Jinling Zhao

New Optimized Spectral Indices for Identifying and Monitoring Winter Wheat Diseases

The vegetation indices from hyperspectral data have been shown to be effective for indirect monitoring of plant diseases. However, a limitation of these indices is that they cannot distinguish different diseases on crops. We aimed to develop new spectral indices (NSIs) that would be useful for identifying different diseases on crops. Three different pests (powdery mildew, yellow rust, and aphids) in winter wheat were used in this study. The new optimized spectral indices were derived from a weighted combination of a single band and a normalized wavelength difference of two bands. The most and least relevant wavelengths for different diseases were first extracted from leaf spectral data using the RELIEF-F algorithm. Reflectance of a single band extracted from the most relevant wavelengths and the normalized wavelength difference from all possible combinations of the most and least relevant wavelengths were used to form the optimized spectral indices. The classification accuracies of these new indices for healthy leaves and leaves infected with powdery mildew, yellow rust, and aphids were 86.5%, 85.2%, 91.6%, and 93.5%, respectively. We also applied these NSIs for nonimaging canopy data of winter wheat, and the classification results of different diseases were promising. For the leaf scale, the powdery mildew-index (PMI) correlated well with the disease index (DI), supporting the use of the PMI to invert the severity of powdery mildew. For the canopy scale, the detection of the severity of yellow rust using the yellow rust-index (YRI) showed a high coefficient of determination ( \mbiR2 = 0.86) between the estimated DI and its observations, suggesting that the NSIs may improve disease detection in precision agriculture application.

Comparison and Analysis of Data Assimilation Algorithms for Predicting the Leaf Area Index of Crop Canopies

Data assimilation as an approach for crop Leaf Area Index (LAI) estimation has been rapidly developed in the field of agricultural remote sensing. Many studies have attempted to integrate sequential remotely sensed observations in the dynamical operation of physical models, aiming to improve model performance of LAI estimation by using various data assimilation schemes. In this study, a new data assimilation algorithm is proposed. For this algorithm, the background field of free parameters is constructed according to the ensemble construction scheme of EnKF algorithm, and a cost function is constructed based on the cost function construction strategy in 4DVAR algorithm in order to analyse and update the free parameters. The last updated free parameters are input into the model for LAI estimation until all the observations are assimilated. Additionally, the cost function in data assimilation procedure is optimised using VFSA algorithm. Winter wheat in Beijing in 2002 is selected as the experimental object. The crop growth model CERES-Wheat and the radiative transfer model PROSAIL are coupled in the assimilation process. Sequentially observed NDVI of winter wheat are assimilated into the coupled model using different assimilation algorithms to quantitatively compare and analyse the LAI estimation results. The performance of the new algorithm is better than CERES-Wheat model, EnKF algorithm, and 4DVAR algorithm, with a lesser RMSE by 43.68%, 41.67%, and 28.99%, respectively, and with increased R

Hyperspectral measurements of severity of stripe rust on individual wheat leaves

^{2}

Detecting Aphid Density of Winter Wheat Leaf Using Hyperspectral Measurements

by 110.53%, 90.48%, and 33.33%, respectively. Moreover, for the subset LAI

Crop Disease and Pest Monitoring by Remote Sensing

\geq

Fast detection of fenthion on fruit and vegetable peel using dynamic surface-enhanced Raman spectroscopy and random forests with variable selection

3.00, higher estimation precision and greater efficiency of the new algorithm are obtained.

Analysis of influencing factors on soil Zn content using generalized additive model

The objective of this study was to assess the effect of severity of stripe rust (Puccinia striiformis) on the hyperspectral reflectance of wheat. A total of 110 leaf samples with a range of disease severity were collected at the heading stage (Stage І, 29 April) and grain filling stage (Stage II, 21 May). The spectra of the adaxial and abaxial surfaces of the leaf samples were taken using an ASD Leaf Clip, and the spectral characteristics were analysed. The photochemical reflectance index (PRI) was used to build two linear regression functions from the two growth stages using 70 leaves, and the remaining 40 leaves were used to validate their effectiveness. The results indicated that P. striiformis caused changes in foliar water and chlorophyll, and those changes made it feasible to assess disease severity using in situ hyperspectral measurements. In general, the reflectance values from the adaxial surfaces of the leaf samples were smaller than the abaxial surfaces. In comparison to Stage І, the spectral contrast of four different disease severities was greater at Stage II. By comparing the regression functions, the coefficient of determination using the set of leaves for validation for Stage І (R2 = 0.74) was smaller than that for Stage II (R2 = 0.83). However, the coefficient of determination for validation for Stage І (R2 = 0.91) was slightly larger than that of Stage II (R2 = 0.90). The results suggest that the ASD Leaf Clip is an ideal tool to collect in situ hyperspectral measurements of wheat leaves showing symptoms of stripe rust, and Stage II is more appropriate to assess severity compared to Stage І.

