[Horticultural plant diseases multispectral classification using combined classified methods].
The research on multispectral data disposal is getting more and more attention with the development of multispectral technique, capturing data ability and application of multispectral technique in agriculture practice. In the present paper, a cultivated plant cucumber' familiar disease (Trichothecium roseum, Sphaerotheca fuliginea, Cladosporium cucumerinum, Corynespora cassiicola, Pseudoperonospora cubensis) is the research objects. The cucumber leaves multispectral images of 14 visible light channels, near infrared channel and panchromatic channel were captured using narrow-band multispectral imaging system under standard observation and illumination environment, and 210 multispectral data samples which are the 16 bands spectral reflectance of different cucumber disease were obtained. The 210 samples were classified by distance, relativity and BP neural network to discuss effective combination of classified methods for making a diagnosis. The result shows that the classified effective combination of distance and BP neural network classified methods has superior performance than each method, and the advantage of each method is fully used. And the flow of recognizing horticultural plant diseases using combined classified methods is presented.
Feng
Jie
J
School of Physics & Electronic Information Technology, Yunnan Normal University, Kunming 650092, China. fengjie_ynnu@yahoo.com.cn
Li
Hong-Ning
HN
Yang
Wei-Ping
WP
Hou
De-Dong
DD
Liao
Ning-Fang
NF
D000383:Agriculture; D018553:Cucumis sativus / Q000382:microbiology*; D016571:Neural Networks, Computer; D010935:Plant Diseases / Q000145:classification*; D018515:Plant Leaves; D013057:Spectrum Analysis