# Method for Recognizing Disordered Sugarcane Stacking Based on Improved YOLOv8n
## 1 Introduction  
In recent years, with the development of advanced sugarcane planting technologies, a pre-cut seed-cane planting method has been proposed. This method primarily involves manually removing pre-cut sugarcane sections from disordered stacked boxes and placing them on the conveyor belt of a planting machine, from which the sugarcane falls into seed distribution tubes for planting. Typically, to complete the seeding of a row, two workers are required to perform the seed-cane retrieval [1; 2; 3; 4; 5]. However, this method of operation involves high labor intensity and does not meet the needs for automated sugarcane seeding. Therefore, to address this issue, our research group has proposed replacing manual labor with machines through visual detection of disordered stacked sugarcane. Nonetheless, retrieving seed-canes from disordered stacks presents several challenges, such as occlusions and overlaps between targets leading to background diversity issues, sugarcane seed-canes often being made from the tips, which results in varied shapes and sizes, and poor lighting conditions in field operations. Consequently, developing a detection algorithm with high robustness and powerful feature extraction capabilities is crucial and forms the key technological foundation for achieving automated mechanical planting of sugarcane seed-canes [6; 7; 8; 9]. When traditional machine learning techniques are employed for crop target detection, they are affected by factors such as terrain, weather conditions, camera angles, and cropspacing [10]. This typically requires manual setting and adjustment of feature thresholds, leading to long training times and lower recognition accuracy. In recent years, with innovations in neural network algorithms and the increasing demand for smart devices, many scholars have conducted research on crop target detection using deep learning technology [11; 12; 13; 14]. For example, [PERSON] et al. [15] employed the YOLOv3 algorithm and modified the prediction boxes to circular boundaries, effectively enhancing the accuracy of tomato localization to an average precision (mAP) of 96.4%. [PERSON] et al. [16] combined the K-means++ algorithm with mean denoising techniques, successfully improving the precision of YOLOv3 in identifying post-harvest cotton residue. [PERSON] et al. [17] introduced the DenseNet network into YOLOv4, improving the label box design, thereby increasing the mAP for cherry detection by 15%. [PERSON] et al. [18] integrated RepGhost and ECA attention mechanisms into YOLOv7, developing a new method for detecting dragon fruit. [PERSON] et al. [19] optimized the YOLOv5 network, incorporating focus networks and cross-stage techniques as well as the EIOU loss function, enhancing the detection accuracy for small tomato targets. [PERSON] et al. [20] proposed a deep learning-driven method for enhancing fruit classification image annotation, effectively resolving the issue of a large number of unlabeled images in automatic annotations. Given the current challenges faced in identifying disordered stacked sugarcane, a plan has been formulated to develop an improved object detection algorithm. This algorithm aims to effectively recognize disordered stacked sugarcane while maintaining high accuracy and low computational resource consumption. Among the many object detection algorithms, the YOLO series is widely praised for its excellent balance between detection accuracy and processing speed. Therefore, this study focuses on improving four key aspects of the YOLOv8n algorithm, aimed at enhancing and boosting its performance in sugarcane recognition. The main contributions of this paper include the following:  
1. Backbone network replacement: Replace the original backbone network of the YOLOv8n model with VanillaNet to enhance the model's feature extraction capability and computational efficiency, ensuring more effective processing and analysis of disordered stacked sugarcane image data. 2.