Comparison between rice grain yield predictions using artificial neural networks and a very simple model under different levels of water and nitrogen application

In this paper, a method for identifying the loss of prestressing force (prestress-loss) in the cable-anchorage system of prestressed concrete structures using the impedance responses and artificial neural networks (ANNs) is developed. First, theories of impedance responses and damage detection methods for diagnosing the occurrence and the severity of prestress-loss are presented. In which, the occurrence of prestress-loss is determined by MAPD (Mean Absolute Percentage Deviation) index. Then, the severity of the prestress-loss is determined by ANNs. The feasibility of the developed method is verified by numerical simulations for a real cable-anchorage system with different levels of prestress-loss. The reliability of the numerical simulations for impedance responses is evaluated by comparison to experimental results. Finally, the occurrence and severity of the prestress-loss are exactly identified by the proposed method. The results of this study show that the proposed method is highly effective in determining the prestress-loss in the cable-anchorage system

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Adaptability and yield stability of soybean genotypes by mean Eberhart and Russell methods, artificial neural networks and centroid

Agronomy Science and Biotechnology ◽

10.33158/asb.r142.v8.2022 ◽

2021 ◽

Vol 8 ◽

pp. 1-13

Author(s):

Mário Carmo Oda ◽

Tuneo Sediyama ◽

Cosme Damião Cruz ◽

Moysés Nascimento ◽

Éder Matsuo

Keyword(s):

Neural Networks ◽

Artificial Neural Networks ◽

Grain Yield ◽

Block Design ◽

Joint Analysis ◽

Phenotypic Stability ◽

Randomized Block Design ◽

Soybean Genotypes ◽

Artificial Neural

The soybean crop is prominent in national and international scenarios. A large part of the world production of soybean is cultivated in Brazil and this has been possible due to the performance of different technological areas, among them genetics and plant breeding. Soybean breeding has acted in the development and launch of new cultivars and for this it is required the studies of interaction genotypes x environments and those of adaptability and stability. Thus, the objective was to evaluate the adaptability and phenotypic stability of the grain yield of late-cycle soybean genotypes. Five experiments were conducted in the state of Minas Gerais, each of which was considered as an environment. In each, 17 soybean genotypes were evaluated in a randomized block design with three repetitions, for grain yield, in kg ha-1. The data were analyzed by means of individual (each environment) and joint analysis of variance. Subsequently, analyses of adaptability and phenotypic stability were performed using the methods of Eberhart and Russell (1966), Artificial Neural Networks (Nascimento et al., 2013) and Centroid (Rocha, Muro‑Abad, Araujo, & Cruz, 2005). The results indicated the classification of the analyzed genotypes for unfavorable, general or favorable adaptability, with high or low stability. DM-339 is indicated for favorable environments and UFV-18 (Patos de Minas), UFV91-651226, UFV99-8552093, UFV01-871375B, UFV01-66322813 and UFV99-8552099 are indicated as general adaptability, considering the three methods of adaptability and stability analysis.

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Artificial Neural Networks, Postdigital Infrastructures and the Politics of Temporality

10.5117/9789463727426_ch15 ◽

2021 ◽

Author(s):

Andreas Sudmann

Keyword(s):

Machine Learning ◽

Neural Networks ◽

Artificial Neural Networks ◽

Levels Of Analysis ◽

Ethnographic Research ◽

Artificial Neural ◽

Relationship Of ◽

The Relationship ◽

Different Levels ◽

Learning Data

This essay examines the infrastructures and temporalities of modern AI technology based on artificial neural networks (ANN) and aims to contribute to a more substantial understanding of its political challenges. In order to unlock the different temporalities of ANN, a theoretical framework for the relationship of media and infrastructures is suggested that also might help to distinguish between the different levels of analysis related to specific steps and aspects of the machine learning process (the collection and production of learning data, the training of AI models etc.). An important reference point for the following considerations is ethnographic research conducted at TwentyBN,1 a Toronto and Berlin based AI company specialized in ANN and computer vision that just recently developed an app for the fitness market.

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Artificial Intelligence Simulating Grain Productivity During the Wheat Development Considering Biological And Environmental Indicators

Journal of Agricultural Studies ◽

10.5296/jas.v7i3.15153 ◽

2019 ◽

Vol 7 (2) ◽

pp. 197

Author(s):

Ângela Teresinha Woschinski De Mamann ◽

José Antonio Gonzalez da Silva ◽

Manuel Osório Binelo ◽

Osmar Bruneslau Scremin ◽

Adriana Roselia Kraisig ◽

...

