The estimation of wheat grain yield based on a composite of morphological features and mass of wheat organs was introduced in this study. The morphological features (length, width, and perimeter for the wheat stem and ear) were extracted by a computer vision system whose performance was evaluated by correlating the measured and estimated perimeter and length of the wheat stem at an of 0.9609 and 0.9779, respectively. Six regression models were developed based on the extracted features. The linear regression based on the wet weight of the stem, the ear, and the leaves outperformed all the other statistical models explored with an of 0.9893 and an RMSE of 0.0684 mm in estimating the dry grain yield with wet wheat organ mass as the predictors. This proposed system can be applied as nondestructive in a field technique for wheat phenotyping. Additionally, it can be applied to other similar crops.

中文翻译：

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基于图像形态学特征和小麦生物量的小麦籽粒产量估算

本研究介绍了小麦形态特征和小麦器官质量的综合估算。通过计算机视觉系统提取形态特征（小麦茎和穗的长度，宽度和周长），其性能通过将测得的和估计的小麦茎的周长和长度分别关联到0.9609和0.9779进行评估。根据提取的特征开发了六个回归模型。基于茎，穗和叶的湿重的线性回归优于其他所有使用在估计以湿小麦器官质量为预测指标的干粮产量时，采用0.9893的均方根值和0.0684 mm的RMSE。该提议的系统可以在小麦表型的田间技术中作为非破坏性应用。另外，它可以应用于其他类似的农作物。