关键词: 数字图像, 线性网络, BP神经网络, 径向基网络, 氮素含量

Abstract:

Artificial Neutral Network (ANN) has some important features, such as self-study, acceptance-error, building math model rapidly. ANN has been widely used in many fields, some people have made a lot of findings in agriculture by ANN. The technology of digital image processing is also very important for agriculture, and people have found there are some relation between color information and the nitrogen quantity for maize, tomato. But nobody use ANN to found the relation. The objective of this research is to process the digital image of cotton leaf, and use ANN to select the best math model and input vectors for establishing the relation between the color information and nitrogen quantity of cotton leaf. So we can use the advantages of ANN and the technology of digital image processing, and select the most suitable result for this research automatically. We select three ANN models (line on network, BP network and radical basis function (RBF) network) and six pieces of input vectors for this research, and train each model with color information from 180 pieces of digital images, and use the better to forecast nitrogen quantity of 30 pieces of images. The results showed that linear network was not fit this research and the relation between color information and nitrogen quantity was not fit the linear models, and RBF network was better for this research than BP network. RBF network had a lot of advantages in calculating the quantity of nitrogen using vector (B, H, G-R, G/R). The precision of training result was very marked, with r = 0.9022**, and the precision of forecast was high, with r = 0.8674** by this ANN forecast using the 30 pieces of cotton digital image. Because of local smallest, simple framework, and rapid training, RBF network can get the nitrogen quantity in plant by digital image information, and enhance the application of ANN in agriculture.

Key words: Digital image, Line network, BP network, RBF (radical basis function) network, Nitrogen quantity