The aboveground biomass (AGB) is a key index for predicting wheat yield. In the case of high biomass, the AGB estimation of single spectral feature or image texture is poor. Therefore, this study evaluated the ability of fusion of spectral reflectance and texture to predict wheat AGB. Among them the reflectance spectrum of the wheat canopy was collected by near-earth spectrometer, and the texture features of three bands of RGB were extracted by gray co-occurrence matrix. Partial least squares regression (PLS) model was used to evaluate the relationship between fusion features and AGB. The experimental results based on the validated data set show that the AGB estimation effect of feature fusion is better than that of single feature (R2 = 0.70; RMSE = 0.06). This shows that the combination of spectral reflectance and texture can improve the accuracy of AGB estimation in the later stage.
As a novel and ultrasensitive detection technology that had advantages of fingerprint effect, high speed and
low cost, surface-enhanced Raman scattering (SERS) was used to develop the regression models for the fast quantitative
detection of thiram by support vector machine regression (SVR) in the paper. Meanwhile, three parameter optimization
methods, which were grid search (GS), genetic algorithm (GA) and particle swarm optimization (PSO), were employed
to optimize the internal parameters of SVR. Furthermore, the influence of the spectral number, spectral wavenumber
range and principal component analysis (PCA) on the quantitative detection was also discussed. Firstly, the experiments
demonstrate the proposed method can realize the fast and quantitative detection of thiram, and the best result is obtained
by GS-SVR with the spectra of the range of characteristic peak which are processed by PCA. And the effect of GS, GA,
PSO on the parameter optimization is similar, but the analysis time has a great difference in which GS is the fastest.
Considering the analysis accuracy and time simultaneously, the spectral number of samples over each concentration
should be set to 50. Then, developing the quantitative model with the spectra of range of characteristic peak can reduce
analysis time on the promise of ensuring the detection accuracy. Additionally, PCA can further reduce the detection error
through reserving the main information of the spectra data and eliminating the noise.
Owing to the shortages of inconvenience, expensive and high professional requirements etc. for conventional recognition devices of wheat leaf diseases, it does not satisfy the requirements of uploading and releasing timely investigation data in the large-scale field, which may influence the effectiveness of prevention and control for wheat diseases. In this study, a fast, accurate, and robust diagnose system of wheat leaf diseases based on android smartphone was developed, which comprises of two parts—the client and the server. The functions of the client include image acquisition, GPS positioning, corresponding, and knowledge base of disease prevention and control. The server includes image processing, feature extraction, and selection, and classifier establishing. The recognition process of the system goes as follow: when disease images were collected in fields and sent to the server by android smartphone, and then image processing of disease spots was carried out by the server. Eighteen larger weight features were selected by algorithm relief-F and as the input of Relevance Vector Machine (RVM), and the automatic identification of wheat stripe rust and powdery mildew was realized. The experimental results showed that the average recognition rate and predicted speed of RVM model were 5.56% and 7.41 times higher than that of Support Vector Machine (SVM). And application discovered that it needs about 1 minute to get the identification result. Therefore, it can be concluded that the system could be used to recognize wheat diseases and real-time investigate in fields.
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