Wheat Grain Yield Estimation Based on Image Morphological Properties and Wheat Biomass

Abstract

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 R2 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 R2 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.