# ASHM-YOLOv9: A Detection Model for Strawberry in Greenhouses at Multiple Stages
## 5 Discussion  
### Model Advantages  
In this work, the YOLOv9 model was chosen as the base model for experiments addressing the multistage object detection problem of strawberry cultivation in greenhouses. Since the introduction of YOLO in 2015, it has evolved through multiple versions, becoming more mature and addressing its limitations. This progression has endowed the YOLO series of algorithms with numerous advantages in object detection. With several version updates, the balance between recognition speed and accuracy has significantly improved, and its ability to monitor small targets has become more sophisticated. Additionally, its strong generalization ability allows it to be applicable in various fields [39]. YOLOv9 is a recently proposed object detection method that addresses the problem of information loss occurring layer by layer during information transmission. It has demonstrated outstanding performance in object detection tasks on the MS COCO dataset, surpassing all previous real-time object detection methods. Additionally, its strong transferability allows it to be applicable in various research fields [23]. For example, [PERSON] and others proposed an FS-YOLOv9 model based on the YOLOv9 model for detecting and identifying breast cancer. This model has shown superior performance on both the original dataset and the breast cancer dataset, demonstrating significant improvements over previous models. This also provides practical assistance for the diagnosis of high-risk breast cancer patients [40]. [PERSON] and others proposed a MAR-YOLOv9 algorithm for object detection of crop types in the agricultural field and in complex and variable environments. Experiments have shown that it can recognize fruits against complex backgrounds, with accuracy superior to that of other models [41]. [PERSON] [42] utilized YOLOv9 as an improved foundation in the field of smart urban traffic and proposed the GC-YOLOv9 model. This model significantly enhances the perception capability and detection performance of the original model, surpassing existing technologies. It also holds potential value in the management of fire safety in public areas, the monitoring of forest fires, and intelligent security systems. In summary, YOLOv9, with its fast speed, high accuracy, and strong transferability, has become our base model. Next, we improved the base YOLOv9 model by making modifications from four different perspectives. On the basis of the unique shape characteristics of strawberries, we changed the traditional convolution kernel shape, moving away from the conventional square kernels to use convolution kernels of arbitrary sizes to better fit the shape of strawberries. We successfully reduced the sizes of various model parameters via AKConv, resulting in a slight improvement in accuracy. [PERSON] [43] utilized YOLOv8 to detect and classify Stropharia Rugoso-Annulata and verified in their ablation experiments that the incorporation of the AKConv module substantially reduced the sizes of diverse parameters, which is consistent with our research findings. We integrate the SE module into the original model, which can learn global information to enhance features that are beneficial for recognition while suppressing features that are not helpful for recognition. [PERSON] [44] used YOLOv5 as a base model for recognizing aerial remote sensing images and subsequently added the SE module. Their ablation experiments demonstrated that the introduction of this module could improve the recognition accuracy and other parameters. Correspondingly, we also successfully improved the model's accuracy. The Haar wavelet transform is a signal handling approach that enhances recognition accuracy and precision by decomposing image information into low-frequency and high-frequency components and then combining them into new feature maps.