High-efficiency phenotyping for vitamin A in banana using artificial neural networks and colorimetric data

Aquino, César Fernandes; Salomão, Luiz Carlos Chamhum; Azevedo, Alcinei Místico

doi:10.1590/1678-4499.467

Cited by 7 publications

(5 citation statements)

References 20 publications

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“…According to Soares et al (2015), the best relationship between the number of training samples and the number of intermediate connections must be considered in the selection of the prediction model; and the latter should be higher than 2 to reach the least average relative error of validation. Similar prediction studies confirm that the addition of neurons per layer does not always favor the performance of the model (Soares et al, 2014;Azevedo et al, 2015, Aquino et al, 2016a. According to Silva et al (2010), the continuous addition of neurons in the network, in the training phase, allows memorization of the studied data, but does not identify the probable associations between the data inserted in the input and output layers -a technical condition called overfitting.…”

Section: Resultsmentioning

confidence: 80%

“…Gianola et al (2011) stated that good results obtained by artificial neural networks could be explained by the adequate adjustment to the nonlinear systems. Moreover, according to Aquino et al (2016a), the ANN consider numerous explanatory variables concomitantly in the model that does not always present good results by other statistical models, such as multiple linear regression. Several researchers also confirmed the efficiency of artificial intelligence found in this study (Azevedo et al, 2015;Brasileiro et al, 2015;Soares et al, 2015; Aquino et al, 2016a, 2016b).…”

Section: Resultsmentioning

confidence: 99%

“…The artificial neural networks (ANN) is a successful tool to describe, substantiate, and elucidate high-complexity issues in the field of modeling Azevedo et al, 2015;Brasileiro et al, 2015;Soares et al, 2015;Aquino et al, 2016aAquino et al, , 2016bAzevedo et al, 2017). Thus, the use of ANN in agronomic modeling for the cactus pear crop can be efficient for predicting yield.…”

Section: Introductionmentioning

confidence: 99%

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Prediction of ‘Gigante’ cactus pear yield by morphological characters and artificial neural networks

Guimarães

Donato

Azevedo

et al. 2018

Rev. bras. eng. agríc. ambient.

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Estimating cactus pear yield is important for the planning of small and medium rural producers, especially in environments with adverse climatic conditions, such as the Brazilian semi-arid region. The objective of this study was to evaluate the potential of artificial neural networks (ANN) for predicting yield of ‘Gigante’ cactus pear, and determine the most important morphological characters for this prediction. The experiment was conducted in the Instituto Federal Baiano, Guanambi campus, Bahia, Brazil, in 2009 to 2011. The area used is located at 14° 13’ 30” S and 42° 46’ 53” W, and its altitude is 525 m. Six vegetative agronomic characters were evaluated in 500 plants in the third production cycle. The data were subjected to ANN analysis using the R software. Ten network architectures were trained 100 times to select the one with the lowest mean square error for the validation data. The networks with five neurons in the middle layer presented the best results. Neural networks with coefficient of determination (R2) of 0.87 were adjusted for sample validation, assuring the generalization potential of the model. The morphological characters with the highest relative contribution to yield estimate were total cladode area, plant height, cladode thickness and cladode length, but all characters were important for predicting the cactus pear yield. Therefore, predicting the production of cactus pear with high precision using ANN and morphological characters is possible.

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Section: Resultsmentioning

confidence: 80%

Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Prediction of ‘Gigante’ cactus pear yield by morphological characters and artificial neural networks

Guimarães

Donato

Azevedo

et al. 2018

Rev. bras. eng. agríc. ambient.

