Prediction of kiwifruit firmness using fruit mineral nutrient concentration by artificial neural network (ANN) and multiple linear regressions (MLR)

‎Torkashvand, Ali Mohammadi; Ahmadi, Abbas; Nikravesh, Niloofar Layegh

doi:10.1016/s2095-3119(16)61546-0

Cited by 93 publications

(55 citation statements)

References 30 publications

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“…Such results show the suitability of use of neural networks for yield prediction in lima bean. Niazian et al (2018), using metrics similar to those presented here, observed the efficiency of the ANN tool compared to the MLR model to predict seed yield of ajowan, and Torkashvand et al (2017) observed the superiority of MLR models over ANNs using the RMSE and correlation measures applied to observed and predicted data. Niedbała et al (2019) used the MAE and MAPE measures to evaluate the relative efficiency of three artificial neural network architectures.…”

Section: Resultssupporting

confidence: 58%

See 1 more Smart Citation

Prediction of grain yield, adaptability, and stability in landrace varieties of lima bean (Phaseolus lunatus L.)

Sousa

Silva

Lopes

et al. 2020

Crop Breed. Appl. Biotechnol.

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The aim of this study was to compare the Multiple Linear Regression and Artificial Neural Network models in prediction of grain yield of ten landrace varieties of lima bean and evaluate adaptability and stability through the Lin and Binns method for identification of the best performing variety. Trials were conducted in the municipalities of Teresina, PI, and São Domingos do Maranhão, MA, through measurement of 12 traits, except for grain yield in São Domingos do Maranhão. The parameters of Pearson and Spearman correlation, root mean square error, mean absolute error, and coefficient of determination were used to compare the models. The Artificial Neural Network proved to be more adequate for prediction of grain yield. Adaptability and stability analyses indicated that the environments are discriminant for selection of promising genotypes, and that the landrace variety Mulatinha can be recommended for planting in the municipalities.

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Section: Resultssupporting

confidence: 58%

“…In recent years, artificial intelligence has repeatedly been used to predict the phenotypic expression of agronomic traits in economically important species, or even in species with high economic potential. Torkashvand et al (2017) used Multilayer perceptrons (MLPs) to predict fruit firmness in kiwi varieties AMCB Sousa et al…”

Section: Introductionmentioning

confidence: 99%

Prediction of grain yield, adaptability, and stability in landrace varieties of lima bean (Phaseolus lunatus L.)

Sousa

Silva

Lopes

et al. 2020

Crop Breed. Appl. Biotechnol.

View full text Add to dashboard Cite

show abstract

“…Considering that firmness is one of the most important properties of fresh fruits, and is highly connected with shelf-life, Torkashvand et al [2] used ANN models to develop predictive methods for fruit firmness of kiwis. Their results seemed to indicate that the ANN model showed better potential in determining the relationship between firmness and nutrients concentration, when compared to the predictive capacity of multiple linear regression.…”

Section: B Texture and Rheologymentioning

confidence: 99%

“…ANNs correspond to computational systems that aim to imitate some properties of biological neurons. Methods based on ANNs imitate the natural neural system via computer programs being fairly popular due to several advantages such as, for example: nonlinearity, adaptation, generalization, model independence, easy to use and high accuracy [1], [2]. The first artificial model of a biological neuron was obtained in 1943 by the pioneering work of Warren McCulloch (psychiatrist and neuroanatomist) and Walter Pitts (mathematician).…”

Section: Introductionmentioning

confidence: 99%

The Use of Artificial Neural Networks (ANN) in Food Process Engineering

Guiné¹,

Dets²

2019

ijfe

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 -Artificial neural networks (ANN) aim to solve problems of artificial intelligence, by building a system with links that simulate the human brain. This approach includes the learning process by trial and error. The ANN is a system of neurons connected by synaptic connections and divided into incoming neurons, which receive stimulus from the external environment, internal or hidden neurons and output neurons, that communicate with the outside of the system. The ANNs present many advantages, such as good adaptability characteristics, possibility of generalization and high noise tolerance, among others. Neural networks have been successfully used in various areas, for example, business, finance, medicine, and industry, mainly in problems of classification, prediction, pattern recognition and control. In the food industry, food processing, food engineering, food properties or quality control, statistical tools are frequently present, and ANNs can process more efficiently data comprising multiple input and output variables. The objective of this review was to highlight the application of ANN to food processing, and evaluate its range of use and adaptability to different food systems. For that a systematic review was undertaken from the scientific literature and the selection of the information was based on inclusion criteria defined. The results indicated that ANN is widely used for modelling and prediction in food systems, showing good accuracy and applicability to a wide range of situations and processes in food engineering.

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“…To address this issue, much attention has recently been focused on the nonparametric-based methods in the degradation prognostic of rotating machinery. For instance, artificial neural network (ANN) [17][18][19][20], fuzzy logic [21], and deep learning network (DPN) [22,23], etc., could be considered as successful nonparametric-based approaches in the PHM field. The benefit of using nonparametric-based methods lies in their ability to model the evolution of complex multi-dimensional degradation data, which can effectively extract latent features such as the spatio-temporal correlations (STC) among historical data.…”

Section: Of 27mentioning

confidence: 99%

Intelligent Prognostics of Degradation Trajectories for Rotating Machinery Based on Asymmetric Penalty Sparse Decomposition Model

Liang

2018

Symmetry

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The ability to accurately track the degradation trajectories of rotating machinery components is arguably one of the challenging problems in prognostics and health management (PHM). In this paper, an intelligent prediction approach based on asymmetric penalty sparse decomposition (APSD) algorithm combined with wavelet neural network (WNN) and autoregressive moving average-recursive least squares algorithm (ARMA-RLS) is proposed for degradation prognostics of rotating machinery, taking the accelerated life test of rolling bearings as an example. Specifically, the health indicators time series (e.g., peak-to-peak value and Kurtosis) is firstly decomposed into low frequency component (LFC) and high frequency component (HFC) using the APSD algorithm; meanwhile, the resulting non-convex regularization problem can be efficiently solved using the majorization-minimization (MM) method. In particular, the HFC part corresponds to the stable change around the zero line of health indicators which most extensively occurs; in contrast, the LFC part is essentially related to the evolutionary trend of health indicators. Furthermore, the nonparametric-based method, i.e., WNN, and parametric-based method, i.e., ARMA-RLS, are respectively introduced to predict the LFC and HFC that focus on abrupt degradation regions (e.g., last 100 points). Lastly, the final predicted data could be correspondingly obtained by integrating the predicted LFC and predicted HFC. The proposed methodology is tested using degradation health indicator time series from four rolling bearings. The proposed approach performed favorably when compared to some state-of-the-art benchmarks such as WNN and largest Lyapunov (LLyap) methods.

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Prediction of kiwifruit firmness using fruit mineral nutrient concentration by artificial neural network (ANN) and multiple linear regressions (MLR)

Cited by 93 publications

References 30 publications

Prediction of grain yield, adaptability, and stability in landrace varieties of lima bean (Phaseolus lunatus L.)

Prediction of grain yield, adaptability, and stability in landrace varieties of lima bean (Phaseolus lunatus L.)

The Use of Artificial Neural Networks (ANN) in Food Process Engineering

Intelligent Prognostics of Degradation Trajectories for Rotating Machinery Based on Asymmetric Penalty Sparse Decomposition Model

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