# ASHM-YOLOv9: A Detection Model for Strawberry in Greenhouses at Multiple Stages
## 1 Introduction  
Strawberry is an important economic crop that is rich in nutrients and vitamins. China is a large country in terms of the production, export, and consumption of strawberries, withits own area and output ranking first in the world. According to statistics, the strawberry planting area in China reached 110,000 hectares in 2018, accounting for approximately one-third of the world's strawberry planting area [1]. The growth stages of strawberries can be divided into several main periods: the flowering period, which typically begins in spring when strawberry plants start to develop flower buds. These buds gradually open to form flowers; the growth period follows flowering, during which the flowers are pollinated and enter the growth phase. This stage is primarily focused on fruit development, as strawberries begin to form small fruits; during the ripening period, strawberry fruits gradually enlarge and change color from green to red, indicating maturation [2]. The demands for nutrients and water vary significantly during different growth stages, making accurate identification of these stages crucial for scientific management, yield improvement, and quality enhancement [3]. Traditional manual identification methods face several critical limitations: labor-intensive and time-consuming processes that cannot meet the demands of large-scale agricultural production; subjective assessment variations among different operators leading to inconsistent results; and limited scalability for continuous monitoring in greenhouse environments and high operational costs that reduce agricultural profitability. These limitations necessitate the development of automated, objective, and efficient identification systems. Existing computer vision approaches for crop identification encounter specific challenges: similarity between adjacent growth stages, particularly between growth and ripening periods where fruit sizes are nearly identical; complex greenhouse environments with variable lighting conditions, leaf occlusion, and fruit clustering; and limited dataset diversity as most existing studies focus on mature strawberries in field conditions rather than comprehensive multistage identification in controlled environments. With the acceleration of industrialization, traditional manual picking techniques are no longer capable of fulfilling the requirements of modern agriculture and are time-consuming and costly, thereby necessitating innovative solutions. In recent years, the advent of automated strawberry picking technologies based on image recognition has led to revolutionary changes in this field. These technologies can accurately determine the ripeness of strawberries, thereby increasing their picking efficiency and precision [4]. Therefore, improving the ability to accurately identify the various growth stages of strawberries is not only an important step toward promoting agricultural modernization but also critical for ensuring food security, increasing farmers' income, and promoting sustainable development. Precise delineation of strawberry phenological phases is indispensable for implementing stage-specific interventions, maximizing productivity, and maintaining harvest integrity--core imperatives for advancing sustainable, high-value horticulture [5;6]. In practical agricultural image recognition, traditional machine learning methods and image processing algorithms have gradually lost their advantages. These methods often require significant manual intervention and cumbersome feature extraction processes, leading to a heavy workload and insufficient accuracy [7]. In contrast, target detection algorithms, which rely on deep learning and convolutional neural networks (CNNs), can automatically extract intricate information from images, thereby remarkably increasing recognition accuracy [8;9]. This approach not only reduces labor costs but also demonstrates greater adaptability in handling complex scenes, resulting in more accurate recognition outcomes and advancing the development of agricultural intelligence.