Evaluation of neural network techniques in predicting and minimizing the mass of soil wastes in a sugar-beet harvesting season

Author

Tummarello, G. ; Riva, G. ; Toscano, G. ; Piazza, F.

Author_Institution

Area of Agric. Eng., Univ. of Ancona, Italy

Volume

1

fYear

2002

fDate

6/24/1905 12:00:00 AM

Firstpage

427

Lastpage

431

Abstract

The unwanted soil collected during the harvesting of the sugar beets represents one of the main by-products of the sugar industry in Europe. In this study, we adopt different neural network predictors to be used as a base for an online harvesting optimization scheme. Our aim is to demonstrate the feasibility of a combinatory optimization of the harvesting schedule based on neural net predictions and a priori statistics in order to minimize the global value of soil tare for the season. Simulated results show the approach described has significant economical and ecological advantages

Keywords

agriculture; forecasting theory; neural nets; optimisation; soil; time series; FIR filter; agriculture; neural network predictions; quantization; soil tare; soil waste reduction; sugar-beet harvesting; time series; Biological system modeling; Cleaning; Costs; Economic forecasting; Europe; Intelligent networks; Neural networks; Soil; Sugar industry; Sugar refining;

fLanguage

English

Publisher

ieee

Conference_Titel

Neural Networks, 2002. IJCNN '02. Proceedings of the 2002 International Joint Conference on

Conference_Location

Honolulu, HI

ISSN

1098-7576

Print_ISBN

0-7803-7278-6

Type

conf

DOI

10.1109/IJCNN.2002.1005510

Filename

1005510