Timmermans, A.J.M.

Verdenius, F.

English

Wageningen Yield 

EXPERIENCES WITH NEURAL NETWORK APPLICATIONS IN 
AGRICULTURE: TESTING EXPECTATIONS 
/ 
F. Verdenius, A.J.M. Timmermans 
ATO-DLO, Postbus 17, 6700 AA Wageningen, The Netherlands; phone: 
(+31) 317 47 50 00; fax: (+31) 317 41 22 60 e-mail: (F. Verdenius, 
A.J.M. Timmermansj@ato.dlo.nl 
\ 
\ 
Abstract: Neural networks have been reported as promising for applica­
tion in noisy, non-linear domains where human experts outperform many 
artificial intelligence solutions. The paper tests these expectations. Four 
NN applications are discussed in the light of three general purpose proc­
ess models for application of learning techniques. The evaluation shows 
that, in spite of several practical problems, neural networks are a valuable 
tool for practical applications. 
Keywords: Neural Networks, Classification, Regression, Agricultural 
Application 
1. INTRODUCTION 
In this paper the expectations about neural 
networks (NN) application are tested by dis­
cussing four projects performed at ATO-
DLO. In section 2 we discuss relevant as­
pects for NN applications. To enable evalua­
tion, three general process models for apply­
ing learning techniques are discussed as a 
reference in section 3. Section 4 discusses 
the four projects in relation to these refer­
ences. Section 5 abstracts from the actual 
projects, evaluating NN application in agri­
cultural domains, and section 6 concludes. 
2. CONTEXT 
2.1 Neural Networks 
Neural networks are computational tech­
niques that function in analogy with the hu­
man brain. In practice, applications of neural 
network are almost exclusively applying one 
technique: feed-forward multi-layer percep-
trons, with (variants of) error back propaga­
tion (BPNN, Rumelhart et al., 1986) as the 
training algorithm. This NN technique is able 
to represent complex functions and is rela­
tively easy to operate. The almost exclusive 
application of BPNN was concluded in a re-
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