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

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.

Application of artificial neural networks and genetic algorithm to predict and optimize greenhouse banana fruit yield through nitrogen, potassium and magnesium

“…The predictive accuracy of their ANN model was confirmed in the training and testing sets with R 2 values of 0.998 and 0.996, respectively. Scremin et al [ 24 ] used ANN model for modeling and predicting oat grain yield according to four levels of nitrogen as input variables.…”

“…Among the various ML algorithms, artificial neural networks (ANNs) have been considered as the most well-known models [ 22 ]. The effectiveness and reliability of these models in investigating and predicting different fertilizers have been confirmed in different plants such as wheat [ 23 ] and oat [ 24 ]. Genetic algorithm (GA) is a well-known single objective optimization algorithm, which a hybrid with ANN (ANN-GA) make them a potential hybrid model for modeling and optimizing in different biology and agriculture systems such as soil heavy metals [ 25 ], plant tissue culture [ 19 , 26 , 27 ], and adsorption and photocatalysis of a synthesized nanomaterial [ 28 ].…”

Artificial Intelligence in the Simulation of Fungicide Management Scenarios for Satisfactory Yield and Food Safety in oat Crops

“…In this sense, artificial intelligence techniques have emerged as an alternative for simulating and optimizing agricultural systems (Trautmann et al, 2020). Artificial neural networks (ANN) are among artificial intelligence techniques focused on implementing models that resemble biological neural structures (Scremin et al, 2020). Therefore, they can learn and generalize information from external data and provide consistent results for unknown data (Silva et al, 2018;De Mamann et al, 2019).…”

“…Therefore, they can learn and generalize information from external data and provide consistent results for unknown data (Silva et al, 2018;De Mamann et al, 2019). Artificial neural network models can efficiently simulate and estimate results based on common characteristics of the selected input variables (Scremin et al, 2020).…”

Redes Neurais Artificiais na Previsibilidade da Produtividade de Grãos da Aveia pelo uso do Nitrogênio e Condições do Ciclo de Desenvolvimento da Cultura

“…As RNAs são técnicas computacionais que apresentam um modelo inspirado na estrutura neural de organismos inteligentes, adquirem conhecimento através da experiência e o tornam disponíveis para uso. Assim, uma RNA é capaz de reconhecer padrões, ou seja, possui a capacidade de aprender por meio de exemplos e de generalizar 2 as informações aprendidas, gerando modelos não lineares, o que torna a sua aplicação fundamental e eficiente para simulação da produtividade de diversas culturas [12,13].…”