基于不同维度低场核磁共振技术的大豆含油率检测与判别  

Soybean oil content detection and discrimination based on different dimensional low-field nuclear magnetic resonance techniques

作　　者：张宇[1] 赵亚楠 赵健翔 宋平[1] ZHANG Yu;ZHAO Yanan;ZHAO Jianxiang;SONG Ping(College of Information and Electrical Engineering,Shenyang Agricultural University,Shenyang 110866,China)

机构地区：[1]沈阳农业大学信息与电气工程学院,沈阳110866

出　　处：《农业工程学报》2024年第4期337-344,共8页Transactions of the Chinese Society of Agricultural Engineering

基　　金：国家自然科学基金项目(31701318);辽宁省教育厅基础研究项目(LJKMZ20221034)。

摘　　要：大豆含油率的高低直接影响榨油与育种结果。为探究大豆含油率的最佳检测方法与构建含油率高低判别模型,该研究基于不同维度低场核磁共振(low field nuclear magnetic resonance,LF-NMR)技术,以国标法为对照,利用LF-NMR波谱和LF-NMR含油含水率软件检测大豆含油率;核磁共振成像(magnetic resonance imaging,MRI)结合深度学习,建立大豆含油率高低判别模型。引入低场二维核磁共振(low field two-dimensional nuclear magnetic resonance,LF-2D-NMR)技术,定性分析一维波谱中信号重叠无法区分组分的问题。试验结果表明,LF-NMR含油含水率软件能快速准确检测大豆含油率,T1-T2二维核磁图谱成功解决了自由水和油信号重叠问题。利用U-net++深度学习模型对MRI成像的矢状面、冠状面、横截面以及三面混合数据集进行训练,其中横截面评价指标与其他数据集相比更优,语义分割部分中平均交并比(mean intersection over union,mIoU)约0.9058,全局准确率0.9980,训练后的模型能够将MRI图像识别并分割,快速判别大豆含油率高低。试验证明,LF-NMR及MRI能够快速无损掌握大豆含油率信息,为大豆的高油育种提供了新思路和技术支持。The oil content of soybeans can directly dominate oil extraction and breeding.This study aims to rapidly detect and then identify the oil content of soybeans.Excellent seeds were provided for the soybean oil extraction and breeding.A systematic investigation was also implemented to construct the optimal detection model for the soybean oil content.Different dimensional low-field nuclear magnetic resonance(LF-NMR)spectrum was used to detect the oil and water content of soybeans,according to the national standard.The magnetic resonance imaging combined with deep learning was then used to establish a detection model for the high and low oil content.Two-dimensional LF-NMR was introduced to qualitatively analyze the component overlap that failed to be distinguished in the one-dimensional spectrum.The experimental results showed that the LF-NMR oil and water content software performed best to more rapidly and accurately detect the oil content of soybeans.The T1-T22D-NMR spectrum was used to successfully treat the overlapping of the signals of not easily flowing water,free water and oil.It was an excellent tool to supplement one-dimensional descriptions,where the water and oil signals of soybean kernels were visually distinguished.At the same time,a novel analytical approach was used to offer new perspectives on soybean trade and breeding with great promise.The U-net++deep learning model was utilized to train the sagittal,coronal,cross-sectional,and three-sided mixed datasets of MRI imaging.Among them,the cross-sectional evaluation indexes were better compared with other datasets,where the mIoU in the semantic segmentation part was about 0.9058,with a global accuracy rate of 0.9980.The trained model was used to identify and segment the soybean MRI images,and then rapidly identify the oil content rate from the oil content level.The cross-sectional datasets were taken as the segmentation inputs for the magnetic resonance imaging,indicating the more reliable and accurate.The model,when combined with the data,allowed more in

关 键 词：低场核磁共振 低场二维核磁共振 磁共振成像 大豆含油率 深度学习 

分 类 号：S351.51[农业科学—作物栽培与耕作技术]