Abstract
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.
Highlights
The Brazilian semi-arid region, circumscribed in the Caatinga biome, presents severe limits to plant production, mainly due to the low contents of available water in the soil for the plants (Albuquerque et al, 2018)
The objective of this study was to compare simple and multiple linear regression models and artificial neural networks to allow the maximum security in harvest prediction of ‘Gigante’ cactus pear
The study was developed in the experimental field of the Baiano Federal Institute-IFBAIANO, Campus Guanambi, Bahia, Brazil, in a predominantly flat area, with soil classified as a Litolic Neosol, with the coordinates 14°13′30′′ S, 42°46′5′′ W and altitude of 525 m, with rainfall and average annual temperature of 670.2 mm and 25.9 °C, respectively
Summary
The Brazilian semi-arid region, circumscribed in the Caatinga biome, presents severe limits to plant production, mainly due to the low contents of available water in the soil for the plants (Albuquerque et al, 2018). Even in this environment, unfavorable to plant growth and development, the cactus pear has emerged as a strategic resource in ruminant feeding (Ochoa et al, 2018) with significant levels of biomass production (Padilha Junior et al, 2016). There are studies relating morphometric, morphogenic and production components in different cactus pear species (Padilha Junior et al, 2016) or even studies approaching morphological and their reflexes on yield (Silva et al, 2014), general literature lacks information that allows a comparison between the predictive tools related to cladode production in a practical and precise order, with direct application in the field
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