基于深度学习ImCascade R-CNN的小麦籽粒表形鉴定方法  被引量:1

Identification Method of Wheat Grain Phenotype Based on Deep Learning of ImCascade R-CNN

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作  者:泮玮婷 孙梦丽 员琰 刘平 PAN Weiting;SUN Mengli;YUN Yan;LIU Ping(College of Mechanical and Electronic Engineering/Intelligent Agricultural Machinery and Equipment Laboratory,Shan‐dong Agricultural University,Taian 271000,China)

机构地区:[1]山东农业大学机械与电子工程学院/智能化农业机械与装备实验室,山东泰安271000

出  处:《智慧农业(中英文)》2023年第3期110-120,共11页Smart Agriculture

基  金:山东省重点研发计划项目(2022LZGCQY002);山东省自然科学基金重点项目(ZR2020KF002)。

摘  要:[目的/意义]培育优质高产的小麦品种是小麦育种的主要目标,而小麦籽粒完整性直接影响小麦育种进程。完整籽粒与破损籽粒的部分特征差异较小,是限制基于深度学习识别破损小麦籽粒精准度的关键因素。[方法]为解决小麦籽粒检测精度低的问题,本研究建立ImCascade R-CNN模型,提出小麦籽粒表形鉴定方法,精准检测小麦籽粒完整性、分割籽粒并获取完整籽粒表形参数。[结果和讨论]ImCascade R-CNN模型检测小麦籽粒完整性的平均精度为90.2%,与Cascade Mask R-CNN、Deeplabv3+模型相比,能更好地识别、定位、分割小麦籽粒,为籽粒表形参数地获取提供基础。该方法测量粒长、粒宽的平均误差率分别为2.15%和3.74%,测量长宽比的标准误差为0.15,与人工测量值具有较高的一致性。[结论]研究结果可快速精准检测籽粒完整性、获取完整籽粒表形数据,加速培育优质高产小麦品种。[Objective]Wheat serves as the primary source of dietary carbohydrates for the human population,supplying 20%of the required ca‐loric intake.Currently,the primary objective of wheat breeding is to develop wheat varieties that exhibit both high quality and high yield,ensuring an overall increase in wheat production.Additionally,the consideration of phenotype parameters,such as grain length and width,holds significant importance in the introduction,screening,and evaluation of germplasm resources.Notably,a noteworthy positive association has been observed between grain size,grain shape,and grain weight.Simultaneously,within the scope of wheat breeding,the occurrence of inadequate harvest and storage practices can readily result in damage to wheat grains,consequently lead‐ing to a direct reduction in both emergence rate and yield.In essence,the integrity of wheat grains directly influences the wheat breed‐ing process.Nevertheless,distinguishing between intact and damaged grains remains challenging due to the minimal disparities in cer‐tain characteristics,thereby impeding the accurate identification of damaged wheat grains through manual means.Consequently,this study aims to address this issue by focusing on the detection of wheat kernel integrity and completing the attainment of grain pheno‐type parameters.[Methods]This study presented an enhanced approach for addressing the challenges of low detection accuracy,unclear segmentation of wheat grain contour,and missing detection.The proposed strategy involves utilizing the Cascade Mask R-CNN model and replac‐ing the backbone network with ResNeXt to mitigate gradient dispersion and minimize the model's parameter count.Furthermore,the inclusion of Mish as an activation function enhanced the efficiency and versatility of the detection model.Additionally,a multilayer convolutional structure was introduced in the detector to thoroughly investigate the latent features of wheat grains.The Soft-NMS al‐gorithm was employed to identify the candidate frame and achie

关 键 词:小麦育种 ImCascade R-CNN模型 籽粒完整性 语义分割 籽粒表形参数 深度学习 

分 类 号:TP391[自动化与计算机技术—计算机应用技术] S512[自动化与计算机技术—计算机科学与技术]

 

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