## 3 Results
### Feasibility for Creating Prescription Maps
In practical applications, the selection of the optimal buffer distance depends on factors such as infestation pattern, planting swath, and the producer's risk tolerance. A buffer size of 10 m is practical, especially if Sentinel-2 imagery is used. If the farmer wants to make sure all potential root areas are treated or if the application equipment has a wider swath, a larger buffer can be used. At any rate, site-specific fungicide treatment significantly reduces the fungicide use and increases the farming profit. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{3}{*}{**Field**} & \multicolumn{6}{c}{**Treatment Area (m\({}^{2}\))**} \\ \cline{2-9}  & \multicolumn{2}{c}{**Airborne Image**} & \multicolumn{6}{c}{**Sentinel-2A Image**} \\ \cline{2-9}  & \multicolumn{2}{c}{**Neural Network**} & \multicolumn{2}{c}{**Spectral Model**} & \multicolumn{2}{c}{**Texture Model**} & \multicolumn{2}{c}{**Spectral-Texture Model**} \\ \cline{2-9}  & **m\({}^{2}\)** & **\%** & **m\({}^{2}\)** & **\%** & **m\({}^{2}\)** & **\%** & **m\({}^{2}\)** & **\%** \\ \hline
1 & 116,491 & 18.54 & 114,256 & 18.19 & 175,743 & 27.97 & 139,645 & 22.23 \\
2 & 52,167 & 7.52 & 21,678 & 3.12 & 32,212 & 4.64 & 34,640 & 4.99 \\
3 & 34,568 & 3.97 & 13,758 & 1.58 & 47,750 & 5.48 & 35,137 & 4.03 \\
4 & 428,797 & 46.11 & 339,681 & 36.53 & 567,137 & 60.98 & 468,346 & 50.36 \\
5 & 83,240 & 23.37 & 65,462 & 18.38 & 83,894 & 23.55 & 81,363 & 22.84 \\
6 & 321,353 & 75.78 & 335,307 & 79.07 & 362,983 & 85.60 & 332,339 & 78.37 \\
7 & 207,813 & 78.22 & 197,849 & 744.74 & 208,800 & 78.59 & 193,535 & 72.85 \\
8 & 115,322 & 60.85 & 113,833 & 60.06 & 118,242 & 62.39 & 115,817 & 61.11 \\ Total & 1,359,752 & 31.20 & 1,201,822 & 27.58 & 1,596,761 & 36.64 & 1,400,823 & 32.14 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Treatment areas of prescription maps generated from airborne and Sentinel-2A images for Fields 1–8. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multirow{2}{*}{**Category**} & \multicolumn{3}{c}{**Prescription Area (m\({}^{2}\))**} & **UA** & **OA** \\ \cline{3-6}  & & **Nontreatment** & **Treatment** & **Total** & & \\ \hline \multirow{3}{*}{Spectral} & Nontreatment & 2,877,719 & 278,789 & 3,156,508 & 91.17\% & 90.83\% \\  & Treatment & 120,858 & 1,080,964 & 1,201,822 & 89.94\% & \\  & Total & 2,998,577 & 1,359,753 & 4,358,330 & & \\  & PA & 95.97\% & 79.50\% & & & \\ \hline \multirow{3}{*}{Texture} & Nontreatment & 2,599,780 & 161,789 & 2,761,569 & 94.14\% & 87.14\% \\  & Treatment & 398,756 & 1,198,005 & 1,596,761 & 75.03\% & \\  & Total & 2,998,536 & 1,359,794 & 4,358,330 & & \\  & PA & 86.70\% & 88.10\% & & & \\ \hline \multirow{3}{*}{Spectral-texture} & Nontreatment & 2,790,556 & 166,951 & 2,957,507 & 94.36\% & 91.40\% \\  & Treatment & 208,022 & 1,192,801 & 1,400,823 & 85.15\% & \\ \cline{1-1}  & Total & 2,998,578 & 1,359,752 & 4,358,330 & & \\ \cline{1-1}  & PA & 93.06\% & 87.72\% & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Error matrices for prescription maps generated by binary logistic regression (BLR) models for the whole study area. Figure 9 illustrates the OA, PA, and UA for the treatment category of the prescription maps generated from the Sentinel-2A images by the three BLR models for Fields 1-8. From Figure 9a, the spectral model achieved the best OA for Fields 1, 3, 5, and 7, followed by the spectral-texture model and the texture model, while the spectral-texture model achieved the best OA for Fields 2, 4, 5, and 8, followed by the spectral model and the texture model. From Figure 9b, the PA for the treatment category based on the spectral-texture model was the highest for Fields 1-5, while the PA for  
Figure 8: Overlaid prescription maps from airborne and Sentinel-2A images for Fields 1–8 and the zoomed in rectangle scenes for the areas marked on the maps are shown at the right.