摘要
针对大田作物行特征复杂多样,传统作物行识别方法鲁棒性不足、参数调节困难等问题,该研究提出一种基于特征工程的大田作物行识别方法。以苗期棉花作物行冠层为识别对象,分析作物行冠层特点,以RGB图像和深度图像为数据来源,建立作物行冠层特征表达模型。运用特征降维方法提取作物行冠层的关键特征参数,降低运算量。基于支持向量机技术建立作物行冠层特征分割模型,提取作物行特征点。结合随机抽样一致算法和主成分分析技术建立作物行中心线检测方法。以包含不同光照、杂草、相机位姿的棉花作物行图像为测试数据,运用线性核、径向基核和多项式核的支持向量机分类器开展作物行冠层分割试验;对比分析典型Hough变换、最小二乘法和所建作物行中心线检测方法的性能。结果表明,径向基核分类器的分割精度和鲁棒性最优;所建作物行中心线检测方法的精度和速度最优,航向角偏差平均值为0.80°、标准差为0.73°;横向位置偏差平均值为0.90像素,标准差为0.76像素;中心线拟合时间平均值为55.74 ms/f,标准差为4.31 ms/f。研究成果可提高作物行识别模型的适应性,减少参数调节工作量,为导航系统提供准确的导航参数。
Aiming at the complexity and diversity of the characteristics of field crop rows,the lack of robustness of the traditional crop row detection method,and the difficulty of parameter adjustment,a field crop row detection method based on feature engineering was proposed.Taking the seedling cotton crop row canopy as the recognition object,the crop row canopy characteristics were analyzed,and the feature expression model of the canopy of cotton crop was established with RGB image and depth image as the data source.The key feature parameters of crop row canopy were extracted by using feature dimensionality reduction method to reduce the amount of computation.A crop canopy feature segmentation model was established based on support vector machine technology to extract crop feature points.The method of crop row centerline detection was established by combining random sample consensus algorithm and principal component analysis.Using cotton crop row images with different illumination,weed and camera positions as test data,SVM classifiers with linear,RBF,and polynomial kernels were employed to conduct crop row canopy segmentation experiments.The performance of typical Hough transform,linear square method and the established crop row centerline detection method was compared and analyzed.The results showed that the RBF classifier had the best segmentation accuracy and robustness.The accuracy and speed of the established crop row centerline detection method were the best.The mean value of heading angle deviation was 0.80°and the standard deviation was 0.73°;the mean value of lateral position deviation was 0.90 pixels and the standard deviation was 0.76 pixels;the mean value of centerline fitting time was 55.74 ms/f and the standard deviation was 4.31 ms/f.The research results can improve the adaptability of crop row detection model,reduce the workload of parameter adjustment,and provide accurate navigation parameters for navigation system.
作者
张硕
刘禹
熊坤
翟志强
朱忠祥
杜岳峰
ZHANG Shuo;LIU Yu;XIONG Kun;ZHAI Zhiqiang;ZHU Zhongxiang;DU Yuefeng(College of Engineering,China Agricultural University,Beijing 100083,China)
出处
《农业机械学报》
EI
CAS
CSCD
北大核心
2023年第S01期18-26,共9页
Transactions of the Chinese Society for Agricultural Machinery
基金
国家自然科学基金项目(32101622)
中央高校基本科研业务费专项资金项目(2023TC083)
关键词
作物行识别
大田作物
冠层分割
支持向量机
双目视觉
机器学习
crop row detection
field crop
canopy segmentation
support vector machine
binocular vision
machine learning
