Multifractal Method And Modeling On Leaf Image For Crop Diagnostic

Leaf, as a vital organ of crops, can reflect growth conditions of the crops directly. The type of nutritional deficiency and plant disease can be mirrored by the images of the leaves. The images can also provide critical clues for identifying and diagnosing different level of nutrition. In the research of digital agriculture, it is an important way to solve above problems to process the leaf digital images based on mathematics and computers. How to get the useful information from the leaf images have become the key issues to locate, recognize and diagnose the singular region in the images impacted by the nutritional deficiency and diseases. Current studies are focused on the leaf color and shape characteristics, few studies on the texture information. However, as an inherent nature and characteristics of leaf, the leaf’s texture remains relatively stable in the crop growth and less susceptible to outside influence. Meanwhile, the leaf’s texture will change when the leaves are affected by nutrient deficiency and disease. By this token, the texture features of leaf image are ideal object for researching the above problems. Multifractal theory is an important tool to describe the texture features, which is widely applied in the field of image processing. Targeting at non-stationary trait of the leaf images, some robust texture descriptors are proposed in our paper by the multifractal theory to study the above problems. It should lay foundation for diagnosing the corps with nutritional deficiency and diseases nondestructively by machine intelligence.A definition of image stationary and two methods of detecting the image stationary are proposed. The research of the images stationary problem is the premise concern in determining which multifractal technologies to extract the texture feature for the leaf images. Since the existed multifractal methods were proposed based on stationary measures, they cannot solve the non-stationary problems. Unlike the other images, the leaf images with nutritional deficiency and diseases easily lead to mutation of local gray values and may produce not smooth measures. In allusion to this phenomenon, we define the image stationary tentatively and present two methods of stationary detection based on the stationary of one-dimension series. The investigation by two groups of synthetic images generated by fractional Gaussian noise and fractaional Brown motion surface, respectively, shows that the definition and the detection methods of image stationary are effective. In the experiment of detecting the stationary for six corn disease leaf images by the above methods, it shows that they are all non-stationary.Some texture characterization algorithms based on multifractal detrended fluctuation analysis (MF-DFA) is proposed. Employing right feature extraction method by the multifractal theory is the important safeguard for getting the useful texture information of the leaf images. Targeting at non-stationary trait of the leaf images and the problem of the non-stationary measures cannot be solved by standard multifractal analysis; we propose a texture characterization method based on multifractal detrended fluctuation analysis (MF-DFA). Three new texture descriptors, namely, the generalized Hurst exponent of gray series for global image, denoted as h(q), the generalized Hurst exponent of two-dimension surface for global image, denoted as H(q), and the generalized Hurst exponent of local two-dimension surface for each pixel in the image, denoted as LHq, are obtained. Next, we test the three exponents by two groups of experiments. On one hand, by comparing with the traditional texture descriptors, such as mono-fractal dimension and multifractal spectrum based on mono (multi)-fractal, four statistics calculated by gray occurrence matrix method, the results demonstrate that the proposed h(q) and H(q) have the best noise immunity (the average error is less than2%), best compression resistance (the average error is less than5%) and best anti-ambiguity (the average error is less than7%). The three kinds of errors are less than responding errors calculated by the other texture descriptors significantly. On the other hand, both the proposed texture descriptor LHq and other two multifractal indicators, which are Holder coefficients based on capacity measure and generalized multifractal dimension Dq based on multifractal differential box-counting (MDBC) method have been compared in our segmentation experiments. The first comparative experiment indicates that the segmentation results obtained by the proposed local multifractal detrended fluctuation exponent are as well as the MDBC-based Dq and superior to the Holder coefficients significantly. The results in the second comparative experiment of noise immunity demonstrate that the proposed method can distinguish the texture images more effectively and proazvide more robust segmentations than the MDBC-based Dq significantly.A novel image segmentation algorithm based on local MF-DFA (LMF-DFA) is proposed. The image segmentation problem is the key problem for illustrating the singular regions in the leaf images. In allusion to non-stationary trait of the leaf images with nutritional deficiency and diseases, we propose a novel image segmentation algorithm based on LMF-DFA. In the proposed algorithm, the local generalized Hurst exponent LHq is calculated firstly. And then, box-counting dimension f(LHq) is calculated for the sub-images constituted by the LHq of some pixels, which come from a specific region. Consequently, series of f(LHq) of the different regions can be obtained. Finally, the singular regions are segmented according to the corresponding f(LHq). The method is used to locate the lesion regions for the six corn disease leaf images, namely, Southern corn leaf blight, Gray leaf spot, Curvularia leaf spot, Round leaf spot, Rust disease and Brown spot. Meanwhile, both the proposed method and other two segmentation methods--multifractal spectrum based and fuzzy C-means clustering have been compared for the six disease images. The results indicate that the proposed method can recognize the lesion regions more effectively and provide more robust segmentations.A kind of diagnostic model for rapeseed nitrogen based on the multifractal feature of leaf image is proposed. Model construction is the kernel problem in researchingthe crop nutrition diagnosis based on leaf characteristic. On the one hand, we construct some regress models for the rapeseed nitrogen content under the different levels. The least relative square mean root error of predicting the nirtrogen is about10%-20%by using the multifractal parameters as the independent variables. On another hand, a qualitative diagnostic model is proposed for identifying different nitrogen level of rapeseed leaves. In the model, using the selected multifractal parameters as image characteristics, we take diagnosis analysis for base, middle and top of rapeseed plant with different nitrogen levels, which are collected from the field environment. The experiment result shows that the best diagnose accuracy comes from the base of the rapeseed leaves. It is explained that the base leaf is most sensitive to the nitrogen deficiency. In the diagnose of the mixed samples, the accuracy reaches94.03%å'Œ94.90%under support vector machines and kernel methods and random forests methods by the5-fold cross validation.