrated good prospects for the domestic agro-industrial complex as a method of control, management, and dispatching. However, specific operational patterns require further studies. |
| first_indexed | 2024-03-08T11:39:39Z |
| format | Article |
| id | doaj.art-ab67581bc4bc42ac92817ae2afe8da1e |
| institution | Directory Open Access Journal |
| issn | 2074-9414 2313-1748 |
| language | Russian |
| last_indexed | 2024-03-08T11:39:39Z |
| publishDate | 2023-12-01 |
| publisher | Kemerovo State University |
| record_format | Article |
| series | Техника и технология пищевых производств |
| spelling | doaj.art-ab67581bc4bc42ac92817ae2afe8da1e2024-01-25T08:23:19ZrusKemerovo State UniversityТехника и технология пищевых производств2074-94142313-17482023-12-0153481682310.21603/2074-9414-2023-4-2481Neural Network Technologies in Predicting the Operating Status of Agricultural EnterprisesAleksandr V. Grachev0Siberian State Industrial University , Novokuznetsk, RussiaAll agricultural facilities in Russia are currently going through digital transformation. However, the process needs a unified approach for the entire agricultural sector. Neural network methods have already proved extremely effective in various areas of IT. The authors used neural networks to analyze statistic data and assess the performance of agricultural infrastructure. This study involved technical data from the production cycle of agro-industrial enterprises, namely packaging and greenhouses. The data obtained were analyzed using artificial neural networks. The procedure included identifying a set of factors that described an agro-industrial complex or some of its properties that corresponded to a specific task. These data were used in planning and making managerial decisions. The program identified five factors that described the state of an agricultural enterprise. These factors were used to build a model while its elements served as output data for the neural network. The model calculated the future state of the object. Trials were run on a limited data set on a multilayer perceptron. The neural network showed reliable results for a small data set. The root mean square error of was 0.1216; the mean modulus deviation was 0.0911. In this research, modern neural network technologies demonstrated good prospects for the domestic agro-industrial complex as a method of control, management, and dispatching. However, specific operational patterns require further studies.https://fptt.ru/en/issues/22269/22259/neural networksmachine learningmultilayer perceptronstatisticsforecastingmodelsagricultureequipment |
| spellingShingle | Aleksandr V. Grachev Neural Network Technologies in Predicting the Operating Status of Agricultural Enterprises Техника и технология пищевых производств neural networks machine learning multilayer perceptron statistics forecasting models agriculture equipment |
| title | Neural Network Technologies in Predicting the Operating Status of Agricultural Enterprises |
| title_full | Neural Network Technologies in Predicting the Operating Status of Agricultural Enterprises |
| title_fullStr | Neural Network Technologies in Predicting the Operating Status of Agricultural Enterprises |
| title_full_unstemmed | Neural Network Technologies in Predicting the Operating Status of Agricultural Enterprises |
| title_short | Neural Network Technologies in Predicting the Operating Status of Agricultural Enterprises |
| title_sort | neural network technologies in predicting the operating status of agricultural enterprises |
| topic | neural networks machine learning multilayer perceptron statistics forecasting models agriculture equipment |
| url | https://fptt.ru/en/issues/22269/22259/ |
| work_keys_str_mv | AT aleksandrvgrachev neuralnetworktechnologiesinpredictingtheoperatingstatusofagriculturalenterprises |