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Research on satellite imagery aims to advance sustainable agriculture
Date:4/1/2011

methods to discriminate differences in reflectance between the crops and bare soil: stepwise discriminant analysis and two artificial neural networks (multilayer perceptron and radial basis function).

"The results of the three classification methods showed that the highest percentage of accuracy was achieved with multilayer perceptron (MLP), followed by stepwise discriminant analysis and radial basis function (RBF)", said Lpez-Granados. The classification matrix from the MLP model using cross-validation showed that most crops discriminated in spring and late summer were 100% classifiable; the best overall classification was obtained in late summer, when fewer crop species were available.

"Our results show that the MLP model identified and recognized the differences between spectral signatures of bare soil and the most important horticultural irrigated herbaceous and tree crops at spring and late summer. They also indicate that the MLP neural network model should be considered for a successful classification of remote sensed data," concluded Lpez-Granados. "For future research, we recommend acquiring two multispectral satellite images taken in spring and late summer for monitoring and mapping these irrigated crops, thus avoiding costly field surveys".


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Contact: Michael W. Neff
mwneff@ashs.org
703-836-4606
American Society for Horticultural Science
Source:Eurekalert

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