Eficiência da estimação da área foliar de couve por meio de redes neurais artificiais

Azevedo, Alcinei Místico; Júnior, Valter Carvalho de Andrade; Júnior, Aderbal S Sousa; Santos, Albertir Aparecido dos; Cruz, Cosme Damião; Pereira, Sanderlei; Oliveira, Altino Jm

doi:10.1590/s0102-053620170103

Cited by 8 publications

(8 citation statements)

References 12 publications

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“…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%

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: Introductionmentioning

confidence: 99%

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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“…In kale the neural networks were used by Azevedo et al [11] in leaf area prediction. The technique proved to be feasible to estimate the leaf area and to assist in the selection of superior genotypes.…”

Section: Discussionmentioning

confidence: 99%

“…However, there is the possibility of carrying out these studies through computational intelligence using artificial neural networks (ANNs) [1,9]. The main advantages of RNAs are their non-parametric approach, tolerance to data loss, and the need for detailed information about the modeling system as a design and genealogies [10,11]. grouping and process characterization works.…”

Section: Introductionmentioning

confidence: 99%

Self-organized map: the new aproach for study of genetic divergence in kale

Brito

Júnior

Azevedo

et al. 2020

Preprint

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The objective of this study was to study the genetic divergence between genotypes of kale, to propose a methodology for the use of neural networks of the SOM type and to test its efficiency through Anderson's discriminant analysis. We evaluated 33 families of half-siblings of kale and three commercial cultivars. The design was a randomized block with four replications with six plants per plot. A total of 14 plant-level quantitative traits were evaluated. Genetic values were predicted at family level via REML / BLUP. For the study of divergence, neural networks of the SOM type (Selforganizing Map) were adopted. We evaluated different network architectures, whose consistencies of the clusters were identified by the Anderson discriminant analysis and by the number of empty clusters. After selecting the best network configuration, a dissimilarity matrix was obtained, from which a dendrogram was constructed using the UPGMA method. The best network architecture was formed with five rows and one column, totaling five neurons and consequently five clusters. The greatest dissimilarity was established between clusters I and V. The crossing between the genotypes of cluster I and those belonging to clusters III and V are the most recommended, since they aim to recombine families with characteristics of interest to the improvement and high dissimilarity. Anderson's discriminant analysis showed that the genotype classification was 100% correct, indicating the efficiency of the methodology used. Map (SOM) neural networks are a type of exploratory multivariate analysis tool that allows, through artificial computational intelligence, to design high-dimensional data in a smaller dimensional space, without loss of information [12]. This new organization prioritizes maintaining the structure, such as clusters and information relationships [13]. This reinforces its constant use in several

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“…In corn, Soares et al (2015) evaluated the performance of ANN in the prediction of yield based on morphological variables and evidenced a high predictive capacity due to the strong correlation between the estimated values and the real grain yield data obtained in field experiments. Similarly, Azevedo et al (2017) estimated kale leaf area with high efficiency for genotype selection in breeding programs. Soares et al (2014) developed a model to predict yield in 'Maçã' banana fruits using the 'BRS Tropical' (AAAB) hybrid by ANN and regression equations, and Guimarães et al (2013) for 'Prata' bananas (AAB and AAAB) using regression equations.…”

Section: Introductionmentioning

confidence: 99%

Yield prediction of ‘Prata Anã’ and ‘BRS Platina’ banana plants by artificial neural networks1

Guimarães

Donato

Aspiazú

et al. 2021

Pesqui. Agropecu. Trop.

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Prediction models may contribute to data analysis and decision-making in the management of a crop. This study aimed to evaluate the feasibility of predicting the yield of ‘Prata-Anã’ and ‘BRS Platina’ banana plants by means of artificial neural networks, as well as to determine the most important morphological descriptors for this purpose. The following characteristics were measured: plant height; perimeter of the pseudostem at the ground level, at 30 cm and 100 cm; number of live leaves at harvest; stalk mass, length and diameter; number of hands and fruits; bunches and hands masses; hands average mass; and ratio between the stalk and bunch masses. The data were submitted to artificial neural networks analysis using the R software. The best adjustments were obtained with two and three neurons at the intermediate layer, respectively for ‘Prata-Anã’ and ‘BRS Platina’. These models presented the lowest mean square errors, which correspond to the higher proximity between the predicted and the real data, and, therefore, a higher efficiency of the networks in the yield prediction. By the coefficient of determination, the best adjustments were found for ‘Prata-Anã’ (R² = 0.99 for all the network compositions), while, for ‘BRS Platina’, the data adjustment enabled an R² with values between 0.97 and 1.00, approximately. Yield predictions for ‘Prata-Anã’ and ‘BRS Platina’ were obtained with high efficiency by using artificial neural networks.

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Eficiência da estimação da área foliar de couve por meio de redes neurais artificiais

Cited by 8 publications

References 12 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

Self-organized map: the new aproach for study of genetic divergence in kale

Yield prediction of ‘Prata Anã’ and ‘BRS Platina’ banana plants by artificial neural networks1

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