# _remote sensing_
## 3 Results
### Crop Diseases Results
#### 3.2.2 Wheat Yellow Rust Mapping  
A primary aim of the research, taking wheat yellow rust as an example, was to use remote sensing technology to achieve accurate monitoring of crop diseases at different scales, thus providing a reference for the application of remote sensing technology in crop disease monitoring and forecasting. We have analyzed the pathogenesis and epidemiological process of wheat yellow rust and have developed a feature extraction method that is consistent with the pathological characteristics and epidemiological mechanism of this disease. Starting from the characteristics of the impact of wheat yellow rust on the physiological and biochemical indicators of wheat, parameters such as vegetation index and wavelet features were extracted, and the data characteristics at different scales were considered to construct monitoring models applicable to different scales. At the leaf scale, a monitoring model based on wavelet features extracted from non-imaging hyperspectral data was developed. It was found that the wavelet features with strong correlation with disease severity were mainly distributed in the 480 nm-750 nm range. When the proportion of yellow rust spores in the leaf was small (4% to 19%), the average monitoring accuracy of the model was 70.1%. With the increase of yellow rust infestation time and area of yellow rust spores in the leaf, the spectrum of the leaf changed significantly, therefore providing more valuable information for disease monitoring and rapidly improving the monitoring accuracy of the model. The average monitoring accuracy of model was 86.3% when the proportion of yellow rust spore in the leaf was more than 40%. Table 8 shows the change in the average monitoring accuracy of the model after yellow rust infestation. At the canopy scale, wavelet features and vegetation indices with high correlations with disease severity were extracted using UAV hyperspectral images, and then applied to construct the disease monitoring model. The results of the analysis showed that the monitoring accuracy of the applied model tended to increase, and then decrease, as the development of yellow rust progressed. The monitoring accuracy (Table 9) ranged from 88.7% to 99.2% for healthy samples and 84.2% to 100% for diseased samples. The highest monitoring accuracy of the model was achieved for 31 days after the infection, with 99.2% and 100% of identification accuracy for healthy and diseased samples, respectively. The results of this study showed that accurate monitoring of wheat yellow rust at the canopy scale can be achieved using UAV hyperspectral imagery. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Feature**} & \multirow{2}{*}{**Class**} & \multicolumn{6}{c}{**Classification Accuracy (\%)**} \\ \cline{3-10}  & & **7 DAI** & **14 DAI** & **21 DAI** & **28 DAI** & **31 DAI** & **34 DAI** & **41 DAI** \\ \hline \multirow{2}{*}{WRSFs} & Healthy & 73.5 & 81.2 & 88.6 & 95.4 & 96.9 & 95.2 & 96.1 \\  & Infected & 80.5 & 84.8 & 79.8 & 92.7 & 98.2 & 98.4 & 98.5 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Changes in average monitoring accuracy of yellow rust monitoring model after infestation, expressed in days after infection (DAI). At the regional scale, spectral indices sensitive to LAI and LCC were constructed based on disease-sensitive spectral bands at the leaf and canopy scale, and then the yellow rust monitoring model KPCA-SVM was developed. The monitoring results are shown in Figure 11. The model monitoring results were generally consistent with our field survey. Wheat yellow rust mainly occurred around the Jialing River in Ningqiang county, an area that provides ideal environmental conditions for the infection and development of yellow rust. Table 10 gives the confusion matrix and kappa value corresponding to the monitoring results. It can be found that the overall accuracy of the monitoring model is 84.4% and the kappa coefficient is 0.742, thus achieving accurate monitoring of yellow rust.