Paper Title:
The BP Network Classification Leafminer-Infected Leaves Based on the Fractal Dimension
  Abstract

Leafminer is one of pest of many vegetables, and the damage may cover so much of the leaf that the plant is unable to function, and yields are noticeably decreased. In order to get the information of the pest in the vegetable before the damage was not serious, this research used a BP neural network to classify the leafminer-infected tomato leaves, and the fractal dimension of the leaves was the input data of the BP neural network. Prediction results showed that when the number of FD was 21 and the hidden nodes of BP neural network were 21, the detection performance of the model was good and the correlation coefficient (r) was 0.836. Thus, it is concluded that the FD is an available technique for the detection of disease level of leafminer on tomato leaves.

  Info
Periodical
Chapter
Chapter 3: Computational Methods for Engineering
Edited by
Elwin Mao and Linli Xu
Pages
163-167
DOI
10.4028/www.scientific.net/AEF.1.163
Citation
D. K. Wu, C. Y. Xie, "The BP Network Classification Leafminer-Infected Leaves Based on the Fractal Dimension", Advanced Engineering Forum, Vol. 1, pp. 163-167, 2011
Online since
September 2011
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