基于神经架构搜索的灌浆期水稻稻穗分割及特征分析

Panicle Segmentation and Characteristics Analysis of Rice During Filling Stage Based on Neural Architecture Search

灌浆期是水稻的重要生育期之一,为精准分割灌浆期水稻稻穗,探究稻穗特征与水稻长势之间的关系,提出了一种基于神经架构搜索的灌浆期水稻稻穗分割及特征分析方法。以DeepLabV3Plus网络模型为基础框架,基于神经架构搜索算法自动设计主干网络,修改空洞空间卷积池化金字塔(ASPP),搭建语义分割网络Rice-DeepLab。通过田间摄像头采集4种水稻的灌浆期图像并经Rice-DeepLab分割后,计算稻穗面积占比、离散程度、图像平均曲率和颜色特征等参数并分析。实验结果显示:改进后的语义分割网络Rice-DeepLab的平均交并比(mIoU)为85.74%,准确率(Acc)为92.61%,与原网络模型相比mIoU、Acc分别提高了6.5%、2.97%;由图像的稻穗面积占比、离散程度、图像平均曲率、颜色特征可大致判别稻穗稀疏或稠密,稻穗是否饱满,色泽青绿、金黄或灰白等长势。本研究表明,可以利用田间摄像头便捷地开展灌浆期水稻监测工作,通过稻穗分割及其特征分析初步判断水稻的长势,为田间管理提供支持。

The grain filling stage is a critical growth phase of rice.To segment the panicle accurately during filling stage and explore the relationship between its characteristics and plant maturation,a method of segmentation and characteristics analysis is proposed based on neural architecture search(NAS).Based on the DeepLabV3Plus network model,the backbone network is automatically designed using NAS,and the semantic segmentation network Rice-DeepLab is built by modifying atrous spatial pyramid pooling(ASPP).The area ratios,dispersion,average curvature,and color characteristics of the panicles of four rice varieties are calculated and analyzed after segmentation by Rice-DeepLab.The experimental results show that the improved Rice-DeepLab network has a mean intersection over union(mIoU)of 85.74%and accuracy(Acc)of 92.61%,which is 6.5%and 2.97%higher than that of the original model,respectively.According to the panicles’area ratios,dispersion,average curvature,and color characteristics recorded in the image,it can be roughly distinguished whether the panicles are sparse or dense,whether grain filling is complete,and whether the color is green,golden,or gray.This study suggests that field cameras can be easily used to monitor rice in the filling stage preliminarily to estimate maturation and crop size by panicle segmentation and characteristics analysis,thus providing support for field management.