# Research on Intelligent Recognition for Plant Pests and Diseases Based on Improved YOLOv8 Model
## 5 Analysis of Experimental Results
### Comparative Experiment  
With the aim of verifying the significant advantages of the improved model proposed in this paper compared to other models, multiple groups of comparative experiments are set up. The Faster R-CNN, YOLOv7, YOLOv8, and the improved YOLOv8 are employed to rice and cotton datasets, and a comparative analysis of visualization results and evaluation  
Figure 9: The confusion matrices generated by the proposed improved YOLOv8 model and original YOLOv8 model.  
results is performed. To ensure the reliability of the comparison results, all experimental parameters are kept the same, along with identical experimental environment settings.  
In the comparison stage of the visualization results, each typical disease and pest is tested by experimental models. Different models present different effects on the detection of typical diseases and insect pests. The results are shown in Figure 10.  
Figure 9 indicates the testing results of each model in different typical diseases and pest recognition tasks, respectively. Through the comparison analysis of the experiment, the overall performance of the Faster R-CNN model is poor. The main problems include the low matching accuracy of predicted bounding boxes and instances of missed detections. The model also exhibits different detection effects on the rice and cotton datasets. Compared to the Faster R-CNN, YOLOv7 has a better overall performance, but there are still some missed examinations, and the performance is unstable during the testing of different disease and pest categories. YOLOv8 shows good performance, but it has lower confidence levels for certain disease and pest categories, and its results for detecting small targets are subpar. Based on analysis of typical and numerous samples, the improved YOLOv8 proposed in this paper shows higher confidence in target detection, better matching accuracy of predicted boxes, fewer missed detections, and more comprehensive detection of small  
Figure 10: Recognition results of different models for typical diseases and pests.  
targets compared to other detection models. In conclusion, the improved model proposed in this paper demonstrates superior visual detection results.  
In the comparison stage of the evaluation results, mAP values, F1 values, and FPS values are used to measure the performance of different models. The results are shown in Table 3.  
Analyzing the above, the Faster R-CNN performs poorly compared to other models in terms of accuracy and speed. Compared to YOLOv8, YOLOv7 has faster detection speeds but lower detection accuracy, and its overall performance is not outstanding. This is because the YOLOv8 model has a more complex network structure with more calculated parameters, resulting in a loss of detection speed while maintaining detection accuracy. From the perspective of the evaluation indicators and comparing the improvement percentages, in the rice dataset, the improved model in this paper achieves a 7.13% increase in mAP compared to YOLOv8 and a 44.48% increase compared to the Faster R-CNN. The F1 score is improved by 13.78% over YOLOv8 and 17.77% over the Faster R-CNN. In the cotton dataset, the improved model in this paper achieves a 4.21% increase in mAP compared to YOLOv8 and a 7.84% increase compared to Faster R-CNN. The F1 score is improved by 9.86% over YOLOv8 and 20% over the Faster R-CNN. The above computed result fully demonstrates that the proposed model in this paper makes significant advancements in detection accuracy compared to other traditional mainstream methods.  
In summary, combining with visualization results and evaluation results, the proposed model in this paper outperforms traditional YOLO models notably in small defect detection and accurate matching of detection boxes. It exhibits higher detection accuracy and efficiency, demonstrating certain superiority.