# ASHM-YOLOv9: A Detection Model for Strawberry in Greenhouses at Multiple Stages
## 1 Introduction
In recent studies, object detection technology has overcome the bottleneck of previous technologies and is gradually developing in a more efficient and accurate direction [10]. During the detection phase, object detection approaches can be divided into two principal categories. One type belongs to the two-stage object detection algorithm, which is characterized by the necessity of generating candidate regions prior to identification. The faster region-based convolutional neural network (Faster R-CNN) and the mask region-based convolutional neural network (Mask R-CNN) are typical two-stage object detection algorithms [11; 12]. In crop recognition, the counting accuracy of fruit quantity is often an important metric for assessing model performance. For example, as discussed by [PERSON] [13], they used a revised Faster R-CNN to identify and count ripe strawberries, replaced the original RoiAlign algorithm with the RoiPooling algorithm, and used bilinear interpolation for calculation. The average identification precision of the model reached 87.33%. On the basis of a more detailed recognition of strawberry fruits, [PERSON] [14] employed a modified Mask R-CNN to conduct fine classification and recognition of ripe strawberries. The original network was enhanced by incorporating self-calibrated convolution, resulting in an average precision of 93.7% for the upgraded model. Another type is the single-stage object detection algorithm, with more advanced algorithms such as the single-shot multibox detector (SSD) and the You Only Look Once (YOLO) algorithms [15; 16]. The characteristics of single-stage object detection algorithms are that they do not require the generation of candidate regions and have the merits of a short training time and less computational burden. Therefore, it has greater advantages in recognizing large quantities of crops. For example, [PERSON] [17] evaluated the precision of a strawberry fruit picking robot on the basis of SSD convolutional neural networks. By performing migration learning on SSD-MobileNet V1 with three retraining sessions, the results show that the robot can operate at higher frame rates with 90% precision. Both good and bad strawberries were detected and differentiated. Recently, the YOLO series of algorithms has demonstrated significant advantages in crop recognition, primarily characterized by their speed, accuracy, and outstanding performance in recognizing different varieties of crops. For example, algorithms such as YOLOv5, YOLOv7, and YOLOv8 have been widely applied in agriculture. For example, [PERSON] [18] proposed a real-time multilevel strawberry algorithm that relies on YOLOv5 and introduced the adaptive spatial feature fusion (ASFF) module. Experiments were conducted on the image recognition of strawberries in different periods in complex scenes. The results show that YOLOv5-ASFF has improved detection precision. It performed excellently in all aspects, with the precision and recall values reaching 87.19% and 88.88%, respectively, which are 0.48% and 1.71% higher than those of the original algorithm YOLOv5s. This study provides ideas and technical support for strawberry identification in complex field scenes. [PERSON] [19] introduced a novel deep learning methodology that enhances tea yield estimation by precisely tallying tea buds in croplands. They integrated a squeeze-and-excitation network based on the YOLOv5 model, incorporating the Hungarian matching algorithm and the Kalman filtering algorithm to ensure precision as well as dependable quantification of tea buds. [PERSON] [20] investigated TrichomeYOLO for identifying the length and density of maize trichomes via scanning electron microscope images based on the YOLOv5 model. [PERSON] [6] utilized YOLOv7 and AR technology to determine the maturity of strawberry plants. The results indicated that their model could identify the maturity of strawberry plants well, with mAP and F1 values of 89% and 92%, respectively. Furthermore, the research also indicated that.