APW: An ensemble model for efficient wheat spike counting in unmanned aerial vehicle images

Abstract

The accurate estimation of wheat ear is of great significance to gain high yield for ensuring food security. The use of computer vision for wheat ear counting to improve the estimation efficiency and accuracy has been explored by many scholars. However, existing single-computer vision recognition methods have insufficient robustness and recognition of adherent wheat ears. This study mainly focused on to solve the problem of adherent wheat ears, and proposed a combined algorithm called “APW” that ensemble with alternating direction methods of multipliers (ADMM), Potts algorithm and Watershed to achieve fast recognition and counting of wheat ears. The images of wheat ears were collected under three different scenarios including growth stages, planting densities, and flight heights by using unmanned aerial vehicle (UAV), and the Potts model was used to recognise the images. The masked images of the wheat ear were converted into a greyscale image, and were layered using a greyscale gradient. Finally, the watershed algorithm was used to segment adherent wheat ears in the layered image, and the centroids were labelled and counted. The results shows that the best accuracy was obtained under low planting density scenario, with an R2 of 0.89 and a root mean square error (RMSE) of 3.72, suggesting that APW can handle scenarios with large background differences and low target adherence. These findings provides a new approach for efficient counting of wheat ears in UAV acquired images.