# Detection of Helminthosporium Leaf Blotch Disease Based on UAV Imagery
## 3 Results and Discussion
### Comparison with Other Methods  
In comparison with the _CNN_ methods, four more algorithms for HLB disease category classification were explored. For the first comparison algorithm, only the color histogram was used for feature extraction, and SVM was applied for multi-class classification. For the second comparison algorithm, only the LBPH method was used for feature extraction and the SVM for multi-class classification. The radius and sampling points were set to 1 and 8, respectively. For the third comparison algorithm, only eight vegetation indices were computed as feature vector and SVM was used as the classifier. For the fourth comparison algorithm, the color histogram, LBPH, and vegetation indices were concatenated as the feature vector, which was used for SVM classification. For all the SVM models, the radial basis function (RBF) was chosen as the kernel function. The penalty parameter was set to 1.0, and the \"one against one\" strategy was used for multi-class classification.  
To evaluate the performance of different methods, overall accuracy (OA) and standard error (SE) were calculated, which were used to measure the classification accuracy and the standard deviation of the experimental results [36]. In our experiments, the training and validation samples were randomly selected from our dataset (Table 3). To minimize the effects of randomness, the sample selection and classification were iterated for 10 times. The quantified measurement of OA, SE, and confusion matrix for 10 consecutive experiments were recorded and averaged. The final OA and SE are shown in Table 5, and its corresponding confusion matrix is demonstrated in Table 6.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Hyper-parameter** & **Learning Rate** & **Momentum** & **Batch Size** & **Weight Decay** \\ \hline Value & 0.001 & 0.9 & 4 & 0 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Hyper-parameters obtained.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Method** & **OA (\%)** & **SE (\%)** \\ \hline _Color Histogram + SVM_ & 85.92 & 1.31 \\ _LBPH + SVM_ & 65.10 & 2.86 \\ _VI + SVM_ & 87.65 & 1.17 \\ _Color Histogram + LBPH + VI + SVM_ & 90.00 & 0.96 \\ _CNN_ & 91.43 & 0.83 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Comparison of overall accuracy (OA) and standard error (SE) of different methods on the validation set.  
From Table 5, it can be observed that the _LBPH + SVM_ approach results in low accuracy. The reason for this result is that the texture features of different categories shared little difference. However, the _Color Histogram + SVM_ approach achieved higher accuracy, since the color difference of different categories was more obvious, as can be observed from Figure 6. The _VI + SVM_ approach obtained an approximate performance with the _Color Histogram_ method since the mathematic computation on different color channels (vegetation index) can be regarded as another kind of color information. The _Color Histogram + LBPH + VI + SVM_ approach further increased the accuracy, since extra effective features help to improve the classification. Experimental results showed that the _CNN_ method achieved the highest OA and lowest SE, which outperformed other methods in terms of accuracy and stabilization. On the other hand, for the recognition of the diseased samples, the _CNN_ method significantly outperformed others, as shown in Table 6. One possible reason for this result was that the _CNN_ emphasis automatic feature learning, which may combine the color and texture features and extract better features for the classification stage.