Izvorni znanstveni članak
https://doi.org/10.5552/crojfe.2020.611
A Three-Step Neural Network Artificial Intelligence Modeling Approach for Time, Productivity and Costs Prediction: A Case Study in Italian Forestry
Andrea Rosario Proto
; Mediterranean University of Reggio Calabria Department of AGRARIA Feo di Vito 89122 Reggio Calabria (RC) ITALY
Giulio Sperandio
; Consiglio per la ricerca in agricoltura e l’analisi dell’economia agraria (CREA) Centro di ricerca Ingegneria e Trasformazioni agroalimentari Via della Pascolare 16 00015 Monterotondo (Rome) ITALY
Corrado Costa
; Consiglio per la ricerca in agricoltura e l’analisi dell’economia agraria (CREA) Centro di ricerca Ingegneria e Trasformazioni agroalimentari Via della Pascolare 16 00015 Monterotondo (Rome) ITALY
Mauro Maesano
; National Research Council of Italy Institute for Agricultural and Forest Systems in the Mediterranean and University of Tuscia Department of Innovation in Biological, Agro-food and Forest Systems Via San Camillo De Lellis 01100 Viterbo ITALY
Francesca Antonucci
; Consiglio per la ricerca in agricoltura e l’analisi dell’economia agraria (CREA) Centro di ricerca Ingegneria e Trasformazioni agroalimentari Via della Pascolare 16 00015 Monterotondo (Rome) ITALY
Giorgio Macrì
; Mediterranean University of Reggio Calabria Department of AGRARIA Feo di Vito 89122 Reggio Calabria (RC) ITALY
Giuseppe Scarascia Mugnozza
; University of Tuscia Department of Innovation in Biological, Agro-food and Forest Systems Via San Camillo De Lellis 01100 Viterbo ITALY
Giuseppe Zimbalatti
; Mediterranean University of Reggio Calabria Department of AGRARIA Feo di Vito 89122 Reggio Calabria (RC) ITALY
Sažetak
The improvement of harvesting methodologies plays an important role in the optimization of wood production in a context of sustainable forest management. Different harvesting methods can be applied according to forest site-specific condition and the appropriate mechanization level depends on a number of factors. Therefore, efficiency and functionality of wood harvesting operations depend on several factors. The aim of this study is to analyze how the different harvesting processes affect operational costs and labor productivity in typical small-scale Italian harvesting companies. A multiple linear regression model (MLR) and artificial neural network (ANN) have been carried out to predict gross time, productivity and costs estimation in a series of qualitative and quantitative variables. The results have created a correct statistical model able to accurately estimate the technical parameters (work time and productivity) and economic parameters (costs per unit of product and per hectare) useful to the forestry entrepreneur to predict the results of the work in advance, considering only the values detectable of some characteristic elements of the worksite.
Ključne riječi
ANN; AI; mechanization; accuracy; multivariate statistics; harvesting
Hrčak ID:
233577
URI
Datum izdavanja:
31.1.2020.
Posjeta: 1.395 *