Artificial Neural Networks for estimating the biometric variables of seedlings from forest species produced in different substrates
DOI:
https://doi.org/10.5965/223811711812019047Keywords:
recovery of degraded areas, artificial intelligence, organic substratesAbstract
The aim of this study was to evaluate the stem growth in diameter and height as well as the production of total dry matter from seedlings of Myracrodruon urundeuva, Jacaranda brasiliana and Mimosa caesalpiniaefolia. Concurrently, an Artificial Neural Network (RNA) of Multilayer Perceptron type that would be able to estimate the H and the MST of the seedlings of the studied species was developed. The seedlings were cultivated in a protected environment with 50% shade. Thus, the treatments were considered with five proportions of the organic material (0, 20, 40, 60 and 80% v/v) in the final substrate composition (desertified area soil). At 120 days after sowing, the seedlings were collected to determine the biometric variables. The MLP network was used with help of the Levenberg-Marquardat training algorithm. The variables used as input of the MLP for height and dry mass estimation of the seedlings were: stem diameter, minimum, medium and maximum diameter of stem; and species and sources of organic residues (cattle manure, goat manure and rice straw), totaling ten entries. The hyperbolic tangent activation function was conducted. As a result, a 80:20% ratio (bovine manure and/or goat manure: soil from the degraded area) is recommended to be used in the growing substrate for seedling growth. The addition of bovine manure and goat manure doses influenced the Jacaranda brasiliana DC, with the linear effect increasing with the estimated value of 2.66 mm plant-1. For H, the addition of bovine and goat manure influenced the growth of Myracrodruon urundeuva seedlings. The MST production of seedlings from the three species was also distributed as a function of the increasing proportions of organic residues incorporated into the culture substrate. The use of the Artificial Neural Network of Multilayer Perceptron type was efficient for the estimation of the height and total dry mass of the species studied.
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