# A Simple Method to Estimate Weed Control Threshold by Using RGB Images from Drones
## 3 Results  
The results of the TGC measured with Canopeo were very close to those obtained with Erdas (Table 2). The results from the two software methods were not statistically different, neither at 10 m (\(p=0.619\)), nor 20 m (\(p=0.108\)), and the deviation measured ranged, in absolute value terms, from 3 to 12%. The Pearson correlation coefficient was very high in both cases (\(r=0.87\) and \(0.90\) for 10 and 20 m, respectively, \(p<0.01\)). \begin{table}
\begin{tabular}{c c c} \hline
**Quality Indices** & **Formula** & **Optimum** \\ \hline Root Mean Square Error & \(RMSE=[\sum_{i=1}^{n}(P_{i}-O_{i})\,/n]^{0.5}\) & 0 \\ \cline{2-3}  & \(RMSE/\tilde{\text{O}}=[\sum_{i=1}^{n}(P_{i}-O_{i})\,/\,n]^{0.5}*100/\tilde{ \text{O}}\) & 0 \\ \hline Mean Absolute Error & \(MAE=\sum_{i=1}^{n}|P_{i}-O_{i}|\) & 0 \\ \hline Modelling Efficiency & \(EF=(\sum_{i=1}^{n}\,(O_{i}-\text{O})^{2}-\sum_{i=1}^{n}\,\) & 1 \\  & \((P_{i}-O_{i})^{2}/\sum_{i=1}^{n}\,(O_{i}-\text{O})\) & 1 \\ \hline Coefficient Residual Mass & \(CRM=(\sum_{i=1}^{n}O_{i}-\sum_{i=1}^{n}P_{i})/\sum_{i=1}^{n}O_{i}\) & 0 \\ \hline \end{tabular}
\end{table} 
Table 1: The indices used to evaluate the goodness of fit (\(\text{P}_{i}=\text{estimated values}\), \(\tilde{\text{O}}_{i}=\text{observed values}\), \(\tilde{\text{O}}=\text{the average of observed data}\), and \(n=\text{number of the observations}\)). Figure 3: The Erdas method: (**a**) orthorectified RGB image of a control plot, (**b**) the binary image of the control plot where the maize was manually masked, (**c**) the result of the classification of the masked image using the weed signature set. In Table 3, the values of the M1-MGC\({}_{\rm{WP+CP}}\) and M1-WGC\({}_{\rm{WP+CP}}\) are compared with those obtained from Erdas for the same plots. The estimated values were significantly different from Er-MGC\({}_{\rm{WP+CP}}\) and Er-WGC\({}_{\rm{WP+CP}}\) at both 10 and 20 m. In particular, M1-MGC\({}_{\rm{WP+CP}}\) underestimated the values of Erdas by 0.15 m\({}^{2}\) (\(-\)20%) and 0.32 m\({}^{2}\) (\(-\)30%), at 10 and 20 m, respectively. Moreover, the correlation levels between estimated and measured values were very low at 10 m (\(r\) = 0.36) or nonexistent at 20 m (\(r\) = 0.06). Conversely, we observed a better correlation between the M1-WGC\({}_{\rm{WP+CP}}\) and the Er-WGC\({}_{\rm{WP+CP}}\), even if the two quantities remain statistically different from each other for both the flight heights, the deviation was still larger (\(+\)106 and \(+\)136%, for 10 and 20 m, respectively), than that observed for the maize. Instead, the resulting values of r were very high (significant for \(p\) < 0.01), equal to 0.94 and 0.91 for 10 and 20 m, respectively. Moving on to M2 (Table 4), the values of the M2-MGC\({}_{\rm{WP+CP}}\) results were statically equivalent to those from Erdas at both 10 (\(p\) = 0.71) and 20 m (\(p\) = 0.28), whereas the Pearson correlation coefficient results were not significant. The values of \(r\) were the same calculated for M1-MGC\({}_{\rm{WP+CP}}\) since they were obtained as the product of NMWP+CP by a constant value (AM for M1 and AM* for M2). In addition, the resulting differences between M2-WGC\({}_{\rm{WP+CP}}\) and Er-WGC\({}_{\rm{WP+CP}}\) were further reduced. At 10 m, the deviation was equal to +0.01 m\({}^{2}\) (\(+\)1%) and \(-\)0.11 m\({}^{2}\) (\(-\)10%) for 10 and 20 m, respectively. These values implied statistical significance for the former (\(p\) = 0.013), but not for the latter (\(p\) = 0.135). The values of r were, in both cases, high (\(r\) > 0.90) and significant at (\(p\) < 0.01) (the values of \(r\), were, even in this case, equal to those calculated for M2-WGC\({}_{\rm{WP+CP}}\) for the same reason reported above).