[Study on the Detection of Slight Mechanical Injuries on Apples with Hyperspectral Imaging].

Nondestructive detection is one of the hottest spots in the application of hyperspectral remote sensing. The apple is easy to produce slight mechanical injuries that affects its quality in the process of picking and transporting. The hyperspectral images of 54 “yellow banana” and “Yantai Fushi” apples with slight injuries in the visible and near-infrared (400~1 000 nm) ranges are acquired; the mean spectral curves of injury regions on apples are extracted; the endmember spectrum are extracted based on minimum noise fraction (MNF) and geometric vertex principle; and the spectral angle is calculated between spectral of injury region and endmember spectral; a model of endmember extraction spectral angle (EESA) is constructed to detect slight mechanical injuries on apples.

[Quantitative Identification of Yellow Rust and Powdery Mildew in Winter Wheat Based on Wavelet Feature].

Powdery mildew (Blumeria graminis) and stripe rust (Puccinia striiformis f. sp. Tritici) are two of the most prevalent and serious winter wheat diseases in the field, which caused heavy yield loss of winter wheat all over the world.

[Use of Near-Infrared Hyperspectral Images to Differentiate Architectural Coatings with Different Qualities].

Architectural coatings sold in market fall into many categories which mean different models and qualities. The research plans to differentiate different kinds of architectural coatings in quality using hyperspectral technology. Near-Infrared hyperspectral images of four kinds of architectural coatings (in a descending quality order of brand A, B, C, and D) in same color were acquired.

[Simulated Experimental Research on Using Canopy Spectra of Surface Vegetation to Detect CO2 Microseepage Spots].

With the global warming, people now pay more attention to the problem of the emission of greenhouse gas (CO2). Carbon capture and storage (CCS) technology is an effective measures to reduce CO2 emission. But there is a possible risk that the CO2 might leak from underground.

[Comparison and analysis of hyperspectral remote sensing identifiable models for different vegetation under waterlogging stress].

With the global climate warming, flooding disasters frequently occurred and its influence scope constantly increased in China. The objective of the present paper was to study the leaf spectral features of vegetation (maize and beetroot) under waterlogging stress and design a hyperspectral remote sensing model to monitor the flooding disasters through a field simulated experiment. The experiment was carried out in the Sutton Bonington Campus of University of Nottingham (52.

[Spectral characteristics and identification research of soybean under different disease stressed].

The objective of this paper is to identify disease and its severity of soybean by using single leaf spectral data in the field. The soybean spectral were measured in the Sutton Bonington Campus of University of Nottingham (52.8 degrees N, 1.

[Research on the spectral feature and identification of the surface vegetation stressed by stored CO2 underground leakage].

With the global climate warming, reducing greenhouse gas emissions becomes a focused problem for the world. The carbon capture and storage (CCS) techniques could mitigate CO2 into atmosphere, but there is a risk in case that the CO2 leaks from underground. The objective of this paper is to study the chlorophyll contents (SPAD value), relative water contents (RWC) and leaf spectra changing features of beetroot under CO2 leakage stress through field experiment.

[Using hyperspectral remote sensing to estimate canopy chlorophyll density of wheat under yellow rust stress].

The canopy reflectance of winter wheat infected with different severity yellow rust was collected in the fields and canopy chlorophyll density (CCD) of the whole wheat was measured in the laboratory. The correlation was analyzed between hyperspectral indices and CCDs, the indices with relationship coefficients more than 0. 7 were selected to build the inversion models, and comparing the predicted results and measured results to test the models, the results showed the first derivative index (D750-D550)/(D750+D550) has higher prediction precision than other indices, while the next is first derivative index (D725-D702)/(D725+D702).

[Using canopy hyperspectral ratio index to retrieve relative water content of wheat under yellow rust stress].

The aim of this paper is to estimate canopy relative water contents (RWC) of winter wheat under yellow rust stress by using hyperspectral remote sensing. The canopy reflectance of winter wheat that infected different severity yellow rust was collected and the disease index (DI) of the wheat was investigated respectively in the fields, whereafter the wheat was sampled corresponding to the canopy reflectance measurements and the RWC of the whole wheat were measured in the Laboratory. The research showed that the canopy spectra reflectance gradually decreased in the near-infrared (NIR) region (900-1,300 nm) with RWC reduction, however, canopy spectra reflectance gradually increased in the short-wave-infrared (SWIR) region (1,300-2,500 nm), and there was just higher minus correlation between RWC and DI.

[Using the distance between hyperspectral red edge position and yellow edge position to identify wheat yellow rust disease].

The objective of the present paper is to identify healthy wheat and disease wheat by using hyeprspectral remote sensing as soon as possible. The canopy spectral reflectance of winter wheat infected by different severity yellow rust was measured and the disease indices (DI) were investigated in the field respectively. Smoothing the canopy spectra and calculating the first derivative values, the two methods were used to calculate the red edge position (REP) and yellow edge position (YEP) of the first derivative values: (a) maximum of the first derivative value; (b) Cho and Skidmore method.

[Comparison of principal component analysis with VI-empirical approach for estimating severity of yellow rust of winter wheat].

The canopy reflectance of winter wheat infected by yellow rust with different severity was measured through artificial inoculation, and the disease index (DI) of the wheat corresponding to the spectra acquired in the field was obtained. Principal component analysis (PCA) was used to compute the first 5 principal components (PCs) of canopy spectra in the 350-1 350 nm range and the first 3 PCs of first-order derivative in blue edge (490-530 nm), yellow edge (550-582 nm) and red edge (630-673 nm), respectively. Step-wise regression was used to build models, the results of those models are compared with that of VI-empirical models, and the result shows that the model based on PCs of first-order derivative is particularly accurate compared to others, with the RMSE of 7.

[Using hyperspectral derivative index to monitor winter wheat disease].

The canopy reflectance of winter wheat that infected different severity stripe rust was measured through artificial inoculation, the disease index (DI) of the wheat corresponding to the spectra was acquired in the field, and the parameters of biochemistry and biophysics were measured indoors. The 1st derivatives were analyzed. The results show that the 1st derivative values increase at the green edge (500-560 nm), while decrease at the red edge (680-760 nm) with DI increasing.

[Study on hyperspectra estimation of pigment contents in canopy leaves of winter wheat under disease stress].

The canopy reflectance of winter wheat infected with stripe rust was measured in the field through artificial inoculation, and the pigment contents of the wheat leaves were determined indoor. The correlation between pigment contents and canopy hyperspectra data and the first derivative data of the disease wheat were analyzed respectively. Using linear and non-linear regression methods, and choosing a part of samples, the estimation models about pigment contents of disease wheat were built.