Development of a novel image-based grain counting setup for thousand-grain weight estimation in wheat

Thousand-grain weight ( TGW) is one of the major yield-contributing traits routinely used as a selection criterion by plant breeders. It is also an important grain quality trait that determines milling yield. Accurate phenotyping of TGW is imperative to dissect its genetics for yield improvement. The traditional approach to TGW estimation involves manual grain counting and weighing, which is laborious, tedious and less accurate for large sample sizes. As an alternative, we propose a customized grain counting setup for accurate estimation of TGW in wheat by assembling a photo lighting tent and a smartphone for image acquisition of grain samples. A popular open-source software, 'imageJ' was used to process the images to estimate the grain count. The counted grain samples were weighed to calculate the TGW. The TGW estimate derived from the proposed grain counting setup displayed a high degree of correlation with the manually estimated TGW data (r = 0.99, p <0.05). It took significantly less time to count the grain samples using the proposed setup compared to manual counting with better accuracy and minimal labor. The error rate in grain counting using the imaging-based setup was very low (<1%) and 30 to 40 grain samples can be imaged per hour. This setup can be extended to estimate the TGW of different crops, excluding those having spherical seeds.