Wang Renwei, Tan Dingzhong, Ju Xuerui, Wang Jianing
College of Mechanical and Electrical Engineering, Harbin Engineering University, Harbin 150001, China.
Sensors (Basel). 2025 May 27;25(11):3365. doi: 10.3390/s25113365.
To improve the object recognition and localization capabilities of wolfberry harvesting robots, this study introduces an object feature-based image segmentation algorithm designed for the segmentation and localization of wolfberry fruits and branches in unstructured lighting environments. Firstly, based on the -channel of the Lab color space and the -channel of the color space, a feature fusion algorithm combined with wavelet transformation is proposed to achieve pixel-level fusion of the two feature images, significantly enhancing the image segmentation effect. Experimental results show that this method achieved a 78% segmentation accuracy for wolfberry fruits in 500 test image samples under complex lighting and occlusion conditions, demonstrating good robustness. Secondly, addressing the issue of branch colors being similar to the background, a -means clustering segmentation algorithm based on the Lab color space is proposed, combined with morphological processing and length filtering strategies, effectively achieving precise segmentation of branches and localization of gripping point coordinates. Experiments validated the high accuracy of the improved algorithm in branch localization. The results indicate that the algorithm proposed in this paper can effectively address illumination changes and occlusion issues in complex harvesting environments. Compared with traditional segmentation methods, it significantly improves the segmentation accuracy of wolfberry fruits and the localization accuracy of branches, providing technical support for the vision system of field-based wolfberry harvesting robots and offering theoretical basis and a practical reference for research on agricultural automated harvesting operations.
为提高枸杞采摘机器人的目标识别与定位能力，本研究引入一种基于目标特征的图像分割算法，用于在非结构化光照环境下对枸杞果实和枝条进行分割与定位。首先，基于Lab颜色空间的 - 通道和 颜色空间的 - 通道，提出一种结合小波变换的特征融合算法，实现对两个特征图像的像素级融合，显著增强图像分割效果。实验结果表明，该方法在复杂光照和遮挡条件下的500个测试图像样本中，枸杞果实的分割准确率达到78%，具有良好的鲁棒性。其次，针对枝条颜色与背景相似的问题，提出一种基于Lab颜色空间的 - 均值聚类分割算法，结合形态学处理和长度滤波策略，有效实现枝条的精确分割和抓取点坐标定位。实验验证了改进算法在枝条定位方面的高精度。结果表明，本文提出的算法能够有效解决复杂采摘环境中的光照变化和遮挡问题。与传统分割方法相比，它显著提高了枸杞果实的分割准确率和枝条的定位准确率，为田间枸杞采摘机器人的视觉系统提供了技术支持，为农业自动化采摘作业的研究提供了理论依据和实践参考。
