# A Framework for Agricultural Intelligent Analysis Based on a Visual Language Large Model
## 4 Discussion  
In this study, we designed AgriVLM, which uses ViT to extract image features, then, through Q-Former, connects the image encoder and language model, and finally, through LoRA, performs fine-tuning to achieve an intelligent analysis of agricultural knowledge. We conducted many experiments based on this model. Figure 7 systematically shows the various sub-tasks addressed by the model, including different crop growth stages, diseases, and poultry recognition tasks. The light-blue part in this figure represents the part where the model has already been experimented with, while the light-green part represents the part where experiments can be conducted in the future.  
The experimental results in different sub-tasks show that the model performs quite well with an accuracy of more than 90%. Moreover, by comparing it with other existing classic visual language large models, it was found that the recognition ability of our model is significantly better than other methods, which illustrates the effectiveness of AgriVLM. In addition, in order to explore the influence of fine-tuning methods, prompt designs, and languages on experimental results, we conducted further experiments. We compared the effects of two different fine-tuning methods, LoRA and P-tuning, on the results and found that for simple tasks with obvious category differences, the performance difference between the two is not very obvious, but as the difficulty of the task increases, the performance disparity between the two methods will continue to increase. Then, we compared the  
\begin{table}
\begin{tabular}{p{22.8 pt} p{22.8 pt}} \hline \hline  & Q: What are the main pests and diseases of chili pepper? \\  & A: The main pests and diseases of chili pepper are: bacterial wilt, powdery mildew, soft rot, blight, anthracose, and so on. \\  & Q: What does the harm symptom of chili pepper blight disease have? \\  & A: In chili pepper blight disease plants, the leaves are dark green and brown and necrotic between the leaf veins. The stems become pale or brown in color, and irregular water-stained spots appear at the beginning of the piece, and then expand into round black-brown spots. \\ \hline  & Q: What reasons are wheat leaf yellow caused at present? \\  & A: The reasons of yellowing of wheat leaves are related to insufficient soil fertility, lack of water, and poor root development. \\  & Q: What are the main reasons of wheat collapse? \\  & A: 1. Lack of water: the root system is unable to absorb enough water, resulting in slow growth and easy to collapse; 2. Over-fertilization: over-application of nitrogen fertilizer can lead to oversized and thin leaves, which lose their supporting capacity and are prone to collapse; 3. Low-temperature freezing: wheat is prone to collapse after freezing under cold weather conditions. \\ \hline  & Q: What do the drugs that treat pig parasitic disease have? \\  & A: Drugs for the treatment of swine parasitic diseases include ivermectin, alpendazole, avermectin, and so on. \\  & Q: How many times a year can fattening pigs be vaccinated against swine fever? \\  & A: Fattening pigs can be vaccinated against swine fever twice a year. \\ \hline \hline \end{tabular}
\end{table}
Table 8: Case studies of AgriVLM: agriculture questions and answers.  
effects of prompt designs of different granularities on the results and found that fine-grained prompts with rich contextual information can further improve the accuracy of model predictions. Finally, we compared the impact of different languages on the results and found that due to the differences in pre-training data, English prompts improved the accuracy of the model to a certain extent.