| Pre-estimating the yield of grain crops is one of the significant content of the precision agriculture. Currently, many relevant researches have been reported over the world, and so(?)ne pre-estimating approaches have been applied to the practical production. However, most of these methods have the disadvantages of needing many manpower as well as material recourses, and some of them were very unconvinent in collecting basic data, thus it is too difficult to popularize these methods in rural area. Simple, practical and less-investment methods for pre-estimating the yield have been required.In this paper, author proposed a novel method for estimating the paddy yield on the basis of the fractal theory and the image texture analysis. Presently, traditional on-the-spot sampling and survey methods, remote sensing yield estimating and predicting yield based on the crop-environment model are three main methods. Traditional on-the-spot sampling and survey methods are time and labor consuming and low efficiency. Remote sensing yield estimating needs an enormous input of finacial and material recourses. For the methods based on the crop-environment model, it is very hard to collect the vast amount of meteorological data and agricultural feature parameters. The image texture analysis and the fractal theory have the superiority in describing objects which present abnormities in a microscopic scales but show regularities in a macroscopic scales. In this paper, based on image texture analysis and the fractal theory, feature parameters of mass were extracted from images of individual paddy spikes and paddy plots, the correlativities between these feature parameters and the yields were studied, mathematical models were established to measure the mass of individual paddy spikes and the per-square-meter yields of paddy plots. Finally,the PCA technology was used for precision pre-estimating the yield of grain crops. Main contents and conclusions were summed up as follows:1. A novel thought and approach was brought foreward for pre-estimating the yield of paddy crops. In this paper, based on the images of individual paddy spikes and paddy plots in their maturation phase, using digital image processing technology, and combining with the fractal theory and the texture analysis, a new method was designed and studied to estimate the yield of paddy grain. This method achieved the aim of simple, practical and less-investment, and provided a theoretical basis for further researches about accurate yield pre-estimating system.2. Fractal feature parameters of the images of paddy spikes and paddy plots were extracted. The shap of individual paddy spikes and paddy plots present an distinct abnormity in partial areas. Obviously, it is very hard to derive characteristic parameters in a traditional domain of Euclidean geometry. The fractal theory provides us a effective way to extract feature parameters of the images of paddy spikes and paddy plots. In this paper, whether those images bear fractal features were studied, the algorithms of extracting the fractal demention (FD) were worked over based on the definition and the existing calculating principles of the FD, and eventually the FDs were extracted from preprocessed images of paddy spikes and paddy plots using a calculation programme exploited on a VC++ platform. The result shows that the images of paddy spikes and paddy plots bear fractal features. In our researches, calculation programmes were disigned to extract the Box Counting Dimensions (BC) from binorized images, the Differential Box Counting Dimensions (DBC) and the multi-fractal dimensions (D(5)), and relevent feature parameters were extracted.3. Texture feature parameters of the images of paddy spikes and paddy plots were extracted. The texture of individual paddy spikes and paddy plots present an abnormity in partial areas, but as a whole, it shows a certain regularity. This is the characteristic of the texture. In this paper, based on the description and the statistical methods of texture feature, calculation programmes were designed to extract textural feature parameters. From histogram features, the mean, the variance, the smoothness, the consistency, the third order moment and the entrop of the gray level were extracted. From the gray-level co-occurrence matrix, the diagonal second order moment, the correlation degree, the contrast grade, the entrop, the inverse difference moment and the variance of sum were extracted. And from the gray level-gradient co-occurrence matrix, the energy, the gray average, the gradient average, the correlation degree, the entropy of mixing, the inertia and the inverse difference moment were extracted.4. The correlativity between the different Box-Counting Dimensions and the textual feature parameters of the images of paddy spikes and paddy plots and the masses of paddy spikes and the per-square-meter yield of paddy plots studied. The result shows that feature parameters which have linear correlativity with the masses of paddy spiked are BC, DBC, texture feature parameters extracted from histogram features (the mean, the variance, the smoothness, the third order moment, the consistency and the entrop of the gray level), the correlation degree and the contrast grade extracted from the gray-level co-occurrence matrix, and thegray average, the gradient average of the gray level-gradient co-occurrence matrix, and those have linear correlativity with the per-square-meter yield of paddy plots are DBC, D(5), the entropy of histogram features, the contrast grade of the gray-level co-occurrence matrix, the gray average and the gradient average of the gray level-gradient co-occurrence matrix.5. In our research, Principal Component Analysis (PCA, a vector space transform often used to reduce multidimensional data sets to lower dimensions for analysis) were used to extract some aggregate variables which bear best explanatory ability to the yield. Instead of the original variables, fewer new aggregate variables were used in establishing regression models. These models were used to forecast the mass of individual paddy spike and the per-square-meter yields. The result shows that, instead of primary 7 feature parameters, the contribute rate of the first principal component is as high as 92.83%. instead of primary 6 feature parameters, the contribute rate of the first and the second principal components is as high as 97.37%.6. The multiple linear regression equation method were used in establishing mathematical models for measuring the masses of individual paddy spikes and the per-square-meter yields of paddy plots. The model for measuring the masses of individual paddy spikes could described as:Y=5.7174-0.3668z,, where Y equals the mass of a paddy spike, Z1 equals the principal components. The model for measuring the per-square-meter yields could described as:Y=0.7889+0.0598z1+0.0295z2+0.0541z3, where Y equals the per-square-meter yield, Z1, Z2, Z2 equals the first and the second principal components.7. The posterior-variance-test was used in model precision grade testing. Results show that the precision grade of the model for measuring the masses of individual paddy spikes isâ… (excellent). The precision grade of the model for measuring the per-square-meter yields isâ…¢(reluctantly qualified).This research provided a novel idea and inspiration for the application of the fractal theoty and the image texture analysis in agricultural engineering. The model, which was used to measure the mass of individual paddy spike, would give references to the researches of cultivating new paddy varieties. The models of the per-square-meter yield provide a novel approach to measuring the yields of paddy rice, and could give a new choice in designing an estimating device which could be fixed on a combine harvester. |
PDF Full Text Request