# A Framework for Agricultural Intelligent Analysis Based on a Visual Language Large Model
## 3 Experiments and Analysis of Results
### The Results of Five Recognition Tasks  
AgriVLM evaluates the prediction effectiveness of the model on different tasks through four metrics: accuracy, precision, recall, and F1-score. The performance of the model on different tasks such as crop growth stage, disease recognition, and poultry recognition are shown in Table 4.  
\begin{table}
\begin{tabular}{l c c c c c} \hline \hline  & **Cabbage** & **Strawberry** & **Chili Pepper** & **Tomato** & **Poutry** \\ \hline Accuracy & 0.925 & 0.9739 & 0.9756 & 0.934 & 0.9952 \\ Precision & 0.9348 & 0.97 & 0.9782 & 0.94 & 0.9954 \\ Recall & 0.925 & 0.97 & 0.9713 & 0.93 & 0.9951 \\ F1 & 0.9242 & 0.97 & 0.9745 & 0.93 & 0.9952 \\ \hline \hline \end{tabular}
\end{table}
Table 4: The results of five different recognition tasks.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & **Cabbage** & **Strawberry** & **Chili Pepper** & **Tomato** & **Poutry** \\ \hline lr & 0.0002 & 0.0002 & 0.0002 & 0.0002 & 0.0002 \\ batch\_size & 16 & 16 & 16 & 16 & 16 \\ Trian\_iters & 1500 & 2000 & 2000 & 10,000 & 400 \\ layer & 4 & 4 & 4 & 6 & 2 \\ r & 10 & 10 & 10 & 10 & 10 \\ \hline \hline \end{tabular} Where lr represents the learning rate of the training process, trian_iters is the number of iterations in the training, layer represents the number of LoRA fine-tuning layers, and r is the rank of LoRA fine-tuning.  
\end{table}
Table 3: The hyperparameter settings for different recognition tasks.  
As can be seen in Table 4, the recognition accuracy of AgriVLM in different agricultural application scenarios has reached more than 90%. The accuracy of the five tasks is 0.925, 0.9739, 0.9756, 0.934, and 0.9952, respectively. The AgriVLM model has an excellent performance in recognition tasks in different application scenarios in agriculture, demonstrating extremely high reliability.  
In order to show the ability of the model to recognize different categories more intuitively, the confusion matrix of the results of each task is plotted, which is shown in Figure 3. In the confusion matrix, the rows and columns represent the predicted and true labels, respectively. The diagonal elements of the confusion matrix indicate the number of samples in each category correctly recognized by the model, and the higher the value of the diagonal elements, the higher the recognition accuracy of the model for the corresponding category. On the contrary, the non-diagonal elements indicate the number of samples in one category that the model incorrectly recognized as other categories [30]. Therefore, it can be observed from the confusion matrices of the five tasks in Figure 3 that the model performs quite well and achieves an accurate recognition of different categories, illustrating the effectiveness of the model.  
In addition, to validate the advantages of the model, we also compared it with classic visual language models such as Large Language and Vision Assistant (LLaVA) [31] and Flamingo [32]. The experimental results are shown in Table 5. From this table, it can be seen that our method has a higher prediction accuracy than existing methods. More specifically, in the poultry recognition task where the difference is obvious, the diversity between the  
Figure 3: The confusion matrices of five different recognition tasks. (**A**) Cabbage growth stage. (**B**) Strawberry growth stage. (**C**) Chili pepper disease. (**D**) Tomato disease. (**E**) Poultry recognition.  
three methods is not very big, but in other recognition tasks with less significant differences, especially in the recognition of tomato diseases, our method is significantly better than other methods.