# Detection of Helminthosporium Leaf Blotch Disease Based on UAV Imagery
## 4 Conclusions  
In this study, the UAV data collection and concurrent ground investigation were conducted in two wheat fields. The UAV data were analyzed, and its relationship with HLB disease category was investigated. A _CNN_ was applied for HLB disease category classification. The experiments on data preprocessing, classification, and hyper-parameters tuning were conducted. Besides _CNN_, the performance of traditional approaches was evaluated. Color and texture features, as well as vegetation indices, were extracted as hand craft features, and SVM was applied as the classifier. Experimental results showed that the overall accuracy and standard error of the _CNN_ method was 91.43% and 0.83%, which outperformed other methods in terms of accuracy and stabilization. The experimental results in this study demonstrated that the UAV remote sensing can be an effective tool for HLB disease detection. However, other pathologies may also cause similar symptoms as HLB disease. In our current research, it is unfeasible to distinguish different kinds of disease infection. In the future work: (1) We will collect the UAV data of other diseases and build an effective classifier, and (2) we plan to add extra data and use transfer learning to overcome the problem of over fitting.  
Conceptualization, Y.L.; Funding acquisition, J.D. and Y.L.; Investigation, L.Z. and Y.D.; Project administration, J.D.; Software, H.H. and A.Y.; Writing--original draft, H.H.; Writing--review and editing, S.W., H.Z. and Y.Z.  
This research was funded by Educational Commission of Guangdong Province of China for Platform Construction: International Cooperation on R&D of Key Technology of Precision Agricultural Aviation (Grant No. 2015K GJHZ007), the Science and Technology Planning Project of Guangdong Province, China (Grant No.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Method** & **GT/Predicted Class** & **Normal (\%)** & **Light (\%)** & **Medium (\%)** \\ \hline \multirow{3}{*}{_Color Histogram + SVM_} & **normal** & **89.11** & 10.89 & 0.00 \\  & **light** & 13.79 & **84.48** & 1.72 \\  & **medium** & 2.31 & 22.31 & **75.38** \\  & **normal** & **98.57** & 1.43 & 0.00 \\ _LBPH + SVM_ & **light** & 72.07 & **27.59** & 0.34 \\  & **medium** & 48.46 & 46.92 & **4.62** \\  & **normal** & **88.39** & 11.61 & 0.00 \\ VI + SVM & **light** & 8.97 & **88.62** & 2.41 \\  & **medium** & 0.77 & 16.92 & **82.31** \\  & **normal** & **94.33** & 5.67 & 0.00 \\  & **light** & 9.16 & **89.28** & 1.56 \\  & **medium** & 1.80 & 21.48 & **76.72** \\  & **normal** & **93.93** & 6.07 & 0.00 \\ _CNN_ & **light** & 7.93 & **88.62** & 3.45 \\  & **medium** & 0.00 & 13.08 & **86.92** \\ \hline \hline \end{tabular}
\end{table}
Table 6: Confusion matrix of different methods.  
2017A020208046), the National Key Research and Development Plan, China (Grant No. 2016 YTD0200700), the National Natural Science Fund, China (Grant No. 61675003), the Science and Technology Planning Project of Guangdong Province, China (Grant No. 2016A020210100), the Science and Technology Planning Project of Guangdong Province, China (Grant No. 2017B010117010), the Science and Technology Planning Project of Guangdong Province, China (Grant No. 2018A050506073), and the Science and Technology Planning Project of Guangzhou city, China (Grant No. 201707010047).  
**Conflicts of Interest:** The authors declare no conflict of interest.