## 3 Results
### Image Registration Model
The alignment models were applied to a set of baby lettuce images acquired at a distance of 1000 mm from the ground using a multispectral (MS) sensor. The results of the alignment are presented in Figure 5, which shows the raw images of the R, G, and B channels of the MS sensor alongside the corresponding images aligned using the CB, FT, and RG methods. The aligned images demonstrated satisfactory results, with precise alignment of the bands, thus confirming the effectiveness of the proposed methods. The alignment of the multispectral bands played a crucial role in accurately assessing the physiological condition of the crops. This further supports their application in short-range image analysis contexts (Section 3.3). \begin{table}
\begin{tabular}{l l} \hline \hline \multicolumn{1}{c}{**X Offset Model, Band B vs. R**} & \multicolumn{1}{c}{**Y Offset Model, Band B vs. R**} \\ \hline General model: y(x) = a + b/x & General model: y(x) = a + b/x \\ a = \(-\)2.171 (\(-\)3.162, \(-\)1.18) & a = \(-\)5.471 (\(-\)6.427, \(-\)4.516) \\ b = \(-\)7.311 \(\times\) 10\({}^{4}\) (\(-\)7.394 \(\times\) 10\({}^{4}\), \(-\)7.229 \(\times\) 10\({}^{4}\)) & b = 2.873 \(\times\) 10\({}^{4}\) (2.794 \(\times\) 10\({}^{4}\), 2.953 \(\times\) 10\({}^{4}\)) \\ adjrsquare 0.9984 & adjrsquare 0.9903 \\ rmse: 1.2438 & rmse: 1.1996 \\ \hline \multicolumn{1}{c}{**X Offset Model, Band G vs. R**} & \multicolumn{1}{c}{**Y Offset Model, Band G vs. R**} \\ \hline General model: y(x) = a + b/x & General model: y(x) = a + b/x \\ a = \(-\)6.028 (\(-\)6.664, \(-\)5.392) & a = 0.83 (0.1613, 1.499) \\ b = 319.5 (\(-\)211.6, 850.6) & b = 2.898 \(\times\) 10\({}^{4}\) (2.842 \(\times\) 10\({}^{4}\), 2.954 \(\times\) 10\({}^{4}\)) \\ adjrsquare 0.0104 & adjrsquare 0.9952 \\ rmse: 0.7980 & rmse: 0.8393 \\ \hline \multicolumn{1}{c}{**X Offset Model, Band NR vs. R**} & \multicolumn{1}{c}{**Y Offset Model, Band NR vs. R**} \\ \hline General model: y(x) = a + b/x & General model: y(x) = a + b/x \\ a = \(-\)4.199 (\(-\)5.128, \(-\)3.271) & a = \(-\)8.899 (\(-\)9.784, \(-\)8.014) \\ b = \(-\)3.599 \(\times\) 10\({}^{4}\) (\(-\)3.677 \(\times\) 10\({}^{4}\), \(-\)3.521 \(\times\) 10\({}^{4}\)) & b = 3.877 \(\times\) 10\({}^{4}\) (3.803 \(\times\) 10\({}^{4}\), 3.951 \(\times\) 10\({}^{4}\)) \\ adjrsquare 0.9940 & adjrsquare 0.9954 \\ rmse: 1.1650 & rmse: 1.1107 \\ \hline \multicolumn{1}{c}{**X Offset Model, Band RE vs. R**} & \multicolumn{1}{c}{**Y Offset Model, Band RE vs. R**} \\ \hline General model: y(x) = a + b/x & General model: y(x) = a + b/x \\ a = 1.81 (0.8442, 2775) & a = \(-\)6.91 (\(-\)7.78, \(-\)6.04) \\ b = \(-\)7.298 \(\times\) 10\({}^{4}\) (\(-\)7.379 \(\times\) 10\({}^{4}\), \(-\)7.218 \(\times\) 10\({}^{4}\)) & b = \(-\)304.6 (\(-\)1031, 422.1) \\ adjrsquare: 0.9984 & adjrsquare \(-\)0.2151 \\ rmse: 1.2115 & rmse: 1.0918 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Performance of the FT method correlation models of offsets on the X-axis and Y-axis. The 95% confidence limits of the model parameters are given in round brackets. \begin{table}
\begin{tabular}{l l} \hline \hline \multicolumn{1}{c}{**X Offset Model, Band B vs. R**} & \multicolumn{1}{c}{**Y Offset Model, Band B vs. R**} \\ \hline General model: y(x) = a + b/x & General model: y(x) = a + b/x \\ a = \(-\)2.171 (\(-\)3.162, \(-\)1.18) & a = \(-\)6.139 (\(-\)7.659, \(-\)4.618) \\ b = \(-\)7.479 \(\times\) 10\({}^{4}\) (\(-\)7.517 \(\times\) 10\({}^{4}\), \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Distance (m)** & **b/x (Pixel)** & **y(q) = + b/x (Pixel)** & **Relative Error (err\%)** \\ \hline
1 & 28.970 & 23.373 & 517.91\% \\
10 & 2.897 & \(-\)2.700 & 51.77\% \\
20 & 1.449 & \(-\)4.148 & 25.88\% \\
30 & 0.966 & \(-\)4.631 & 17.26\% \\
50 & 0.579 & \(-\)5.018 & 10.34\% \\
100 & 0.290 & \(-\)5.307 & 5.18\% \\ \hline \hline \end{tabular}
\end{table}