For apple recognition in natural orchard environments, the YOLOv8 algorithm offers unique advantages of accurate detection and rapid speed. This paper introduces an enhanced YOLOv8 model derived from the original YOLOv8 model. Firstly, the CReToNeXt structure replaces the C2f module in the original YOLOv8 model's head section, reducing computational complexity and increasing the model's receptive field, thereby enhancing detector performance by fully exploiting and utilizing multi-scale information of features to improve object detection accuracy. Then, the Shuffle- Attention (SA) attention mechanism module is introduced, enabling the algorithm to integrate deeper features with larger receptive fields, reducing the impact of imbalanced training sample annotation quality, improving the precision of predicted bounding boxes, and enhancing the detection ability of small objects. The study in this paper demonstrates that the improved YOLOv8 model attains a distinct enhancement over the original model: Precision (P) increases by 1.1%, Recall (R) increases by 0.9%, and mean Average Precision (mAP) increases by 1.5%.
With the development of agricultural intelligence, robots replacing manual labor in agricultural production has become an inevitable trend. For apple picking robots, accurate identification of apples is the primary step in their work. At present, the existing L*a*b and OHTA color spaces have problems of incomplete extraction of apple regions and incomplete differentiation of fruit and leaves. To address this issue, this study designed a color space (LPX color space) specifically for apple recognition, and based on this, proposed a complete algorithm for identifying green or red apples in a complex open environment. The experimental results show that the algorithm has high recognition accuracy in both cases of apples being shaded and unshaded, and can provide accurate orientation for the subsequent automated apple picking.
During the operation of apple picking robots, it is necessary to accurately identify branches to prevent collisions with them. However, obtaining branch images in complex environments is often fragmented, making it difficult to reconstruct the entire tree. To solve this problem, this study obtained branch images by removing non-branch areas, and extract tree skeletons and endpoints based on 8-connection and thin line methods. This study investigates the growth characteristics of fruit tree branches and the distribution of endpoints between branches at the same level, using this as a constraint to connect broken branches. Finally, the reconstruction of the entire tree is achieved. The experimental results show that the algorithm has a branch recognition rate of 92.1% on sunny days and 84.2% on cloudy days. Therefore, the algorithm proposed in this study can provide accurate branch information for apple picking robots.
KEYWORDS: Roads, Mathematical optimization, Signals intelligence, MATLAB, Detection theory, Reflection, Data modeling, Control systems, Visual process modeling, Sun
With the rapid development of China’s economy, the number of motor vehicles continues to increase, and the phenomenon of road congestion is becoming increasingly serious. Compared with widening the road surface and adding lanes, it is more feasible to optimize the crossing signal timing scheme. Based on the real-time queue length at the intersection, this study dynamically adjusts the signal timing scheme to overcome the rigidity of the traditional fixed timing scheme with the intersection of Fengxi Avenue and Siyuan Huannan Road in Chang’an District of Xi 'an as an example. The traditional scheme and the optimized scheme are jointly simulated by VISSIM and MATLAB respectively, and the simulation results of the two schemes are compared and analyzed. The results show that the average queue length, average delay time and average stop time of the optimized scheme are significantly lower than those of the traditional scheme, which proves that this method can effectively improve the traffic efficiency of intersections and alleviate traffic congestion.
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