## 4 Experiment
### Experiments on Turkey Plant  
The Turkey Plant disease and pest dataset was established by the Agricultural Faculty of Bingol and Inonu Universities in Turkey [52]; we call it Turkey Plant in this paper for the sake of simplicity. It is designed to promote the research of plant disease and pest recognition. There are 4447 images of size 4000 \(\times\) 6000 from 15 categories. The minimal and maximum sample number for an image class are 69 and 1110, respectively. Example images for each class are shown in Figure 12. The image background is very complex and in each class, for example, Apple Aphis spp, some images only contain many apple leaves inside, some images contains apple fruits and leaves inside, some images focus on the tree branch and a few leaves and some images do not have leaves. Therefore, it is hard to identify different plant diseases correctly; that is to say, Turkey Plant is an extremely challenging plant disease dataset. In order to test the performance of our method on Turkey Plant, we conducted an experiment to compare our method and other competing methods, the comparison results are presented in Table 10; our proposed method achieves the highest recognition accuracy, 96.19%, which is the average value of 95.82%, 97.01%, 96.27%, 96.49% and 95.37% for the five repeated experiments. More importantly, our method is the only method with a recognition rate of more than 90%, which outperforms the second best method by nearly 10%. Compared with the results on previous plant datasets, the performance of the handcrafted methods decrease heavily; the reason is that those methods extract leaf shape information; however, it is difficult to estimate the shape  
Figure 11: Example images from Leafsnap dataset; four species are shown. \begin{table}
\begin{tabular}{c c c} \hline \hline  & **Methods** & **Accuracy** & **Year** \\ \hline \multirow{4}{*}{Hand} & SC+DP [9] & 59.21 & 2002 TPAMI \\  & IDSC+DP [10] & 46.81 & 2007 TPAMI \\  & MDM [24] & 39.66 & 2012 TIP \\  & MTCD [13] & 50.59 & 2016 ECAI \\  & MFD [14] & 61.07 & 2019 SPIC \\  & MTD+LBP-HF [27] & 73.65 & 2021 PR \\ \hline \multirow{4}{*}{DNN} & AlexNet+relu5 [58] & 86.61 & 2012 NIPS \\  & VGG16+relu5\_2 [54] & 89.86 & 2015 ICLR \\  & Dual-Path CNN [19] & 88.54 & 2017 ICIP \\  & Deep Plant [18] & 86.12 & 2017 PR \\  & HGO-CNN [20] & 86.57 & 2018 TIP \\  & MaskCOV [35] & 90.12 & 2021 PR \\  & IMTD+relu5\_2 [38] & 91.29 & 2023 ESA \\  & Ours & **93.40**(\(\pm\)0.69) & - \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Classification accuracy (%) comparison on Leafsnap dataset with identical settings. features for the leaves in the Turkey Plant dataset. It is obvious that our learned plant features are very effective and discriminative for plant leaf disease recognition even if the plant images have a complex background, scale variations and viewpoint rotation. Moreover, we display the confusion matrix for the recognition results on Turkey Plant in Figure 13, in which _ci_ means the _i_-th class. Among the 15 categories, there are 12 categories with accuracy beyond 90% and 3 categories with accuracy equal to 100%. The misclassified samples of each category are 6, 2, 10, 6, 1, 3, 0, 1, 0, 4, 1, 2, 2, 0, 2 respectively. The confusion matrix can reveal that our method is robust against dataset unbalancing. \begin{table}
\begin{tabular}{l l l l} \hline \hline  & **Methods** & **Accuracy** & **Year** \\ \hline \multirow{4}{*}{Hand} & SC+DP [9] & 22.17 & 2002 TPAMI \\  & IDSC+DP [10] & 19.55 & 2007 TPAMI \\  & MDM [24] & 17.60 & 2012 TIP \\  & MTCD [13] & 18.50 & 2016 ECAI \\  & MFD [14] & 20.22 & 2019 SPIC \\  & MTD+LBP-HF [27] & 28.61 & 2021 PR \\ \hline \multirow{4}{*}{DNN} & AlexNet+relu5 [58] & 80.67 & 2012 NIPS \\  & VGG16+relu5\_2 [54] & 86.59 & 2015 ICLR \\  & Dual-Path CNN [19] & 83.56 & 2017 ICIP \\  & Deep Plant [18] & 82.58 & 2017 PR \\  & HGO-CNN [20] & 82.43 & 2018 TIP \\  & MaskCOV [35] & 86.14 & 2021 PR \\  & IMTD+relu5\_2 [38] & 86.83 & 2023 ESA \\  & Ours & **96.19**(\(\pm\)0.63) & - \\ \hline \hline \end{tabular}
\end{table}