孙俊, 张林, 周鑫, 武小红, 沈继锋, 戴春霞. 采用高光谱图像深度特征检测水稻种子活力等级[J]. 农业工程学报, 2021, 37(14): 171-178. DOI: 10.11975/j.issn.1002-6819.2021.14.019
    引用本文: 孙俊, 张林, 周鑫, 武小红, 沈继锋, 戴春霞. 采用高光谱图像深度特征检测水稻种子活力等级[J]. 农业工程学报, 2021, 37(14): 171-178. DOI: 10.11975/j.issn.1002-6819.2021.14.019
    Sun Jun, Zhang Lin, Zhou Xin, Wu Xiaohong, Shen Jifeng, Dai Chunxia. Detection of rice seed vigor level by using deep feature of hyperspectral images[J]. Transactions of the Chinese Society of Agricultural Engineering (Transactions of the CSAE), 2021, 37(14): 171-178. DOI: 10.11975/j.issn.1002-6819.2021.14.019
    Citation: Sun Jun, Zhang Lin, Zhou Xin, Wu Xiaohong, Shen Jifeng, Dai Chunxia. Detection of rice seed vigor level by using deep feature of hyperspectral images[J]. Transactions of the Chinese Society of Agricultural Engineering (Transactions of the CSAE), 2021, 37(14): 171-178. DOI: 10.11975/j.issn.1002-6819.2021.14.019

    采用高光谱图像深度特征检测水稻种子活力等级

    Detection of rice seed vigor level by using deep feature of hyperspectral images

    • 摘要: 为实现水稻种子活力的准确检测,该文研究了一种基于高光谱图像技术结合深度学习的高精度检测方法。采用人工加速老化的方式得到老化0,1,2和3 d的1 200个水稻种子样本,使用高光谱成像设备获取不同老化天数样本的高光谱图像,并从单个样本区域提取其光谱信息。随后对1 200个样本进行发芽试验,根据发芽试验结果将所有样本划分为高活力、低活力和无活力3个等级。采用小波阈值去噪(Wavelet Threshold Denoising,WTD)结合一阶导数(First/1st Derivative,FD)的方法(WTD-FD)对原始光谱进行预处理,使用主成分分析(Principal Component Analysis,PCA)和堆叠自动编码器(Stacked Auto-Encoder,SAE)分别从预处理光谱中提取特征变量。分别基于PCA和SAE特征变量构建支持向量机(Support Vector Machine,SVM)模型,并根据模型准确率确定较佳模型,最后使用灰狼优化算法(Grey Wolf Optimizer,GWO)对选择的模型进行参数优化。结果显示WTD-FD对原始光谱的预处理是有效的,使用从预处理光谱中提取的SAE非线性深层特征相比于PCA线性特征更具有代表性,基于其建立的SAE-SVM模型的准确率达到96.47%。SAE-SVM模型经过GWO优化之后,模型准确率提高到98.75%。研究结果表明,高光谱图像技术结合深度学习方法对水稻种子活力等级准确检测具有指导意义。

       

      Abstract: Abstract: Vigor is a significant indicator for the yield and quality of rice seed in agricultural production and food security. This study aims to fast, non-destructively, and precisely detect the rice seed vigor using hyperspectral imaging and deep learning. Rice seed of Lvhan 1 was selected as the research object. Artificial accelerated aging was also performed on the rice seeds to obtain the samples with different aging degrees. Firstly, a hyperspectral imaging instrument was utilized to collect the hyperspectral images of 1 200 single rice seeds with four aging levels, where the spectral information was extracted from a single sample area. A germination experiment was then carried out, after which all samples were divided into three vigor levels, including high, low, and no vigor. Next, wavelet threshold denoising (WTD), first derivative (FD), and their combination (WTD-FD) were used to preprocess the original spectral data. A model was then established using the preprocessed spectral data. Model evalution was also carried out for the best preprocessing. After that, principal component analysis (PCA) and stack auto encoder (SAE) were adopted to reduce the dimension of spectral data, while extracting spectral features. Thirdly, a detection model of vigor level in rice seed was established using original spectra and the spectral feature data extracted by PCA and SAE. The training and test set were divided ten times to repeat the three Support Vector Machine (SVM) models for higher average accuracies. As such, a better model was determined. GWO was then used to optimize the SVM model, in order to improve the performance of the model. The best model was also determined using the vigor grading model of rice seed. Finally, the best model was used to evaluate the vigor level in a batch of unaged seeds for the generalization performance. The results show that the accuracy of preprocessed spectra (WTD, FD, and WTD-FD) model was higher than that of the original spectra. The WTD-FD accurately reduced the effect of noise and baseline drift in the original spectra, indicating a better pretreatment effect than WTD and FD individually, where the full spectra data was used for subsequent analysis. The better modeling effect was obtained, compared with PCA and original spectra, when the deep features were extracted from full spectra data using SAE. The accuracies of training and test set were 99.08% and 96.47% using the SAE-SVM model, respectively. In grey wolf optimizer (GWO), the accuracies of optimized training and test set in the SAE-GWO-SVM model were 100% and 98.75%, respectively, indicating that improved by 0.92 and 2.28 percentage points, respectively. The accuracy of the model reached 98% for non-aged seeds, approximately to the predictive accuracy of the model (98.75%). Therefore, the hyperspectral imaging combined with the best SAE-GWO-SVM model is feasible for the detection of rice seed vigor in actual production.

       

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