School of Mechanical & Electrical Engineering, Guizhou Normal University, Guiyang, China.
Sci Rep. 2024 Jul 5;14(1):15507. doi: 10.1038/s41598-024-65321-9.
The mass and volume of Rosa roxburghii fruits are essential for fruit grading and consumer selection. Physical characteristics such as dimension, projected area, mass, and volume are interrelated. Image-based mass and volume estimation facilitates the automation of fruit grading, which can replace time-consuming and laborious manual grading. In this study, image processing techniques were used to extract fruit dimensions and projected areas, and univariate (linear, quadratic, exponential, and power) and multivariate regression models were used to estimate the mass and volume of Rosa roxburghii fruits. The results showed that the quadratic model based on the criterion projected area (CPA) estimated the best mass (R = 0.981) with an accuracy of 99.27%, and the equation is M = 0.280 + 0.940CPA + 0.071CPA. The multivariate regression model based on three projected areas (PA, PA, and PA) estimated the best volume (R = 0.898) with an accuracy of 98.24%, and the equation is V = - 8.467 + 0.657PA + 1.294PA + 0.628PA. In practical applications, cost savings can be realized by having only one camera position. Therefore, when the required accuracy is low, estimating mass and volume simultaneously from only the dimensional information of the side view or the projected area information of the top view is recommended.
罗梭梨果实的质量和体积是果实分级和消费者选择的重要依据。尺寸、投影面积、质量和体积等物理特性是相互关联的。基于图像的质量和体积估计有助于实现果实分级的自动化，从而取代耗时费力的人工分级。本研究采用图像处理技术提取果实的尺寸和投影面积，利用单变量（线性、二次、指数和幂）和多变量回归模型来估计罗梭梨果实的质量和体积。结果表明，基于准则投影面积（CPA）的二次模型可以最佳地估计质量（R=0.981），准确率为 99.27%，方程为 M=0.280+0.940CPA+0.071CPA。基于三个投影面积（PA、PA 和 PA）的多元回归模型可以最佳地估计体积（R=0.898），准确率为 98.24%，方程为 V=-8.467+0.657PA+1.294PA+0.628PA。在实际应用中，仅使用一个相机位置可以节省成本。因此，当所需精度较低时，建议仅从侧视图的尺寸信息或顶视图的投影面积信息同时估计质量和体积。
