- Tytuł:
- Prognostic Models of Panicum virgatum L. Using Artificial Neural Networks
- Autorzy:
-
Lopushniak, Vasyl Ivanovych
Hrytsuliak, Halyna Myhaylovna
Bykin, Anatoliy Viktorovych
Bordyuzha, Nadia Petryvna
Semenko, Larysa Oleksandryvna
Polutrenko, Myroslava Stepanivna
Kotsyubynska, Yulia Zinoviyivna - Powiązania:
- https://bibliotekanauki.pl/articles/2028041.pdf
- Data publikacji:
- 2021
- Wydawca:
- Polskie Towarzystwo Inżynierii Ekologicznej
- Tematy:
-
switchgrass
productivity
biomass
sewage sludge
precipitate
artificial neural network - Opis:
- The article shows the possibility of using modern methods of artificial intelligence to calculate the yield of biomass of crops according to the given set input data (fertilizer doses, agrochemical parameters of the soil, productivity). The study reflects the results of testing a model of a computer program of an artificial neural network, which allowed forecasting the yield of Panicum virgatum L. (Switchgrass) depending on the joint application of fertilizers mineral and precipitate. On the basis of the calculations, the obtained model of productivity of vegetative mass of switchgrass shows a high level of forecasting efficiency (up to 97%). According to the results of experimental studies, the use of sewage sludge at a doses of 20–40 t/ha provides a dry biomass yield of Panicum virgatum L. (Switchgrass) in the range of 13.1–20.3 t/ha, which is 3.4–7.2 t/ha more than in the option without fertilizer.
- Źródło:
-
Journal of Ecological Engineering; 2021, 22, 11; 62-71
2299-8993 - Pojawia się w:
- Journal of Ecological Engineering
- Dostawca treści:
- Biblioteka Nauki
Artykuł