基于相机拍照的油茶果形状特征提取研究

【目的】为实现油茶果实尺寸及大小分布的快速获取,提出一种基于相机拍摄的油茶果形状特征参数批量化提取方法。【方法】首先将采摘油茶果摆放于含刻度尺的背景板,利用相机快速获取油茶果图像并进行校正;然后利用Mask R-CNN模型对图像油茶果进行快速检测计数,根据生成的掩码采用椭圆拟合法统计油茶果特征参数(长轴长、短轴长、面积、周长)的像元个数;最后结合背景板刻度尺计算的像元大小,获取油茶果特征参数,同时利用实测值进行精度验证。【结果】Mask R-CNN模型的平均识别准确率和召回率分别为99.55%和91.19%,测度值为95.22%,满足用于统计油茶果形状特征参数的要求。对油茶果面积的估测精度最高,决定系数(R²)、平均绝对误差(MAE)、均方根误差(RMSE)分别为0.999 0、10.75 mm²、14.88 mm²;其次为周长和长轴长,短轴长的估测精度最低,其R²、MAE、RMSE分别为0.864 7、3.15 mm、3.74 mm。【结论】该方法实现了油茶果采摘后的快速准确计数以及形状特征参数的批量化提取,可为大量果实特征参数的快速准确检测提供参考,为指导油茶果实分级和快速测产提供科学依据。

Research on extraction of shape features of Camellia oleifera fruits based on camera photography

【Objective】Camellia oleifera, one of the four major woody oilseeds in the world, has a comprehensive utilization value. As a key rural industry in southern China, it plays an important role in the development of the agricultural economy. In recent years, with a vigorous support from the national government at all levels, the C. oleifera planting area has been expanding annually. Therefore, it is important to obtain information on the shapes of C. oleifera fruits quickly and accurately to determine the size distribution, guide fruit grading, and calculate rapid yield measurements. This paper proposes a batch extraction method for C. oleifera fruit shape feature parameters based on camera photography. 【Method】First, the picked C. oleifera fruits were placed on a background plate with a scale, using a camera to quickly obtain images of C. oleifera fruits and a correction of the geometric distortions produced by photography. Then, the Mask R-CNN model was used for a fast detection and counting of the C. oleifera fruits in the image. According to the generated mask, the number of pixels of the feature parameters (length of the long axis and short axis, area and perimeter) of the camellia fruits was counted using the elliptic fitting method. Finally, the image pixel size calculated with the help of the background plate scale was used to obtain the key parameters of the oil tea fruits, while the accuracy was verified using the actual measured values. 【Result】The average recognition accuracy and recall of the mask R-CNN model were 99.55% and 91.19%, respectively, and the F-measure value was 95.22%, showing that the method was suitable for describing C. oleifera fruit shape parameters statistically. The estimated accuracy of the area of the C. oleifera fruits was the highest with the coefficient of determination (R²), mean absolute error (MAE), and root mean square error (RMSE) of 0.999 0, 10.75 mm², and 14.88 mm², followed by the perimeter and length of the long axis, and the lowest accuracy of length of the the short axis with R², MAE, and RMSE of 0.864 7, 3.15 mm, and 3.74 mm, respectively. 【Conclusion】The method achieved a rapid and accurate counting of C. oleifera fruits shape feature parameters after picking and batch extraction. Thus, the technique can rapidly and accurately detect a large number of fruit feature parameters and provide a scientific basis for the C. oleifera fruit grading and rapid yield measurement.