DocumentCode
1968177
Title
Recognition of Plants by Leaves Digital Image and Neural Network
Author
Pan, Jiazhi ; He, Yong
Author_Institution
Sci. Coll., Hangzhou Normal Univ., Hangzhou
Volume
4
fYear
2008
fDate
12-14 Dec. 2008
Firstpage
906
Lastpage
910
Abstract
To identify different plants by leaves digital image is one key problem in precision farming. By the combination of image processing and neural network, Most of the image blocks of different plants could be correctly classified. Firstly, the image enhancement processing can make objects in the source image clear. Secondly, due to the different shapes and sizes of image blocks of leaves, they could be separated and extracted from sources. Then, by using image analysis tools from Matlab, these characters such as sizes, radius, perimeters, solidity, and eccentricity could be calculated. Then, using them as input data, create a radial basis function neural networks. Divide the input data into two parts. Select one part to train the network and the other to check the validity of the model. Finally, input data from other image frames under the same condition could be used to check the model. In this work, the total accuracy is about 80%. These methods was simple and highly effective, So they could easily be integrated into auto machines in the field, which can largely saving labor and enhance productivity.
Keywords
crops; farming; image classification; image enhancement; radial basis function networks; Matlab; image analysis tool; image block classification; image enhancement; leaves digital image; plant recognition; precision farming; radial basis function neural network; Data mining; Digital images; Image analysis; Image enhancement; Image processing; Image recognition; Mathematical model; Neural networks; Radial basis function networks; Shape; RBF--NN; crop; digital image; recognition; weed;
fLanguage
English
Publisher
ieee
Conference_Titel
Computer Science and Software Engineering, 2008 International Conference on
Conference_Location
Wuhan, Hubei
Print_ISBN
978-0-7695-3336-0
Type
conf
DOI
10.1109/CSSE.2008.918
Filename
4722765
Link To Document