## 1 Introduction  
Agriculture continues to play a crucial role in economies relying on agricultural activities [1], particularly in countries like Morocco, where it constitutes approximately 10.33% of the Gross Domestic Product (GDP) as of 2022 [2]. This sector is a significant source of employment in Morocco, contributing to nearly 33.3% of jobs and accounting for over 23% of exports [3]. Fresh strawberries are securing their position among the top 10 most exported fruits and vegetables from this country: the revenue earned through strawberry production consistently ranges from USD 40 to USD 70 million almost every year and is still increasing by an average of 3% annually [4]. However, the production of such soft fruits faces a great challenge because of pest infestation and diseases: the costs of managing these can contribute to nearly half of the production costs [5]. Recently, innovative techniques have been developed and used to improve agricultural practices, giving birth to the term \"precision agriculture\" [6]. The introduction of new tools and technologies, such as sophisticated irrigation systems or modern agricultural machinery, has significantly increased productivity and the ability to feed a growing population. However, plant detection in large areas remains a challenging task for farmers, particularly when dealing with small plants such as strawberry plants. This issue has prompted numerous studies aimed at finding solutions, including research on plant segmentation [7], fruit counting [8], and other related approaches [9]. In red fruit industries, such as the tomato industry [10; 11], the integration of robotics with machine learning algorithms enables a more precise, efficient, and sustainable approach, assisting farmers in making informed decisions to enhance yields, optimize the use of agricultural inputs, and reduce their environmental impact [12]. These human-like, perceptive, high-capability machines have shown great potential, especially when dealing with detection tasks. [PERSON] et al. [10] introduced an advanced yet easy-to-embed algorithm for detecting ripe tomatoes, utilizing enhancements in the HSV (Hue, Saturation, Value) color space and watershed segmentation. This approach was able to reach an accuracy detection of up to 81.6% and was designed to be used for all red fruits. However, these methods, despite exhibiting acceptable performance for certain data acquisition conditions, do not generalize effectively. Hence, recent studies have concentrated on exploring universal feature extraction methods to address the constraints faced by traditional image detection algorithms. To the best of our knowledge, [PERSON] et al. [13] pioneered the application of deep learning networks for fruit detection. These techniques are known for their capability to learn complex features autonomously and can be used in the development of an end-to-end non-linear model with high precision for detection and other applications. An example of such a model was designed by [PERSON] et al. [14], who used a convolutional neural network for strawberry fruit detection. Their network was based on three sets of convolutional layers (classification, detection, and prediction) capable of being embedded in Raspberry Pi 3B. The detection speed achieved by this model was 1.63 frames per second, with an average precision (AP) of 84.2%. Another example was introduced by [PERSON] et al. [15], who employed the widely recognized Mask R-CNN framework [16] with a ResNet 50 backbone [17] combined with a feature pyramid network (FPN) [18] for feature extraction to attain an AP of 95.78% for strawberry fruits in a non-structured environment. This method covers overlapping and hidden fruits under varying illumination intensities. Moreover, the processing time for images of 640 \(\times\) 480 pixels was just 0.13 s when using a high-performance computer. However, these multi-stage detection methods demand substantial resources for region proposal selection, thereby posing limitations on the detection speed.