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

Guimarães, Bruno Vinícius Castro; Donato, Sérgio Luíz Rodrigues; Azevedo, Alcinei Místico; Aspiazú, Ignácio; Silva, Ancilon A. e

doi:10.1590/1807-1929/agriambi.v22n5p315-319

Cited by 16 publications

(26 citation statements)

References 13 publications

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“…Lucena et al (2018b) observed that, independent of the cladode order, the area of Elephant-Mexican clone (O. stricta) presented a positive correlation (r) with the product of length by width with a magnitude of 0.90. Guimarães, Donato, Azevedo, Aspiazú, and Silva Jr. (2018) also verified a positive correlation of weight with the cladode area of 0.86 in O. ficus-indica. Evaluating the relationship between the RCA and LW of the primary cladodes verified that the linear regression model presented an explanatory power (R²) of 98.55%, while the power model had an R 2 of 99.97%, and the gamma model had an explanatory power of 99.52% (Table 4) (Table 4).…”

Section: Resultssupporting

confidence: 61%

Estimating the area and weight of cactus forage cladodes using linear dimensions

et al. 2020

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The forage palm is one of the main forages of ruminants in semiarid regions. Measurements of leaf area are required in agronomic studies because they are one of the main parameters used to evaluate plant growth. The objective of this study was to validate and define the best models for estimating the area and weight of Giant Sweet clone (Nopalea cochenillifera) forage cladodes in a non-destructive way based on the linear dimensions of length, width and thickness. There were 432 randomly measured cladodes at 550 days after planting. The length, width and thickness of each cladode were measured using a digital calliper. The cladodes were weighed individually. The cladode area was calculated by the gravimetric method. The power regression model was the most efficient method to explain the cladode area as a function of the product of length by width, while the gamma model was the most efficient method to explain the weight of cladodes as a function of the product of length by width and thickness. The power model, and gamma model, = 0.536T + 0.028LW, were used to determine the area and weight of Nopalea cochenillifera Giant Sweet clone cladodes, respectively, based on the values of linear dimensions measured independently of the order of the cladode.

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Section: Resultssupporting

confidence: 61%

Estimating the area and weight of cactus forage cladodes using linear dimensions

et al. 2020

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“…Correlations between morphometric characteristics of forage cactus cladodes are known to be positively correlated, a fact that can be found in the reports of Cunha et al (2012) Little Sweet clone, Lucena et al (2019a) in N. cochenillifera Giant Sweet clone (r > 0.95), Lucena et al (2018a) in Opuntia stricta ( = 0.90), Reis, Gazarini, Fonseca, and Ribeiro (2018), ( = 0.95) in Opuntia ficus-indica correlating real cladode area with product of length by width and Guimarães, Donato, Azevedo, Aspiazú, and Silva Junior (2018) in Opuntia ficus-indica verified a positive correlation of weight with the cladode area of 0.86. Fisher's discriminant function was defined by Equation 8:…”

Section: Resultsmentioning

confidence: 68%

Characterization of Nopalea cochenillifera clones using linear dimensions and multivariate analysis

Lucena¹,

Leite²,

Simões³

et al. 2020

Acta Sci Technol

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Forage cacti can contribute to increases in biomass yields in agricultural areas, improving the use efficiency of local natural resources. Forage cactus stands out as a forage alternative in semi-arid regions due to its high potential of phytomass production and energy value, large water reserve and easy propagation. The objective of this study was discriminate as morphometric characteristics of Nopalea cochenillifera cladodes in relation to Little and Giant Sweet clones. This research was conducted from March 2016 to July 2019. The design used was randomized blocks with five replicates using the forage cactus clones Little and Giant Sweet of Nopalea cochenillifera (L.) Salm-Dyck. The experimental unit was an area of 126.0 m2 (12.6 x 10.0 m). Study sample is composed of 1018 cladodes (581 of Little Sweet clone) and (437 of Giant Sweet clone), randomly collected. Variables evaluated were the cladode length, width, thickness, area weight. T-tests, Pearson correlation coefficients, discriminant analysis and canonical variables analysis were used to evaluate, compare and discriminate the morphometric characteristics of forage cactus clones. The Giant Sweet clone presented the highest means for the variables length, width, area and weight. Fisher's discriminant function verified a 99.41% hit rate to differentiate groups of forage cactus clone. The hit rate for Little Sweet clone was of 98.97%, while for Giant Sweet clone was of 100.00%. With two canonical variables the explanation rate of the morphometric characteristics for the behavior of forage cactus clones is higher than 90%. Morphometric characteristics of cladodes can be used as parameters that help in the identification of Nopalea cochenillifera clones with high discriminatory power.

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“…The development of high-efficiency models obtained from the original field conditions favors safety when predicting 'Gigante' cactus pear yield (Guimarães et al, 2018), which contributes to the success of rural planning, especially as a support to the producer who needs to define in advance the quantity of animals to be fed with the produced biomass or even the volume to be marketed (Marques et al, 2017). models predictive models with superior performance compared to validated conventional tools (Arruda et al, 2013;Leal et al, 2015;Kaytez et al, 2015).…”

Section: Resultsmentioning

confidence: 99%

“…For Amania et al (2019), this productive potential is associated with several mechanisms of adaptation of the crop to the adverse conditions in which the species is normally conducted. construct predictive models that minimize the effects of the productive uncertainties or the risk associated with the activity in the field (Guimarães et al, 2018).…”

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

Cited by 16 publications

References 13 publications

Estimating the area and weight of cactus forage cladodes using linear dimensions

Estimating the area and weight of cactus forage cladodes using linear dimensions

Characterization of Nopalea cochenillifera clones using linear dimensions and multivariate analysis

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

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