Support vector regression for rice age estimation using satellite imagery

Srestasathiern, Panu; Lawawirojwong, Siam; Suwantong, Rata

doi:10.1109/ecticon.2016.7561335

Cited by 3 publications

(7 citation statements)

References 11 publications

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“…The second kind of ML model was a SVM model that is based on statistical learning theory (Gu, Zhou, Yu & Shen, 2018) that can be used for regression or pattern recognition (Fan, Wu, Ma, Zhou & Zhang, 2020, Gu, Zhou, Yu & Shen, 2018, among others (Gu, Zhou, Yu & Shen, 2018). This kind of models can be used in different research fields such as: i) In Engineering to fault diagnosis of rolling bearings (Gu, Zhou, Yu & Shen, 2018), ii) in Agricultural and Biological Sciences to estimate the rice age using satellite imagery (Srestasathiern, Lawawirojwong & Suwantong, 2016), iii) in Food technology to try to determine hardness and sugariness of melons (Sun, Zhang, Liu & Wang, 2017) or to classify different types of rice (Lu, Deng, Zhu & Tian, 2015), inter alia.…”

Section: Introductionmentioning

confidence: 99%

Stability assessment of extracts obtained from Arbutus unedo L. fruits in powder and solution systems using machine-learning methodologies

et al. 2020

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Arbutus unedo L. (strawberry tree) has showed considerable content in phenolic compounds, especially flavan-3ols (catechin, gallocatechin, among others). The interest of flavan-3-ols has increased due their bioactive actions, namely antioxidant and antimicrobial activities, and by association of their consumption to diverse health benefits including the prevention of obesity, cardiovascular diseases or cancer. These compounds, mainly catechin, have been showed potential for use as natural preservative in foodstuffs; however, their degradation is increased by pH and temperature of processing and storage, which can limit their use by food industry. To model the degradation kinetics of these compounds under different conditions of storage, three kinds of machine learning models were developed: i) random forest, ii) support vector machine and iii) artificial neural network. The selected models can be used to track the kinetics of the different compounds and properties under study without the prior knowledge requirement of the reaction system.

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Section: Introductionmentioning

confidence: 99%

Stability assessment of extracts obtained from Arbutus unedo L. fruits in powder and solution systems using machine-learning methodologies

et al. 2020

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“…SVM models present an advantage in comparison with other methods, for example, partial least square-discriminant analysis, to model classification of nonlinear problems [39]. As a result of this advantage, the SVM can be applied in different research areas such as agricultural sciences [38,40], medicine [41,42], or Economics [43,44], inter alia.…”

Section: Introductionmentioning

confidence: 99%

“…(iii) SVM was proposed in 1992 by Boser et al [37] for classification problem [38]. SVM is a supervised learning method [38,39] that can construct a hyperplane to separate data into many classes [38,39], even a group of hyperplanes, which can be used for different tasks such as regression or classification [18,39].…”

Section: Introductionmentioning

confidence: 99%

“…ANNs are able to find complex relationships between input and output variables [30]. Owing to these advantages, ANNs can be used in different areas, such as environmental sciences [26,27], energy fields [28,32], food technology [30,33,34], or chemistry [35,36], inter alia . (iii) SVM was proposed in 1992 by Boser et al [37] for classification problem [38]. SVM is a supervised learning method [38,39] that can construct a hyperplane to separate data into many classes [38,39], even a group of hyperplanes, which can be used for different tasks such as regression or classification [18,39].…”

Section: Introductionmentioning

confidence: 99%

“…Owing to these advantages, ANNs can be used in different areas, such as environmental sciences [26,27], energy fields [28,32], food technology [30,33,34], or chemistry [35,36], inter alia . (iii) SVM was proposed in 1992 by Boser et al [37] for classification problem [38]. SVM is a supervised learning method [38,39] that can construct a hyperplane to separate data into many classes [38,39], even a group of hyperplanes, which can be used for different tasks such as regression or classification [18,39]. SVM models present an advantage in comparison with other methods, for example, partial least square‐discriminant analysis, to model classification of nonlinear problems [39].…”

Section: Introductionmentioning

confidence: 99%

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Random Forest, Artificial Neural Network, and Support Vector Machine Models for Honey Classification

Martinez-Castillo

Astray

Mejuto

et al. 2019

eFood

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Different separated protein fractions by the electrophoretic method in polyacrylamide gel were used to classify two different types of honeys, Galician honeys and commercial honeys produced and packaged outside of Galicia. Random forest, artificial neural network, and support vector machine models were tested to differentiate Galician honeys and other commercial honeys produced and packaged outside of Galicia. The results obtained for the best random forest model allowed us to determine the origin of honeys with an accuracy of 95.2%. The random forest model, and the other developed models, could be improved with the inclusion of new data from different commercial honeys.

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Modelling Polyphenol Extraction through Ultrasound-Assisted Extraction by Machine Learning in Olea europaea Leaves

Rodríguez-Fernández,

Fernández-Gómez,

Mejuto

et al. 2023

Foods

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The study of the phenolic compounds present in olive leaves (Olea europaea) is of great interest due to their health benefits. In this research, different machine learning algorithms such as RF, SVM, and ANN, with temperature, time, and volume as input variables, were developed to model the extract yield and the total phenolic content (TPC) from experimental data reported in the literature. In terms of extract yield, the neural network-based ANNZ-L model presents the lowest root mean square error (RMSE) value in the validation phase (9.44 mg/g DL), which corresponds with a mean absolute percentage error (MAPE) of 3.7%. On the other hand, the best model to determine the TPC value was the neural network-based model ANNR, with an RMSE of 0.89 mg GAE/g DL in the validation phase (MAPE of 2.9%). Both models obtain, for the test phase, MAPE values of 4.9 and 3.5%, respectively. This affirms that ANN models would be good modelling tools to determine the extract yield and TPC value of the ultrasound-assisted extraction (UAE) process of olive leaves under different temperatures, times, and solvents.

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Support vector regression for rice age estimation using satellite imagery

Cited by 3 publications

References 11 publications

Stability assessment of extracts obtained from Arbutus unedo L. fruits in powder and solution systems using machine-learning methodologies

Stability assessment of extracts obtained from Arbutus unedo L. fruits in powder and solution systems using machine-learning methodologies

Random Forest, Artificial Neural Network, and Support Vector Machine Models for Honey Classification

Modelling Polyphenol Extraction through Ultrasound-Assisted Extraction by Machine Learning in Olea europaea Leaves

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