Open AccessArtificial Intelligence by Artificial Neural Networks to Simulate Oat (Avena sativa L.) Grain Yield Through the Growing Cycle
Author(s) -
Osmar Bruneslau Scremin,
José Antônio Gonzalez da Silva,
Ivan Ricardo Carvalho,
Ângela Teresinha Woschinski De Mamann,
Adriana Rosélia Kraisig,
Juliana Aozane Da Rosa,
Cibele Luisa Peter,
Eduarda Warmbier,
Laura Mensch Pereira,
Natiane Carolina Ferrari Basso,
Cláudia Vanessa Argenta,
Ester Mafalda Matter
Publication year - 2020
Publication title -
journal of agricultural studies
Language(s) - English
Resource type - Journals
ISSN - 2166-0379
DOI - 10.5296/jas.v8i4.17711
Subject(s) - avena , artificial neural network , biomass (ecology) , randomized block design , agricultural engineering , yield (engineering) , productivity , agronomy , mathematics , fertilizer , environmental science , computer science , engineering , biology , artificial intelligence , economics , materials science , metallurgy , macroeconomics
Artificial neural networks simulating oat grain yield throughout the crop cycle, can represent an innovative proposal regarding management and decision making, reducing costs and maximizing profits. The objective of the study is to develop biomathematical models via artificial neural networks, capable of predicting the productivity of oat grains by meteorological variables, nitrogen management and biomass obtained throughout the development cycle, making it possible to plan more efficient and sustainable managements. In each cultivation system (soybeans/oats; maize/oats), two experiments were carried out in 2017 and 2018, one for analyzing grain yield and the other for cutting every 30 days to obtain biomass. The experiments were conducted in a randomized block design with four replications for four levels of N-fertilizer (0, 30, 60 and 120 kg ha-1), applied in the stage of the 4th expanded leaf. The use of the artificial neural network makes it possible to predict grain yield by harvesting the biomass obtained at any stage of oat development, together with the handling of the nitrogen dose and meteorological information during cultivation. Therefore, a new tool to aid the simulation of oat productivity throughout the cycle, facilitating faster decision making for more efficient and sustainable management with the crop.