Investigation of the effect of hectoliter and thousand grain weight on variety identification in wheat using deep learning method

Abstract

Accurate identification of wheat varieties in the seed and flour industry is extremely important. The success rate of correctly identifying wheat varieties using artificial intelligence methods compared to traditional methods is quite high. Whether hectoliter weight (HLW) and thousand grain weight (TGW) represent the variety in identification studies is a subject to debate. The reason of this debate is these parameters are heavily affected by environmental factors such as soil nutrient levels, amount of rainfall, and number of sunny days. In other words, it is assumed that these parameters are not specific to the variety. In this study, the feature map obtained using the GLCM method was compared with the feature map obtained by adding the HLW and TGW parameters. As a result of the comparison, the accuracy rate was calculated as 78% in the first feature map. However, when standard features were added to the HLW and TGW parameters, the accuracy rate was calculated as 82%. The results show that the HLW and TGW parameters contribute to the identification of the wheat variety when used correctly with artificial intelligence.