# Intelligently Counting Agricultural Pests by Integrating SAM with FamNet
## 4 Discussion and Conclusions
### Discussion  
Although the SAM auxiliary counting system proposed in this study effectively harnesses the powerful generalization performance of large models, it still faces limitations and challenges. Firstly, it heavily relies on the accuracy of the large model. While it benefits from the strong domain adaptation capability provided by the large model, it also faces issues encountered by large models, including the problem of model hallucination and failure in specific sample scenarios. These limitations will restrict the future development of the current model. Secondly, the introduction of large models comes at a cost, which means higher computational resources and increased device costs. This will impact the practical implementation of the model. Finding ways to use large models in a more lightweight manner, utilizing acceptable resources to achieve better results, remains a topic for further exploration. Furthermore, the data currently processed by the model still remain at the  
Figure 8: Comparative experimental results between four different architectures.  
level of two-dimensional images. Although the model has a large number of parameters, it is still limited by the lack of three-dimensional information in two-dimensional images. Introducing three-dimensional information is expected to significantly improve the upper limit of the current model. Lastly, the proposed method in the early stage of low-sample counting can lead to a significant decrease in subsequent detection accuracy if incorrect samples are stored in the knowledge base as prompts. Even if a forgetting mechanism is introduced to stabilize the quantity of prompts, excessive expansion due to model prompts can still cause a decrease in performance.  
Conceptualization, J.Q. and X.D.; methodology, J.Q., X.D., Y.L. and J.X.; software, J.Q.; validation, J.Q., X.D. and Y.L.; formal analysis, X.D., Y.L. and J.X.; investigation, X.D., Y.L. and J.X.; resources, J.Q.; data curation, X.D., Y.L. and J.X.; writing--original draft preparation, J.Q.; writing--review and editing, X.D.; visualization, J.Q.; supervision, J.Q.; project administration, J.Q., X.D., Y.L. and J.X.; funding acquisition, X.D. All authors have read and agreed to the published version of the manuscript.  
This research was funded by Key-Area Research and Development Program of Guangdong Province, grant number 2023B0202090001; National Natural Science Foundation of China, grant number 32371984; Key-Area Research and Development Program of Guangdong Province, grant number 2019B020214003; Laboratory of Lingnan Modern Agriculture Project, grant number NT2021009; Key-Area Research and Development Program of Guangzhou, grant number 202103000090; Key-Areas of Artificial Intelligence in General Colleges and Universities of Guangdong Province, grant number 2019 KZDZX1012; The leading talents of Guangdong province program, grant number 2016 LJ06G689; China Agriculture Research System, grant number CARS-15-23 and The 111 Project, grant number D18019.  
Data Availability Statement: Data sets for the study period can be obtained from the corresponding author upon reasonable request. These data include insect disease information for six species. However, the availability of these data is restricted and their usage is subject to a license agreement with the current study. Therefore, they are not publicly accessible.  
The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
Figure A2: Results of initial model + SAM in different rounds.  
Figure A3: Results of initial model + SAM + Forget in different rounds.