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“…Chromatographic methods [high performance liquid chromatography (HPLC) and reverse phase-HPLC] have found wide application for detailed quantitative analysis in bananas (Englberger et al, 2006b;2010;Davey et al, 2009a), while colorimetric and spectrophotometric methods have been applied for the rapid assessment of carotenoids (Amorim et al, 2009), sometimes prior to quantitative analysis (Borges et al, 2014). Several studies on bananas confirmed that pVAC contents show a positive correlation with fruit pulp color (Englberger et al, 2006b;Ngoh-Newilah et al, 2008;Amorim et al, 2009;Aquino et al, 2016). While color assessment and spectrophotometry may be inappropriate for precise calculation of pVACs, they are useful for initial screening of large sets of germplasm for breeding purposes.…”

Section: Sampling and Analysis To Support Provitamin A Enhancement Anmentioning

confidence: 99%

Recent advances in banana (musa spp.) biofortification to alleviate vitamin A deficiency

Amah

Biljon

Brown

et al. 2018

Critical Reviews in Food Science and Nutrition

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Vitamin A deficiency (VAD) is one of the most prevalent micronutrient deficiencies that disproportionately affects low income populations in developing countries. Traditional breeding and modern biotechnology have significant potential to enhance micronutrient bioavailability in crops through biofortification. Bananas (Musa spp.) are economically important fruit crops grown throughout tropical and sub-tropical regions of the world where VAD is most prevalent. Some banana genotypes are rich in provitamin A carotenoids (pVACs), providing an opportunity to use bananas as a readily available vehicle for provitamin A delivery. This review summarizes the progress made in carotenoid research in bananas relative to banana diversity and the use of conventional breeding and transgenic approaches aimed at banana biofortification to address vitamin A deficiency. Existing reports on sampling strategies, pVAC retention and bioavailability are also evaluated as essential components for a successful banana biofortification effort. The wide variability of pVACs reported in banana cultivars coupled with recent advances in unraveling the diversity and genetic improvement of this globally important but often-neglected staple fruit crop underscores their importance in biofortification schemes.

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“…The ANNs are architected in three structures, in which the predictor variables make up the first layer; the hidden layer relates the number of neurons to be scaled; in sequence, the output layer receives stimuli from the hidden layer and constructs the pattern that will be the response. With many applications, the use of ANNs has intensified in agricultural modeling (Aquino et al, 2016;Azevedo et al, 2017).…”

Section: Introductionmentioning

confidence: 99%

Comparison of Methods for Harvest Prediction in ‘Gigante’ Cactus Pear

Guimarães¹,

Donato²,

Aspiazú³

et al. 2019

JAS

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Behavior analysis and plant expression are the answers the researcher needs to construct predictive models that minimize the effects of the uncertainties of field production. The objective of this study was to compare the simple and multiple linear regression methods and the artificial neural networks to allow the maximum security in the prediction of harvest in ‘Gigante’ cactus pear. The uniformity test was conducted at the Federal Institute of Bahia, Campus Guanambi, Bahia, Brazil, coordinates 14°13′30″ S, 42°46′53″ W and altitude of 525 m. At 930 days after planting, we evaluated 384 basic units, in which were measured the following variables: plant height (PH); cladode length (CL), width (CW) and thickness (CT); cladode number (CN); total cladode area (TCA); cladode area (CA) and cladode yield (Y). For the comparison between the artificial neural networks (ANN) and regression models (single and multiple-SLR and MLR), we considered the mean prediction error (MPE), the mean quadratic error (MQE), the mean square of deviation (MSD) and the coefficient of determination (R2).The values estimated by the ANN 7-5-1 showed the best proximity to the data obtained in field conditions, followed by ANN 6-2-1, MLR (TCA and CT), SLR (TCA) and SLR (CN). In this way, the ANN models with the topologies 7-2-1 and 6-2-1, MLR with the variables total cladode area and cladode thickness and SLR with the isolated descriptors total cladode area and cladode number, explain 85.1; 81.5; 76.3; 74.09 and 65.87%, respectively, of the yield variation. The ANNs were more efficient at predicting the yield of the ‘Gigante’ cactus pear when compared to the simple and multiple linear regression models.

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High-efficiency phenotyping for vitamin A in banana using artificial neural networks and colorimetric data

Abstract: For each sample, colorimetric data were obtained and the vitamin A content was estimated in the ripe banana pulp. For the prediction of

Cited by 7 publications

References 20 publications

Prediction of ‘Gigante’ cactus pear yield by morphological characters and artificial neural networks

Prediction of ‘Gigante’ cactus pear yield by morphological characters and artificial neural networks

Recent advances in banana (musa spp.) biofortification to alleviate vitamin A deficiency

Comparison of Methods for Harvest Prediction in ‘Gigante’ Cactus Pear

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