AN IMPROVED YOLOV4 METHOD FOR RAPID DETECTION OF WHEAT EARS IN THE FIELD

Abstract

The automatic detection of wheat ears in the field has important scientific research value in yield estimation, gene character expression and seed screening. The manual counting method of wheat ears commonly used by breeding experts has some problems, such as low efficiency and high influence of subjective factors. In order to accurately detect the number of wheat ears in the field, based on mobilenet series network model, deep separable convolution module and alpha channel technology, the yolov4 model is reconstructed and successfully applied to the task of wheat ear yield estimation in the field. The model can adapt to the accurate recognition and counting of wheat ear images in different light, viewing angle and growth period, At the same time, the model volume with different alpha parameters is more suitable for mobile terminal deployment. The results show that the parameters of the improved yolov4 model are five times smaller than the original model, the average detection accuracy is 76.45%, and the detection speed FPS is two times higher than the original model, which provides accurate technical support for rapid yield estimation of wheat in the field.