Application of artificial neural networks to analyze the emergence of soybean seeds after applying herbal treatments

• Autorzy: Niedbała G. , Piekutowska M. , Rudowicz-Nawrocka J. , Adamski M. , Wojciechowski T. , Herkowiak M. , Szparaga A. , Czechowska-Kosacka A.

Warianty tytułu

PL

Zastosowanie sztucznych sieci neuronowych do analizy wschodów nasion soi po zastosowaniu zapraw ziołowych

• Języki publikacji: EN

Abstrakty

EN

The aim of the following work is to indicate factors which significantly affect the emergence of selected soybean varieties after application of natural herbal extracts based on - Levisticum officinale L., Ribes nigrum L., Matricaria chamomilla L., as wet seed treatments using two methods of treatment. The research material included seeds treated for 24 hours in macerats, decoctions and infusions made from the above herb species as well as untreated seeds, seeded together with preparations in point application. Untreated seeds were used as the control group. The experiment was being conducted for 16 days in a greenhouse facility belonging to the COBORU Experimental Station for Variety Testing in Karzniczka. The assessed parameter referred to the percentage of soybean seedlings emergence ability determined based on the number of emerged plants. Indication of the importance of factors in shaping soybean emergence and considering their rank was possible due to the sensitivity analysis of the generated neural network with the MLP architecture 4:4-13-5-1:1 with two hidden layers. All analyzed factors of the experiment significantly shaped the ability of soybean emergence, with the following order: cultivar, application method, herb species from which the extract was made, form of preparation.

PL

Celem pracy było wskazanie czynników istotnie wpływających na wschody wybranych odmian soi po zastosowaniu naturalnych ekstraktów wodnych na bazie ziół - Levisticum officinale L., Ribes nigrum L., Matricaria chamomilla L., jako zapraw nasiennych na mokro z wykorzystaniem dwóch sposobów zaprawiania. Materiał badawczy stanowiły nasiona zaprawiane przez dobę w maceratach, wywarach i naparach sporządzonych z powyższych gatunków ziół oraz nasiona niezaprawiane, wysiewane łącznie z aplikacją punktową preparatów. Za obiekt kontrolny przyjęto nasiona niezaprawiane. Eksperyment prowadzono przez 16 dni w obiekcie szklarniowych należącym do Stacji Doświadczalnej Oceny Odmian COBORU w Karzniczce. Parametrem poddanym ocenie była procentowa zdolność wschodów siewek soi określana na podstawie liczby wzeszłych roślin. Wskazanie istotności czynników w kształtowaniu zdolności wschodów soi oraz uwzględnienie ich rangi było możliwe dzięki analizie wrażliwości wytworzonej sieci neuronowej o architekturze MLP 4:4-13-5-1:1 z dwoma ukrytymi warstwami. Wszystkie analizowane czynniki doświadczenia znacząco kształtowały zdolność wschodów soi, a ich waga miała następującą kolejność: odmiana, sposób aplikacji preparatu, gatunek zioła, z którego sporządzono ekstrakt, forma preparatu.

Słowa kluczowe

EN

soybean; natural seed treatments; neural networks; sensitivity analysis; MLP

PL

soja; naturalne zaprawy nasienne; sieci neuronowe; analiza wrażliwości; MLP

• Wydawca: Sieć Badawcza Łukasiewicz - Poznański Instytut Technologiczny
• Czasopismo: Journal of Research and Applications in Agricultural Engineering
• Rocznik: 2018
• Tom: Vol. 63, nr 4
• Strony: 145--149
• Opis fizyczny: Bibliogr. 30 poz., rys., tab., wykr., zdj.

Twórcy

autor

Niedbała G.

• Poznań University of Life Sciences, Institute of Biosystems Engineering, Faculty of Agronomy and Bioengineering ul. Wojska Polskiego 50, 60-627 Poznań, Poland

autor

Piekutowska M.

• Koszalin University of Technology, Department of Agrobiotechnology, Faculty of Mechanical Engineering ul. Racławicka 15-17, 75-620 Koszalin, Poland

autor

Rudowicz-Nawrocka J.

• Poznań University of Life Sciences, Institute of Biosystems Engineering, Faculty of Agronomy and Bioengineering ul. Wojska Polskiego 50, 60-627 Poznań, Poland

autor

Adamski M.

• Poznań University of Life Sciences, Institute of Biosystems Engineering, Faculty of Agronomy and Bioengineering ul. Wojska Polskiego 50, 60-627 Poznań, Poland

autor

Wojciechowski T.

• Poznań University of Life Sciences, Institute of Biosystems Engineering, Faculty of Agronomy and Bioengineering ul. Wojska Polskiego 50, 60-627 Poznań, Poland

autor

Herkowiak M.

• Poznań University of Life Sciences, Institute of Biosystems Engineering, Faculty of Agronomy and Bioengineering ul. Wojska Polskiego 50, 60-627 Poznań, Poland

autor

Szparaga A.

• Koszalin University of Technology, Department of Agrobiotechnology, Faculty of Mechanical Engineering ul. Racławicka 15-17, 75-620 Koszalin, Poland

autor

Czechowska-Kosacka A.

• Lublin University of Technology, Institute of Environmental Protection Engineering, Faculty of Environmental Engineering ul. Nadbystrzycka 40B, 20-618 Lublin, Poland

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