Hanping Mao

Combining X-ray computed tomography with relevant techniques for analyzing soil–root dynamics – an overview

The study of below-ground features of roots and soil and their interactions is essential for understanding the configuration and diversities in such a dynamic environment. X-ray computed tomography is recognized as a tool for visualizing the physical interactions in the soil. In many studies, it has been used as a stand-alone tool to describe soil and root parameters. However, in recent times, attempts to couple it with other complementary tools are gaining rapid interest among researchers. The paper therefore provides an overview of the major application of combining X-ray computed tomography with other relevant methods in analyzing the structural characteristics of roots and soil media. The relevance of using this multidisciplinary approach for unraveling the mysteries surrounding root–soil dynamic interactions is stressed. The current and future trends of such studies are also pointed out.

Inspection of Lettuce Water Stress Based on Multi-sensor Information Fusion Technology

Characteristics of reflection spectrum, multi-spectral images and temperature of lettuce canopy were gained to judge the lettuce’s water stress condition which could lead to a precise, rapid & stable test of lettuce moisture and enlarged the models’ universality. By the extraction of lettuce’s multi-sensor characteristics in 4 different levels, quantitative analysis model of spectrum including 4 characteristic wavelengths, characteristic model of multi-spectral image and CWSI were established. These multi-sensor characteristics were fused by using the BP artificial neural network. Based on the fused multi-sensor characteristics, the lettuce moisture evaluation model was established. The results showed that the correlation coefficient of multi-spectral images model, spectral characteristics model and information fusion model were in turn increased, the correlation coefficients were respectively 0.8042, 0.8547 and 0.9337. It was feasible to diagnose lettuce water content by using multi-sensor information fusion of reflectance spectroscopy, multi-spectral images and canopy temperature. The correct rate and robustness of the discriminating model from multi-sensor information fusion were better than those of the model from the single-sensor information.

Quantitative determination of rice starch based on hyperspectral imaging technology

ABSTRACT In this study, a method for the quantitative determination of rice starch based on hyperspectral imaging technology was proposed. First, the hyperspectral imaging system in the spectral range of 871–1766 nm was used to collect the hyperspectral images of 100 rice samples of 10 starch grades. The support vector regression (SVR) model was established to determine the starch content by using full-wavelength spectra data. Among all the models, the SVR-principal component analysis (SVR-PCA) model with the Radial Basis Function showed the best results. To simplify the calibration model, PCA was used for feature extraction and the cumulative contribution rate of the first six principal components reached 99%, which could reflect most of the information of the full spectra data. Three new regression models based on the selected wavelengths were developed and the results were improved obviously. The SVR-PCA model obtained the best accuracy in prediction and calibration with the determination coefficients of prediction (R2p) of 0.991, root mean square error of prediction (RMSEP) of 0.669%, the determination coefficients of calibration (R2c) of 0.989, and root mean square error of calibration (RMSEC) of 0.445%. The overall results from this study demonstrated that the hyperspectral image technology is feasible to detect rice starch.

Identification of eggs from different production systems based on hyperspectra and CS-SVM

ABSTRACT 1. To identify the origin of table eggs more accurately, a method based on hyperspectral imaging technology was studied. 2. The hyperspectral data of 200 samples of intensive and extensive eggs were collected. Standard normalised variables combined with a Savitzky–Golay were used to eliminate noise, then stepwise regression (SWR) was used for feature selection. Grid search algorithm (GS), genetic search algorithm (GA), particle swarm optimisation algorithm (PSO) and cuckoo search algorithm (CS) were applied by support vector machine (SVM) methods to establish an SVM identification model with the optimal parameters. The full spectrum data and the data after feature selection were the input of the model, while egg category was the output. 3. The SWR–CS–SVM model performed better than the other models, including SWR–GS–SVM, SWR–GA–SVM, SWR–PSO–SVM and others based on full spectral data. The training and test classification accuracy of the SWR–CS–SVM model were respectively 99.3% and 96%. 4. SWR–CS–SVM proved effective for identifying egg varieties and could also be useful for the non-destructive identification of other types of egg.

The Relation of Paddy Rice Canopy Spectrum Reflectivity and the Leaf Moisture Content in the Different Nitrogen Condition

The paddy rice canopy spectrum at a critical growth period is obtained by the use of ASD spectrometer. The relationship of leaf water ratio and canopy spectrum reflectivity in the different nitrogen condition is analyzed. The results showed that the relevance of the paddy rice canopy spectrum reflectivity and the leaf moisture content is less under the environment of lack of nitrogen. The correlation of canopy spectrum reflectivity of waveband between 736nm and 1350nm and leaf moisture content is up to above 0.7 under the environment of right nitrogen amount. The correlation of canopy spectrum reflectivity of waveband between 730nm and 1300nm and leaf moisture content is above 0.5 under the environment of excessive nitrogen. The sensitive bands corresponding to paddy rice moisture content are selected, and the ratio vegetation indexes of different growth stages are analyzed. We can find that, at the heading stage, the relevance of R1000/R740 ratio and the moisture content of leaves is best without the influence of nitrogen fertilizer processing. A regression model is set up and the correlation coefficient reached 0.89, and the standard error is 0.0197.

THE RESEARCH OF PADDY RICE MOISTURE LOSSLESS DETECTION BASED ON L-M BP NEURAL NETWORK

The method of the quantitative analysis on the paddy rice moisture condition is studied, which is based on the spectral reflectivity of the leaf crest layer. Several subsections are carried on the entire spectrum curve by the equidistance, The sensitive characteristic wave-length is selected based on the table of molecular spectrum sensitive wave band, obtains the characteristic spectral reflection index value to take as the characteristic value. The convergence rate of the BP neural network is slow, so the L-M algorithm is introduced to carry on the renewal of the neural network weights. The paddy rice water moisture quantitative analysis forecast model is established by making use of the fast study function of the L-M algorithm neural network. The forecasting results indicate that the highest prediction error of the paddy rice water content is 6.72% and the average error rate is 4.23%. The prediction effect is better than the traditional BP network arithmetic, and it can be used in the lossless inspection of paddy rice moisture.

THE RESEARCH ON THE JUDGMENT OF PADDY RICE’S NITROGEN DEFICIENCY BASED ON IMAGE

Because of the unreliability judgment of paddy rice’s nitrogen deficiency depending on the traditional artificial naked eye, in this article, the way of the paddy rice’s nitrogen deficiency examination based on image is put forward, to achieve the precise fast lossless detection and judgment on the paddy rice’s nitrogen. Based on the sorting function of SMV, paddy rice leafs visible images are gathered, the texture features of image are extracted, the RBF nuclear function is chosen, the penalty coefficient C and the regularity coefficient ??are set, and the SVM sorting model is constructed. The recurrence sentencing rate to the training sample achieves 100%. The examination is caught on the test sample, and the accuracy rate of examination recognition achieve 95%, which indicates that the method of paddy rice’s nitrogen lossless examination judgment by image is effective and feasible to achieve the precise fast judgment on paddy rice’s nitrogen.