Authors

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.

Abstract in Chinese

田间麦穗的自动检测在产量估计、基因性状表达及种子筛选等方面都具有较为重要的科学研究价值，育种专家常用的麦穗人工计数方法存在效率低，主观因素影响较高等问题。为了精确检测田间麦穗数量，本文基于MobileNet系列网络模型，深度可分离卷积模块及Alpha通道等技术，重构了YOLOv4模型并成功应用到田间麦穗估产任务上，模型能够适应不同光照，视角，不同生长时期麦穗图像的准确识别和计数，同时采用不同Alpha参数的模型体积更加适应移动终端部署。结果表明，改进型YOLOv4模型参数量较原始模型体积缩小了5倍，平均检测精度达76.45%，检测速度FPS比原模型提升了2倍，为田间小麦快速估产提供准确的技术支撑。