# Classification Models for Nitrogen Concentration in Hop Leaves Using Digital Image Processing
## 3 Results
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Variables**} & \multicolumn{2}{c}{**Mobile Phone Images**} & \multicolumn{4}{c}{**Scanner Images**} \\ \cline{2-7}  & **G1** & **G2** & **G1** & **G2** & **G1** & **G2** & **G2** & **_p_ \\  & **(n1 = 12)** & **(n2 = 60)** & **\(p\)** & **(n1 = 78)** & **(n2 = 72)** & **_p_** \\ \hline \multirow{4}{*}{**R**} & 0.400 (0.073) & 0.372 (0.049) & _0.21_\({}^{\ddagger}\) & 0.438 (0.106) & 0.373 (0.074) & \(<\)_0.01_\({}^{\ddagger}\) \\  & 0.37 & 0.362 & & 0.403 & 0.38 & & \\  & (0.341–0.561) & (0.309–0.603) & & (0.309–0.782) & (0.119–0.496) & & \\ \hline \multirow{4}{*}{**G**} & 0.465 (0.030) & 0.441 (0.022) & \(<\)_0.01_\({}^{\ast}\) & 0.506 (0.074) & 0.463 (0.082) & \(<\)_0.01_\({}^{\ast}\) \\  & 0.461 & 0.438 & & 0.499 & 0.477 & & \\  & (0.432–0.506) & (0.410–0.514) & & (0.384–0.715) & (0.173–0.620) & & \\ \hline \multirow{4}{*}{**B**} & 0.145 (0.026) & 0.174 (0.033) & \(<\)_0.01_\({}^{\ast}\) & 0.076 (0.026) & 0.079 (0.026) & _0.44_\({}^{\ast}\) \\  & 0.141 & 0.176 & & 0.071 & 0.074 & & \\  & (0.110–0.189) & (0.105–0.277) & & (0.033–0.134) & (0.031–0.147) & & \\ \hline \multirow{4}{*}{**NR**} & 0.394 (0.042) & 0.376 (0.032) & _0.08_\({}^{\ast}\) & 0.425 (0.031) & 0.403 (0.040) & \(<\)_0.01_\({}^{\dagger}\) \\  & 0.376 & 0.372 & & 0.42 & 0.405 & & \\  & (0.360–0.482) & (0.326–0.489) & & (0.376–0.520) & (0.268–0.497) & & \\ \hline \multirow{4}{*}{**NG**} & 0.463 (0.025) & 0.450 (0.021) & _0.06_\({}^{\ast}\) & 0.499 (0.019) & 0.505 (0.033) & _0.17_\({}^{\ddagger}\) \\  & 0.466 & 0.452 & & 0.503 & 0.511 & & \\  & (0.417–0.502) & (0.401–0.501) & & (0.441–0.531) & (0.397–0.548) & & \\ \hline \multirow{4}{*}{**NB**} & 0.142 (0.03) & 0.174 (0.03) & \(<\)_0.01_\({}^{\ast}\) & 0.076 (0.033) & 0.092 (0.062) & _0.05_\({}^{\dagger}\) \\  & 0.141 & 0.178 & & 0.069 & 0.077 & & \\  & (0.100–0.191) & (0.102–0.194) & & (0.028–0.153) & (0.024–0.332) & & \\ \hline \multirow{4}{*}{**GRRI**} & 1.193 (0.154) & 1.209 (0.121) & _0.69_\({}^{\ast}\) & 1.182 (0.095) & 1.265 (0.112) & \(<\)_0.01_\({}^{\ast}\) \\  & 1.245 & 1.216 & & 1.203 & 1.322 & & \\  & (0.867–1.340) & (0.835–1.436) & & (0.850–1.300) & (0.902–1.499) & & \\ \hline \multirow{4}{*}{**MPRI**} & \(-\)0.082 (0.072) & \(-\)0.091 (0.052) & _0.62_\({}^{\ast}\) & \(-\)0.080 (0.043) & \(-\)0.113 (0.047) & \(<\)_0.01_\({}^{\ast}\) \\  & \(-\)0.108 & \(-\)0.097 & & \(-\)0.091 & \(-\)0.091 & \(-\)0.125 & \\  & (\(-\)0.145–0.072) & (\(-\)0.177–0.091) & & (\(-\)0.129–0.082) & (\(-\)0.197–0.058) & & \\ \hline \multirow{4}{*}{**YBA**} & 17.7 (35.8) & 7.3 (19.3) & _0.35_\({}^{\dagger}\) & 4.5 (12.2) & 4.2 (13.5) & _0.87_\({}^{\ast}\) \\  & 2.2 & 1.3 & & 1.3 & 0.7 & \\  & (1.1–97.9) & (0.4–100.0) & & (0.6–94.8) & (0.2–68.2) & & \\ \hline \hline \end{tabular}
\end{table} 
Table 3: _Cont._and Dmin for both mobile phone and scanner images, respectively. A high correlation was also observed between the GRRI and MPRI leaf indices and the colors R and G, a fact directly associated with the calculation methodology of those indices, which uses the R and G variables for value composition. The strong correlation between the variable R and the variable YBA is due to the red color reflection caused by chlorosis and the damaged area. Concomitantly with the correlation matrices, the analysis and selection of variables through VIF were performed. For the set derived from the significant variables of the mobile phone images, the remaining variables were G and B (VIF = 1.147). However, for the data obtained from printer images (R, G, NB, GRRI, and MPRI), the remaining variables were G, NB, and GRRI (with VIF values of 5.051; 4.890 and 1.434, respectively), since NR, MPRI, and GRRI depend on the R variable for their calculation. In set 2, which included all variables except the highly correlated ones, the remaining variables after VIF analysis were similar for both databases, mobile phone and scanner images.