# A Plant Disease Classification Algorithm Based on Attention MobileNet V2
## 3 Experimental Results and Analysis
### Comparison of Classification Algorithms  
The following index are introduced for performance evaluation,  
\[\left\{ \begin{array}{l}SEN=\frac{TP}{TP+FN}\\ SPE=\frac{TN}{TN+FP}\\ ACC=\frac{TP+TN}{TP+FP+TN+FN}\\ FPF=1-ACC\end{array} \right. \tag{30}\]  
Figure 11: Iteration curve. Figure 10: The effect of Gabor filtering. where _SEN_ reflects the detection performance for real objects. _SPE_ reflects the detection performance for false objects. _ACC_ reflects the ratio of correct test results to all samples in the test results, and _FPF_ reflects the ratio of false test results diagnosed as true objects. The comparison results of algorithms are shown in Tables 3 and 4. It can be seen that the algorithm's results without leaf area extraction are inferior to those with leaf area extraction, indicating that focusing on the area where leaves are located and reducing the influence of surrounding environments yields a positive effect. This verifies the good effect of the proposed leaf segmentation algorithm on subsequent disease clustering. The algorithm based on texture features proposed in Ref. [2] exhibits disease detection capabilities, albeit with slightly lower performance in terms of indicators. Ref. [23] conducts an in-depth analysis of the image characteristics and constructs a K-means model to improve the classification accuracy. Ref. [8] adds ANN based on an SVM classifier to introduce the classification task into multi-dimensional space, realize classification, and achieve certain results. Based on the current mainstream RESNET, Ref. [30] requires 50 iterations to achieve disease detection, but too many network parameters cause large consumption of computing resources. Ref. [37] constructs the traditional MobileNet V2 algorithm and refines the convolution calculation method to effectively boost the calculation speed and performance. Although SEN reaches an impressive 91%, this process is inconsistent with the human cognitive process. On the basis of MobileNet V2, the proposed algorithm adds an attention mechanism module in line with the process of human visual perception. The modification induces a further enhancement in algorithm performance, with superior results achieved. Table 3 illustrates the limited effect of the traditional algorithm [2; 8; 23], but the algorithm based on depth network exhibits commendable performance. Therefore, deep learning algorithms [30; 37; 42; 43] are compared, as shown in Figure 12. The ResNet algorithm [30] involves many parameters, resulting in slower convergence in the training process. In contrast, the MobileNet V2 algorithm [37] introduces a fast convolution change to reduce parameters, along with faster convergence. Improved CNN (ICNN) [42] based on color features focuses on the area where leaves are located and achieves satisfactory results. Generative adversarial networks (GAN) [43] further improve the effect by deeply mining intra-class and inter-class features in small sample situations. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Algorithm** & _SEN_ & _SPE_ & _ACC_ & _FPF_ \\ \hline Texture [2] & 0.71 & 0.26 & 0.79 & 0.21 \\ K-means [32] & 0.79 & 0.24 & 0.85 & 0.15 \\ SVM + ANN [8] & 0.81 & 0.21 & 0.87 & 0.13 \\ ResNet [30] & 0.85 & 0.15 & 0.91 & 0.09 \\ MobileNet V2 [37] & 0.91 & 0.13 & 0.96 & 0.04 \\ ICNN [42] & 0.91 & 0.11 & 0.97 & 0.03 \\ GAN [43] & 0.92 & 0.10 & 0.96 & 0.04 \\ Ours & 0.94 & 0.08 & 0.98 & 0.02 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Algorithm results with leaf region extraction. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Algorithm** & _SEN_ & _SPE_ & _ACC_ & _FPF_ \\ \hline Texture [2] & 0.65 & 0.31 & 0.72 & 0.28 \\ K-means [32] & 0.72 & 0.29 & 0.76 & 0.24 \\ SVM + ANN [8] & 0.75 & 0.25 & 0.80 & 0.20 \\ ResNet [30] & 0.81 & 0.20 & 0.82 & 0.18 \\ MobileNet V2 [37] & 0.84 & 0.18 & 0.84 & 0.16 \\ ICNN [42] & 0.85 & 0.15 & 0.86 & 0.14 \\ GAN [43] & 0.86 & 0.12 & 0.89 & 0.11 \\ Ours & 0.86 & 0.10 & 0.91 & 0.09 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Algorithm results without leaf region extraction.