## 4 Discussion
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Type** & **Name** & **Band Combination** & **OA** & **AA** & \(\mathbf{K_{c}}\) \\ \hline \multirow{3}{*}{
\begin{tabular}{c} Three \\ Dimensions \\ \end{tabular} } & RIDSI & (R\({}_{1051}\), R\({}_{1049}\), R\({}_{747}\)) & 0.833 & 0.820 & 0.783 \\  & RPSI & (R\({}_{519}\), R\({}_{438}\), R\({}_{706}\)) & 0.889 & 0.800 & 0.853 \\  & RIRSI & (R\({}_{732}\), R\({}_{878}\), R\({}_{712}\)) & 0.944 & 0.900 & 0.928 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: _Cont._and Kc = 0.928. The perfect grading performance shows that the proposed sensitive band selection method could be used to grade the damage severity of cotton aphids. Additionally, in [23; 24], the difference between the healthy and infected samples was introduced in disease detection, which contributed to the improvement in the detection results. In the study, the distance from healthy samples were computed, which not only highlighted the difference between the infected and healthy samples, but also suppressed the similar growing conditions between them such as the management practice, weather and soil conditions, etc., thus the distance between healthy cotton and cotton infected by cotton aphids could have great potential in eliminating the influence of the growing conditions to improve the disease severity grading performance. The relationship between NDSI, RSI, and aphid infestation severity was observed with traditional linear regression method at leaf scale in [34], as shown in their contour maps, NDSI of 600-1100 nm and 1400-2000 nm, and RSI of 1850-2100 nm were \"hotspots\" with high coefficients; NDSI (678, 1471) and RSI (1975, 1904) were determined from these sensitive regions. However, in order to be consistent with Landsat Thematic Mapper spectral 1-5, 690-760 nm was not analyzed in their study, which was exactly the red edge region with high relation to many plant diseases and insect pests [23; 24]. Compared with their results, as shown in Figures 3-5, the sensitive spectral index RIRSI (R732, R878, R712) were derived within the full range of 345-1075 nm at the 1 nm interval in our study, which consisted of the 732 and 712 nm bands in the red edge area. Figure 3b,c shows the contour maps of RNDSI and RRSI, which were based on the traditional NDSI and RSI. It was obvious that both of the contour maps had a similar distribution of \"hotspots\", the three-edge regions including the red edge, yellow edge, and blue edge regions as well as 850-900 nm obtained greater KI values. The red areas showed the highest correlation, while the blue areas showed the lowest, which was in accordance with [34]. Whereas the optimal bands of RNDSI and RRSI selected in this study were not the same as that in [34], this might be due to the following. (1) The authors in [34] regarded disease severity grading as a regression problem and their contour maps corresponded to the continuous disease index, while the contour maps in Figure 3 in our study corresponded to the discrete ideal sequence. (2) Compared to the leaf level hyperspectral measurements in their study, the spot level was adopted in this study, and the contour maps were at different scales. (3) In their study, the red edge regions were not considered. After the spectral index reconstruction, the sensitive spectral indices, RDSI (R702, R715), RNDSI (R643, R656), RRSI (R656, R643), RIDSI (R1051, R1049, R747), RPSI (R519, R438, R706), and RIRSI (R732, R878, R712) were selected from each type of RSI on the possible band combination. RRSI (R656, R643) obtained the best performance of the two-band combination with OA = 88.9%, AA = 80%, Kc = 0.855, and RIRSI (R732, R878, R712) showed the best performance with OA = 94.4%, AA = 90%, Kc = 0.928, which demonstrated that the type of differential RISI including the improved differential RISI had a stronger correlation to the damage severity levels than other types of RISIs. Additionally, the red-edge was indicative of crop growth and nutrition status [23].