Keyword(s):

Neural Networks ◽

Artificial Neural Networks ◽

Grain Yield ◽

Air Temperature ◽

Network Architecture ◽

Wheat Grain ◽

Development Cycle ◽

Grain Productivity ◽

Artificial Neural ◽

Maximum Air Temperature

The artificial neural networks modeling might simulate the efficiency of wheat grain yield involving biological and environmental conditions during the development cycle. Considering the main succession systems in wheat crop in Brazil, the study aimed to adapt an artificial neural network architecture capable of predict the wheat grain productivity throughout the growth cycle, involving nitrogen and non-linearity of maximum air temperature and rainfall. The field experiment was conducted in two successions systems (soybean/wheat and maize/wheat) in 2017 and 2018, the trial design was in a randomize blocs with eight replicate in the level 0, 30, 60, and 120 kg ha-1 N-fertilizer doses in the phenological stage of third fully expanded leaves. Every 30 day of the development cycle were obtained the biomass yield, maximum air temperature and accumulated rainfall information. The perceptron multi-layered artificial neural networks with backpropagation algorithm with network architecture 5-8-1 and 5-7-1 in soybean/wheat and maize/wheat system respectively, is able to simulate the wheat grain yield involving the nitrogen dose at top-dressing and the non-linearity of maximum air temperature and rainfall with biomass information obtained during the cycle crop.

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

Journal of Agricultural Studies ◽

10.5296/jas.v8i4.17711 ◽

2020 ◽

Vol 8 (4) ◽

pp. 610

Author(s):

Osmar Bruneslau Scremin ◽

José Antonio Gonzalez da Silva ◽

Ivan Ricardo Carvalho ◽

Ângela Teresinha Woschinski De Mamann ◽

Adriana Roselia Kraisig ◽

...

Keyword(s):

Neural Networks ◽

Decision Making ◽

Artificial Neural Networks ◽

Grain Yield ◽

Block Design ◽

Randomized Block Design ◽

Development Cycle ◽

Artificial Neural ◽

Four Levels ◽

Reducing Costs

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.

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Modelling of Some Aspects of Skilled Locomotor Behaviour Using Artificial Neural Networks

Computational Intelligence and its Applications - Computational Intelligence for Movement Sciences ◽

10.4018/978-1-59140-836-9.ch005 ◽

2011 ◽

pp. 172-196 ◽

Cited By ~ 2

Author(s):

Stephen D. Prentice ◽

Aftab E. Patla

Keyword(s):

Neural Networks ◽

Artificial Neural Networks ◽

Computational Models ◽

Animal Species ◽

Experimental Techniques ◽

Motor Patterns ◽

Locomotor Behaviour ◽

Neurophysiological Studies ◽

Artificial Neural ◽

Different Levels

Modelling the control of locomotor movements can take place at many different levels and represent gaits of different animal species. In many cases, these models attempt to capture the theoretical constructs for generating rhythmical motor patterns gained from neurophysiological studies. This chapter examines the use of artificial neural networks to gain insights into the control of walking movements. Two models discussed simplify the pathways and structures responsible for forming these fundamental cyclical movements, and capture the global transformations between intended goals and action. The use of computational models permits researchers to address certain questions that cannot be empirically tested using current experimental techniques.

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EVOLUTIONARY ARTIFICIAL NEURAL NETWORKS

International Journal of Neural Systems ◽

10.1142/s0129065793000171 ◽

1993 ◽

Vol 04 (03) ◽

pp. 203-222 ◽

Cited By ~ 160

Author(s):

XIN YAO

Keyword(s):

Neural Networks ◽

Genetic Algorithms ◽

Artificial Neural Networks ◽

Vital Role ◽

Evolutionary Search ◽

Early Stages ◽

Learning Rules ◽

Artificial Neural ◽

Different Levels

Evolutionary artificial neural networks (EANNs) can be considered as a combination of artificial neural networks (ANNs) and evolutionary search procedures such as genetic algorithms (GAs). This paper distinguishes among three levels of evolution in EANNs, i.e. the evolution of connection weights, architectures and learning rules. It first reviews each kind of evolution in detail and then analyses major issues related to each kind of evolution. It is shown in the paper that although there is a lot of work on the evolution of connection weights and architectures, research on the evolution of learning rules is still in its early stages. Interactions among different levels of evolution are far from being understood. It is argued in the paper that the evolution of learning rules and its interactions with other levels of evolution play a vital role in EANNs.

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