New Triangle Vegetation Indices for Estimating Leaf Area Index on Maize

Wheat aphid, Sitobion avenae F. is one of the most destructive pests that emerge in northwest China almost every year, impacting on the production of winter wheat. Hyperspectral remote sensing has been demonstrated to be superior to a traditional method in detecting diseases and pests. In this study, spectral features (SFs) were examined by four methods to detect aphid density of wheat leaf and model was established to estimate aphid density using partial least square regression (PLSR). A total of 60 wheat leaves with different aphid densities were selected. Aphid density of the leaves was first visually estimated, and then the reflectance of leaves was measured in the spectral range of 350-2500 nm using a spectroradiometer coupling with a leaf clip. A total of 48 spectral features were obtained and examined via correlation analysis, independent t-test by spectral derivative method, continuous removal method, continuous wavelet analysis (CWA) and commonly used vegetation indices for stress detection. Based on variable importance in projection (VIP), five spectral features (VIP ≥ 1) were selected from 17 spectral features due to their strong correlation with aphid density (R2 ≥ 0.5) to establish the model for estimating aphid density by PLSR. The result showed that the model had a great potential in detecting aphid density with a relative root mean square error (RMSE) of 15 and a coefficient of determination (R2) of 0.77.

Quantitative Determination of Chlormequat Chloride Residue in Wheat Using Surface-Enhanced Raman Spectroscopy

Plant diseases and pests can affect a wide range of commercial crops, and result in a significant yield loss. It is reported that at least 10% of global food production is lost due to plant diseases (Christou and Twyman, 2004; Strange and Scott, 2005). Excessive pesticides are used for protecting crops from diseases and pests. This not only increases the cost of production, but also raises the danger of toxic residue in agricultural products. Disease and pest control could be more efficient if disease and pest patches within fields can be identified timely and treated locally. This requires obtaining the information of disease infected boundaries in the field as early and accurately as possible. The most common and conventional method is manual field survey. The traditional ground-based survey method requires high labor cost and produces low efficiency. Thus, it is unfeasible for large area. Fortunately, remote sensing technology can provide spatial distribution information of diseases and pests over a large area with relatively low cost. The presence of diseases or insect feedings on plants or canopy surface causes changes in pigment, chemical concentrations, cell structure, nutrient, water uptake, and gas exchange. These changes result in differences in color and temperature of the canopy, and affect canopy reflectance characteristics, which can be detectable by remote sensing (Raikes and Burpee 1998). Therefore, remote sensing provides a harmless, rapid, and cost-effective means of identifying and quantifying crop stress from differences in the spectral characteristics of canopy surfaces affected by biotic and abiotic stress agents.

Compressible Supercapacitor with Residual Stress Effect for Sensitive Elastic-Electrochemical Stress Sensor

Dynamic surface-enhanced Raman spectroscopy (D-SERS) based on the state change of the substrate not only significantly enhances but also provides a highly reproducible Raman signal. Hence, we develop a fast and accurate method for the detection of fenthion on fruit and vegetable peel using D-SERS and random forests (RF) with variable selection. With uniform Ag nanoparticles, the dynamic spectra of fenthion solution at different concentrations were obtained using D-SERS, and fenthion solution greater than or equal to 0.05mg/L can be detected. Then, the quantitative analysis models of fenthion were developed by RF with variable selection for spectra of different range. The model of best performance is developed by RF and spectra of characteristic range with higher RF importance (top 40%), and the root mean square error of cross-validation is 0.0101mg/L. Moreover, the fenthion residue of tomato, pear, and cabbage peel were extracted by a swab dipped in ethanol and analyzed using the above method to further validate the practical effect. Compared to gas chromatography, the maximal relative deviation is below 12.5%, and the predicted recovery is between 87.5% and 112.5%. Accordingly, D-SERS and RF with variable selection can realize the fast, simple, ultrasensitive, and accurate analysis of fenthion residue on fruit and vegetable peel.