Research on the Use of a Neural Network in the Intelligence System of University Research Activities

Gallini, Nadezhda I.; Denisenko, Alexander A.; Kamornitskiy, Daniil T.; Chetyrbok, Petr V.; Motuz, N S

doi:10.1109/elconrus51938.2021.9396216

Cited by 4 publications

(1 citation statement)

References 9 publications

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“…Firstly, in order to solve the problem of creating a high-quality database for training neural networks for recognizing plant photos, it is essential to create a national database taking into account the regional climatic factors. Secondly, to create applications helping agricultural producers to determine the incidence of fruit diseases and advise on methods for their treatment, it is necessary to use the combined machine learning methods, such as the convolutional neural networks (CNN) and fuzzy neural networks (FNN) [2][3][4][5][6][7][8][9][10][11][12][13][14][15].…”

Section: Introductionmentioning

confidence: 99%

Neuromodeling in horticulture and viticulture

Chetyrbok,

Kazak,

Gallini

et al. 2023

E3S Web Conf.

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The article considers the possibilities of using the artificial intelligence in horticulture and viticulture. At present, the artificial intelligence technologies are actively used in agriculture, which make it possible to effectively determine crop yields, automate the cropping and storage of agricultural produce, determine the condition of the soil, the composition and effective use of fertilizers, identify plant diseases and bring weeds under control using recognition methods. The use of the artificial intelligence methods in horticulture and viticulture has its own specific features: firstly, robotic complexes for harvesting cherries, apricots, apples, peaches and grapes; and secondly, the identification of fruit diseases by means photo recognition using neural networks’ machine learning.

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