## 3 Results and Discussion
### Classification and Validation  
The significant bands for the classification of weeds and sorghum were successfully analyzed using linear discriminant analysis (LDA). In 2013, the significant bands were tested for their classification accuracy. The accuracy of _Malva_ sp. (MW) decreased to 90%, while sorghum reached more than 90% accuracy in week two (Table 3, calibration section). This shows that the classification accuracy was high (>80%). Meanwhile, the sorghum accuracy increased to 92% in week three, then finally all weeds showed 100% classification accuracy during week four. Here, the larger the size of the weeds, the more accurate the classification. The weed classification was tested using independent data (Table 3, validation section). In week two, the classification accuracy for sorghum increased to 70%, reaching 75% in week three; however, it decreased to 4% during week four in 2013. The same was true for _Malva_ sp. (MW), where the classification was 75% accurate in week two but increased to 100% in weeks three and four. The accuracy for _Amarant thus macrocarpus_ (AM) decreased to 80% in week three, although for _Urochoa panicoides_ (LS) remained at 100% from week two to week four. Although the accuracy of _Cyperus rotundus_ (NG) decreased from 100% to 67% in \begin{table}
\begin{tabular}{l c c c c c c} \hline \hline \multirow{2}{*}{**Species**} & \multicolumn{3}{c}{**Calibration Data (\%)**} & \multicolumn{3}{c}{**Validation Data (\%)**} \\ \cline{2-7}  & **Wk 2** & **Wk 3** & **Wk 4** & **Wk 2** & **Wk 3** & **Wk 4** \\ \hline _Ipomea plebeia_ (B) & 100 & 100 & 100 & 100 & 100 & 100 \\ \hline _Urochoa panicoides_ (LS) & 100 & 100 & 100 & 89 & 100 & 100 \\ \hline _Cyperus rotundus_ (NG) & 100 & 100 & 100 & 100 & 100 & 100 \\ \hline _Portulaca oleraca_ (PG) & 100 & 100 & 100 & 100 & 100 & 100 \\ \hline Mean & 100 & 100 & 100 & 97 & 100 & 100 \\ \hline \multicolumn{7}{l}{High accuracy (>80), Wk = Week} \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Results for the classification of 20 significant bands in 2014. Adapted from [14]. \begin{table}
\begin{tabular}{l c c c c c c} \hline \hline \multirow{2}{*}{**Species**} & \multicolumn{3}{c}{**Calibration Data (\%)**} & \multicolumn{3}{c}{**Validation Data (\%)**} \\ \cline{2-7}  & **Wk 2** & **Wk 3** & **Wk 4** & **Wk 2** & **Wk 3** & **Wk 4** \\ \hline _Amaranthus macrocarpus_ (AM) & 100 & 100 & 100 & 100 & 83 & 80 \\ \hline _Urochoa panicoides_ (LS) & 100 & 100 & 100 & 100 & 100 & 100 \\ \hline _Malva_ sp. (MW) & 88 & 100 & 100 & 75 & 100 & 100 \\ \hline _Cyperus rotundus_ (NG) & 100 & 100 & 100 & 100 & 67 & 100 \\ \hline _Sorghum bicolur_ ([PERSON] (SG) & 92 & 92 & 100 & 67 & 75 & 71 \\ \hline Mean & 96 & 98 & 100 & 88 & 85 & 90 \\ \hline \multicolumn{7}{l}{High accuracy (>80); Wk = Week} \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Results for the classification of 20 significant bands in 2013. Adapted from [14]. week three, it improved by up to 100% in week four. The classification accuracy decreased by 3-4% in week four for sorghum. Overall, the results proved that the classification improved from week two to week four in 2013. In 2014, the classification was 100% successful for most of the significant bands for each week (both calibration and validation data), except for the 89% classification accuracy for _Urochao panicoides_ (LS) in week two. The independent data tested in 2013 using linear discriminant analysis (LDA) produced accurate results, as highlighted in Table 3. Meanwhile, stepwise linear discriminant analysis (SLDA) was used to test the dependent data and the classification accuracy was found to be slightly lower. The classification accuracy increased to 100% in 2014 using SLDA, as shown in Table 4. The accuracy of the classification for the weed discrimination using LDA and support vector machine (SVM) has been increased at 15 bands compared to the eight bands at more than 90% and 85%, respectively [24]. Similar results were found for the separation between weed species using LDA, which has a high degree of separation [25].