# Low-Cost Handheld Spectrometry for Detecting Flavescence Doree in Vineyards
## 3 Results
### Classification Performance
The highest accuracy is achieved using features 13 extracted by GA on the fourth week and suggests that spectral bands (374, 392, 395, 416, 479, 534, 622, 654, 683, 692, 713, 756, 798 nm) had the potential to discriminate between healthy and phytoplasma-infected leaves. On the other hand, the NIRScan sensor shows slightly lower accuracy compared to the Hamamatsu sensor where the highest accuracy achieved is 72% by using GA-extracted features on the third and fifth weeks. The key spectral bands (third week: 901, 1108, 1367, 1440, 1456, 1462 nm; fifth week: 905, 933, 989, 1394, 1398, 1476, 1691 nm) were extracted by using the GA method for NIRScan sensor, which shows the potential to discriminate between healthy and phytoplasma-infected leaves with an accuracy of 72%. This study is based on small training samples. The classification accuracy between healthy and diseased leaves also analyzed using LOO cross-validation on the whole dataset and results are summarized in Table 4. When all bands of the Hamamatsu sensor were undertaken, the accuracy values vary between 55% to 72% while for the NIRScan sensor they vary from 43% to 60%. Similar to results based on the training set, the results of LOO cross-validation also show that the Hamamatsu sensor gave slightly better accuracy with 85% accuracy in the second week compared to the NIRScan sensor, where a maximum of 68% accuracy is achieved in the fourth week. Among the two feature selection techniques for the Hamamatsu sensor, the features extracted by the ensemble methods perform slightly better than GA while for the NIRScan sensor, GA is performing better with an accuracy of 68% in the fourth week (1 July 2022). To exploit the full range of spectra (340-1700 nm) by joining spectra of two sensors, the classification results between healthy and diseased leaves were analyzed. Table 5 shows the classification results using all bands, reduced by the ensemble method, and finally, GA and models were assessed by using the test samples and LOO cross-validation on all samples. For the classification results using all bands of joined spectra of test samples, the accuracy ranges from 61% to 72%, while for the reduced bands extracted by the ensemble and GA methods, the accuracy ranges from 61% to 67% and from 61% to 83%, respectively. The seven features extracted by GA show better accuracy with a maximum of 83% in the fourth week (1 July 2022) compared to the features extracted by the ensemble method where a maximum accuracy of 67% is achieved and even using all bands where the maximum accuracy of 72% is achieved in 4 th week (1 July 2022). When the models were assessed by using LOO cross-validation considering all samples, the classification results show slightly lower accuracy compared to results achieved on test samples where the highest accuracy of 77% is achieved by using features extracted by the ensemble method in the fourth week. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Dates** & \multicolumn{2}{c}{**Test Samples Accuracy**} & \multicolumn{4}{c}{**Test Samples Accuracy**} \\ \hline \multirow{4}{*}{**All Bands (516)**} & **Features (100)** & **Features** & \multirow{4}{*}{**All Bands(516)**} & **Features (100)** & **Features** \\  & **Selected by** & **Selected by** & **Selected by** & **Ensemble** & **Selected by** \\  & **Ensemble** & **Method** & & **Method** & **GA Method** \\ \hline
8 June 2022 & 61 & 67 & 67 (9) & 58 & 55 & 60 (14) \\
15 June 2022 & 61 & 61 & 61 (8) & 55 & 58 & 60 (12) \\
21 June 2022 & 67 & 61 & 72 (8) & 55 & 57 & 58 (15) \\
1 July 2022 & 72 & 67 & 83 (7) & 72 & 77 & 72 (20) \\
6 July 2022 & 67 & 67 & 72 (8) & 57 & 58 & 62 (20) \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Classification accuracies (%) using all bands of both sensors (Hamamatsu and NIRScan joined), features selection by the ensemble method, then by the genetic algorithms (GA) technique of both sensors (joined spectra) on test samples and leave-one-out (LOO) cross-validation on the whole dataset.