A weed species spectral detector based on neural networks

被引：17

作者：

Moshou D. ^{[1
]}

Ramon H. ^{[1
]}

De Baerdemaeker J. ^{[1
]}

机构：

[1] Department of Agro-Engineering and Economics, Laboratory for Agro-Machinery and Processing, K.U. Leuven, Heverlee 3001

来源：

Precision Agriculture | 2002年 / 3卷 / 3期

关键词：

Classification; Neural networks; Self-organizing systems; Spectra; Weeds;

D O I：

10.1023/A:1015590520873

中图分类号：

学科分类号：

摘要：

A new neural network architecture for classification purposes is proposed. The Self-Organizing Map (SOM) neural network is used in a supervised way for a classification task. The neurons of the SOM become associated with local linear mappings (LLM). Error information obtained during training is used in a novel learning algorithm to train the classifier. The proposed method achieves fast convergence and good generalization. The classification method is then applied in a precision farming application, the classification of crops and different kinds of weeds by using spectral reflectance measurements. The classification performance of the proposed method is proven superior compared to other neural classifiers. Also, the proposed method compares favorably with the results obtained by using an optimal Bayesian classifier.

引用

页码：209 / 223

页数：14

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