Corn yield prediction with artificial neural network trained using airborne remote sensing and topographic data

Author

Serele, C.Z. ; Gwyn, Q.H.J. ; Boisvert, Johanne B. ; Pattey, Elizabeth ; McLaughlin, Neil ; Daoust, Gilles

Author_Institution

Sherbrooke Univ., Que., Canada

Volume

1

fYear

2000

fDate

2000

Firstpage

384

Abstract

Artificial neural networks (ANN) are widely used as continuous models to fit nonlinear transfer functions. The objective of the present work was to develop ANN models to predict corn yield from topographic features, vegetation and texture indices. The proposed ANN is back-propagation neural network (BPN) trained by conjugate gradient algorithm. The generalization ability of the best of four models was confirmed by a regression coefficient higher than 90% and a RMSE of 0.365 t/ha, between predicted and observed corn yield

Keywords

agriculture; backpropagation; geophysical signal processing; geophysical techniques; geophysics computing; image texture; neural nets; remote sensing; vegetation mapping; Zea; agriculture; airborne remote sensing; artificial neural network; backpropagation; conjugate gradient algorithm; corn; crop; crops; geophysical measurement technique; image texture; maize; neural net; nonlinear transfer function; topographic data; trained; vegetation mapping; yield prediction; Agriculture; Artificial neural networks; Crops; Input variables; Mathematical model; Monitoring; Predictive models; Remote sensing; Vegetation; Yield estimation;

fLanguage

English

Publisher

ieee

Conference_Titel

Geoscience and Remote Sensing Symposium, 2000. Proceedings. IGARSS 2000. IEEE 2000 International

Conference_Location

Honolulu, HI

Print_ISBN

0-7803-6359-0

Type

conf

DOI

10.1109/IGARSS.2000.860527

Filename

860527