Identification of the Yield of Camellia oleifera Based on Color Space by the Optimized Mean Shift Clustering Algorithm Using Terrestrial Laser Scanning
Identification of the Yield of Camellia oleifera Based on Color Space by the Optimized Mean Shift Clustering Algorithm Using Terrestrial Laser Scanning
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DOI：
10.3390/rs14030642
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发表时间：
2022-01
期刊：
影响因子：
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通讯作者：
Jie Tang;Fugen Jiang;Yi Long;L. Fu;Hua Sun
中科院分区：
文献类型：
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作者：
Jie Tang;Fugen Jiang;Yi Long;L. Fu;Hua Sun
油茶（Camellia oleifera）是世界上主要的木本食用油料植物之一，在提供食物和原料以及确保水资源保护方面至关重要。油茶产量能直接反映油茶林的生长状况，快速准确的测产直接有利于油茶林的高效经营。激光雷达（Light Detection and Ranging，LiDAR）能够穿透作物冠层获取目标的几何属性，已成为农产品产量识别的有效方法。然而，激光雷达系统获得的共同的几何属性信息总是有限的，在产量识别的精度方面。为了提高产量识别的效率和准确性，本研究采用了红-绿-蓝（RGB）和亮度-带宽-色度（即，YUV颜色空间）识别油茶果实的点云。构建了一种优化的均值漂移聚类算法，用于油茶果实点云的提取和产品识别。利用地面激光扫描（TLS）技术获得了油茶树的点云数据，并在长沙县进行了野外测量。此外，共同的均值漂移，基于密度的空间聚类的应用程序与噪声（DBSCAN），最大最小距离聚类建立了比较和验证。结果表明，优化后的均值漂移聚类算法在RGB和YUV颜色空间均取得了最佳识别效果，检测率分别比普通均值漂移聚类、DBSCAN聚类和最大-最小距离聚类算法提高了9.02%、54.53%和3.91%及7.05%、62.35%和10.78%。此外，改进的均值漂移聚类算法在YUV颜色空间中取得了更高的识别率，平均检测率为81.73%，比在RGB颜色空间中的平均检测率提高了2.4个百分点。因此，该方法可以对油茶进行高效的产量鉴定，为农产品管理提供新的参考。
Oil tea (Camellia oleifera) is one of the world’s major woody edible oil plants and is vital in providing food and raw materials and ensuring water conservation. The yield of oil tea can directly reflect the growth condition of oil tea forests, and rapid and accurate yield measurement is directly beneficial to efficient oil tea forest management. Light detection and ranging (LiDAR), which can penetrate the canopy to acquire the geometric attributes of targets, has become an effective and popular method of yield identification for agricultural products. However, the common geometric attribute information obtained by LiDAR systems is always limited in terms of the accuracy of yield identification. In this study, to improve yield identification efficiency and accuracy, the red-green-blue (RGB) and luminance-bandwidth-chrominance (i.e., YUV color spaces) were used to identify the point clouds of oil tea fruits. An optimized mean shift clustering algorithm was constructed for oil tea fruit point cloud extraction and product identification. The point cloud data of oil tea trees were obtained using terrestrial laser scanning (TLS), and field measurements were conducted in Changsha County, central China. In addition, the common mean shift, density-based spatial clustering of applications with noise (DBSCAN), and maximum–minimum distance clustering were established for comparison and validation. The results showed that the optimized mean shift clustering algorithm achieved the best identification in both the RGB and YUV color spaces, with detection ratios that were 9.02%, 54.53%, and 3.91% and 7.05%, 62.35%, and 10.78% higher than those of the common mean shift clustering, DBSCAN clustering, and maximum-minimum distance clustering algorithms, respectively. In addition, the improved mean shift clustering algorithm achieved a higher recognition rate in the YUV color space, with an average detection rate of 81.73%, which was 2.4% higher than the average detection rate in the RGB color space. Therefore, this method can perform efficient yield identification of oil tea and provide a new reference for agricultural product management.