# Generating Image Descriptions of Rice Diseases and Pests Based on DeiT Feature Encoder
## 4 Results and Analysis
### Image Description Results  
The image caption generation model proposed in this paper demonstrated a strong performance, as evident from the selected prediction results shown in Table 4. Compared to the method proposed by [PERSON] [29], our approach can capture more intricate details of plant diseases and pests. In image 1, our model not only accurately predicted the presence of bacterial stripe disease but also described that the entire rice leaf exhibits a withered yellow color, and the disease is serious. In image 2, our method precisely described the yellow stripes indicating the occurrence of sheath blight in the middle section of the leaf. Additionally, in image 3, our model accurately portrayed the grayish-white spots located at the center of the leaf, which are symptomatic of blast disease. Some of the predicted image descriptions align with the descriptions in the dataset, as depicted in image 4, where both exhibited the description \"The body of the planthopper is semicircular\". To assess the generalization ability of the proposed model, we conducted simultaneous tests on a total of 342 authentic field images. The corresponding evaluation metrics and results are presented in Table 5. Inspection of the table reveals a slight decline in the performance of all models across the seven indicators measuring image description quality when applied to the new sample. Moreover, the values of the BLEU-1 to BLEU-4 indicators exhibited a gradual decrease. Notably, our model attained the highest scores on all the indicators. BLEU scores were still high. The results indicate a good grammatical and lexical match between the generated texts and the reference texts. Our model achieved 112.0 CIDEr and 54.1 ROUGE-L, which means that the key information between the generated texts of our model and the reference texts was better matched, and the generated descriptions were more diverse. In addition, the highest METEOR score indicates the overall high quality and fluency of the generated descriptions. The highest scores of these various indicators demonstrated superior performance when applied to the real-world image dataset captured in the field. The prediction accuracy of 10 types of pests and diseases in this model is shown in Table 6, with an average accuracy of 87.67%. Notably, the accuracy for rice bakanae disease was 52.63%, while the remaining categories pertaining to rice pests and diseases exhibited remarkably high prediction accuracy. Specifically, five categories achieved a perfect accuracy of 100%, which demonstrates the model's proficient diagnostic capability in identifying rice pests and diseases. The test outcomes are communicated to users in a textual format and hold potential value as a point of reference for non-experts. \begin{table}
\begin{tabular}{c c c c c c c c} \hline
**Models** & **BLEU-1** & **BLEU-2** & **BLEU-3** & **BLEU-4** & **CIDEr** & **ROUGE-L** & **METEOR** \\ \hline AlexNet & 72.1 & 59.1 & 50.9 & 44.8 & 162.8 & 63.8 & 37.3 \\ VGG16 & 71.1 & 58.3 & 50.5 & 43.8 & 161.6 & 62.5 & 36.0 \\ ResNet101 & 70.4 & 57.8 & 50.0 & 44.4 & 173.1 & 64.5 & 36.9 \\ ResNet18 & 71.8 & 60.1 & 52.3 & 46.4 & 174.9 & 64.8 & 38.2 \\ InceptionV3 & 70.9 & 58.7 & 50.7 & 45.0 & 172.4 & 64.8 & 38.2 \\ MobileNetV2 & 71.7 & 59.5 & 51.5 & 45.3 & 174.1 & 64.3 & 37.5 \\ DeiT & 76.4 & 63.2 & 53.9 & 47.3 & 177.1 & 65.0 & 37.0 \\ \hline \end{tabular} The encoders shown in the above table are pre-trained, and the decoders of the models with CNN encoders are all Attention + LSTM structures.  
\end{table} 
Table 3: Evaluation results of the ablation experiments. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Category** & **Accuracy \%** \\ \hline Rice bacterial blight & 79.49 \\ Rice bacterial streak disease & 82.22 \\ Rice bakanae disease & 52.63 \\ Rice three chemical borers & 89.47 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Accuracy of category prediction on the actual field images.