Artificial Intelligence by Artificial Neural Networks to Simulate Oat (Avena sativa L.) Grain Yield Through the Growing Cycle

Osmar Bruneslau Scremin,Ângela Teresinha Woschinski De Mamann,Juliana Aozane Da Rosa,Cibele Luisa Peter,Ivan Ricardo Carvalho,Cláudia Vanessa Argenta,Laura Mensch Pereira,Ester Mafalda Matter,José Antonio Gonzalez Da Silva,Natiane Carolina Ferrari Basso,Adriana Roselia Kraisig,Eduarda Warmbier

doi:10.5296/jas.v8i4.17711

Abstract

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.

Highlights

The use of artificial neural networks (ANN) has been growing gradually in the representation of the complex system, mainly in a non-linear variable (Leal et al, 2015; Fleck et al, 2016)
In 2017, the volume of rainfall was low in the vegetative phase, accompanied by a high maximum air temperature
In 2018, the largest amount of rainfall was from sowing at 35 days of oat development and with milder maximum air temperature compared to 2017

Summary

Introduction

The use of artificial neural networks (ANN) has been growing gradually in the representation of the complex system, mainly in a non-linear variable (Leal et al, 2015; Fleck et al, 2016). Neural networks can be used to develop prediction models in complex systems and estimate desired parameters, enhancing process optimization and decision making (Huang, et al, 2010; Silva et al, 2014). Due to the necessity to optimize food production with cost reduction and sustainability to ecosystems (Nikolla et al, 2014; Arenhardt et al 2017). White oats are being used more in the food industry, as flakes, as it is an extremely nutritious and healthy cereal (Gutkoski et al, 2009; Silva et al, 2015)

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Results