# Research on Innovative Apple Grading Technology Driven by Intelligent Vision and Machine Learning
## 3 Experiment and Result Analysis
### Fruit Stem Detection Experiments
#### 3.2.5 Model Comparative Experiments  
By conducting a series of comparative experiments, this study demonstrated the superiority of the proposed FDNet and FDNet-p models in the task of stem detection. These models were thoroughly compared with state-of-the-art mainstream object detection models across various evaluation metrics. As FDNet is characterized as a lightweight single-stage object detection algorithm, the comparison primarily focused on lightweight single-stage detection algorithms from the YOLO series. These included YOLOv3 [38], YOLOv5 [39], YOLOv6 [40], YOLOv8 [41], YOLOv9 [42], YOLOv10 [43], and YOLOv11 [44]. Additionally, to fully validate the performance of FDNet, the comparison included mainstream non-YOLO-series object detection algorithms, such as Faster R-CNN [45], GFL [46], \begin{table}
\begin{tabular}{l c c c c c c c c} \hline \hline \multirow{2}{*}{**Method**} & **Compression** & \multirow{2}{*}{**P/\%**} & \multirow{2}{*}{**R/\%**} & **mAP@** & **mAP@** & \multirow{2}{*}{**GFLOPs**} & **Size/MB** & **Latency/ms** & **FPS** \\  & **Ratio** & & & **0.5**/\%** & & **0.5**/\%** & & **(Batch = 32)** \\ \hline FDNet & 0.0 & 95.7 & 91.0 & 96.9 & 69.8 & 13.6 & 14.2 & \(0.448\pm 0.169\) & 228.1 \\ +lim & 2.0 & 95.9 & 91.5 & 96.6 & 68.2 & 6.8 & 6.4 & 0.277 \(\pm\) 0.072 & 360.4 \\ +1 & 2.0 & 95.4 & 93.1 & 96.1 & 68.2 & 6.8 & 5.5 & 0.275 \(\pm\) 0.161 & 363.0 \\ +AMP & 2.0 & 95.9 & 89.9 & 96.1 & 68.7 & 6.8 & 5.3 & 0.268 \(\pm\) 0.032 & 372.8 \\ +AMP & 3.0 & 93.1 & 93.1 & 96.2 & 67.2 & 4.5 & 3.3 & 0.240 \(\pm\) 0.144 & 416.1 \\ +AMP & 4.0 & 96.3 & 89.4 & 96.6 & 67.9 & 3.4 & 2.5 & 0.200 \(\pm\) 0.113 & 499.5 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Comparative experiments on model compression. Figure 11: Comparison of the number of channels in the model before and after pruning. Based on the quantitative analysis results in Table 5, it is evident that the proposed FDNet (uncompressed) model for stem detection exhibits superior performance compared to other detection models, achieving the highest [EMAIL_ADDRESS] value of 96.9%. Within the YOLO series of object detection algorithms, YOLOv6s shows the best detection performance; however, it has significant parametric and computational requirements, necessitating higher resource availability. In comparison, the FDNet algorithm has a higher [EMAIL_ADDRESS] by 0.09 and a nearly equivalent [EMAIL_ADDRESS]:0.95 value, yet it features a significantly lower GFLOPs and model size than YOLOv6s. When compared to models with similar computational demands, the FDNet model achieves higher detection accuracy. In the realm of non-YOLO object detection algorithms, FDNet remains superior in terms of detection accuracy, computational load, and model size. After applying compression strategies, the FDNet-p model achieved a Precision (P) value of 96.3%, a Recall (R) value of 89.4%, an [EMAIL_ADDRESS] of 96.6%, and an [EMAIL_ADDRESS]:0.95 of 67.9%, with only a 3.4 GFLOPs and a model size of just 2.5 MB. These metrics indicate that FDNet-p achieves a near-lossless compression, highlighting the efficiency of the Lamp pruning algorithm. Among many object detection models, the FDNet-p model has the lowest resource consumption. After applying LAMP pruning, the computational needs are drastically reduced, with the GFLOPs value at just 3.4, while maintaining high accuracy. To visually demonstrate the superior performance of the compressed FDNet model (FDNet-p), a comparative analysis with lightweight YOLO-series models (v5, v6, v8, v9, v10, and v11) was conducted. The visual results are shown in Figure 12. In the stem detection task, FDNet-p requires the lowest GFLOPs compared to other lightweight models, has the smallest model size, and is easily deployable on resource-constrained devices on production lines, all while maintaining the highest [EMAIL_ADDRESS] value to meet the precision requirements of stem detection tasks.