# A Novel Deep Learning Method for Detecting Strawberry Fruit
## 1 Introduction  
Strawberry is among the most popular small berries worldwide due to its high economic and nutritional values [1]. However, strawberry fruit matures quickly and is susceptible to damage, which can easily result in decay and economic loss if not harvested promptly. Currently, strawberry harvesting relies on traditional manual approaches, which causes problems of high labor intensity and costs [2]. Automated strawberry harvesting can effectively improve productivity and reduce labor costs, which presents a promising solution to these challenges. Strawberry detection is a crucial technology for achieving automated harvesting. Traditionally, the primary detection methods involve the integration of machine learning algorithms and computer vision techniques. Image-processing techniques, such as gray morphology, threshold segmentation, logical operations, the OTSU (Nobuyuki Otsu) method, and mean shift segmentation, are widely employed to extract the visual characteristics of fruits from various color spaces [3, 4]. Subsequently, the extracted visual characteristics are combined with prediction algorithms, such as Kalman filtering, and machine learning algorithms, such as K-nearest neighbor and SVMs (support vector machines), to facilitate target recognition and localization [5, 6, 7]. While the mentioned methods can effectively identify fruit targets in certain environments, their performance and robustness may be affected by fluctuating lighting and fruit occlusion [8]. Currently, deep neural network methods have gained popularity for strawberry detection due to their robust feature extraction capabilities [9]. Applying deep neural networks with high accuracy and speed for strawberry harvesting machinery has drawn much research attention. [PERSON] et al. [10] proposed an improved lightweight network RTSD-Net based on YOLOv4-Tiny for strawberry target detection under field conditions. Similarly, [PERSON] et al. [11] presented a novel strawberry detection method implemented on a harvesting robot to localize picking points based on the lightweight network Mobilenet-V1. [PERSON] et al. [12] proposed a strawberry localization method and an autonomous rover system, which utilizes image-processing techniques and the Mobilenet-V2 network to estimate strawberry ripeness in field conditions. Originally designed for embedded devices, these models prioritized lightweight design and detection speed. However, this emphasis compromised their accuracy in complex environments, which was only approximately 80%. Many researchers employed two-stage detection algorithms, which are typically represented by the R-CNN (region-based convolutional neural network) series, to enhance strawberry detection accuracy. [PERSON] et al. [13] proposed a novel algorithm to achieve the visual localization of strawberry picking points based on the Mask R-CNN algorithm, which demonstrated robustness for overlapping and occluded fruits under various lighting conditions. [PERSON] et al. [14] proposed a strawberry detection method that combines Mask R-CNN, region segmentation techniques, and SVM classifiers, which can accurately detect strawberries at six different ripeness levels. [PERSON] et al. [15] proposed a specialized Mask R-CNN model for strawberry instance segmentation, which promised both speed and precision outcomes. [PERSON] et al. [16] utilized a UAV to capture near-surface photographs of strawberry fields and employed the Faster R-CNN algorithm to detect and count flowers, mature strawberries, and immature strawberries, which achieved an average counting accuracy of 84.1%. Also, [PERSON] et al. [17] proposed a detection model based on the Faster R-CNN algorithm for counting strawberry fruit and flowers and effectively addressed the occlusion problem. [PERSON] et al. [2] proposed a Faster-RCNN-based strawberry-monitoring system that uses image acquisition and transfer learning to detect target fruits and measure strawberry ripeness and achieved an average accuracy exceeding 86%. [PERSON] et al.