甘海明, 岳学军, 洪添胜, 等. 基于深度学习的龙眼叶叶绿素含量预测的高光谱反演模型[J]. 华南农业大学学报, 2018, 39(3): 102-110. DOI: 10.7671/j.issn.1001-411X.2018.03.016
    引用本文: 甘海明, 岳学军, 洪添胜, 等. 基于深度学习的龙眼叶叶绿素含量预测的高光谱反演模型[J]. 华南农业大学学报, 2018, 39(3): 102-110. DOI: 10.7671/j.issn.1001-411X.2018.03.016
    GAN Haiming, YUE Xuejun, HONG Tiansheng, LING Kangjie, WANG Linhui, CEN Zhenzhao. A hyperspectral inversion model for predicting chlorophyll content of Longan leaves based on deep learning[J]. Journal of South China Agricultural University, 2018, 39(3): 102-110. DOI: 10.7671/j.issn.1001-411X.2018.03.016
    Citation: GAN Haiming, YUE Xuejun, HONG Tiansheng, LING Kangjie, WANG Linhui, CEN Zhenzhao. A hyperspectral inversion model for predicting chlorophyll content of Longan leaves based on deep learning[J]. Journal of South China Agricultural University, 2018, 39(3): 102-110. DOI: 10.7671/j.issn.1001-411X.2018.03.016

    基于深度学习的龙眼叶片叶绿素含量预测的高光谱反演模型

    A hyperspectral inversion model for predicting chlorophyll content of Longan leaves based on deep learning

    • 摘要:
      目的  探讨龙眼Dimocarpus longan Lour. 叶片发育过程中叶绿素含量二维分布变化规律,实现无损检测病虫害对叶片叶绿素含量分布的影响,为评估嫩叶抗寒能力、龙眼结果期的施肥量和老熟叶的修剪提供参考。
      方法  利用高光谱成像仪采集龙眼叶片在369~988 nm区间的高光谱图像,自动提取感兴趣区域,利用分光光度法测定叶片叶绿素含量。基于皮尔森相关系数(r)分析了龙眼叶片生长过程中各波段光谱响应与叶绿素含量之间相关性,建立偏最小二乘回归模型。分析了特征波段图像纹理特征与叶绿素含量相关性,将光谱特征和纹理特征结合导入深度学习中的稀疏自编码(SAE)模型预测龙眼叶片叶绿素含量,结合“图谱信息”的SAE模型预测龙眼叶片叶绿素含量的分布情况。
      结果  龙眼叶片3个生长发育期相关系数的曲线均在700 nm附近出现波峰,嫩叶、成熟叶和老熟叶3个阶段相关性最高的波长分别为692、698 和705 nm;全发育期的最敏感波段相关性远高于3个生长发育期,r达到0.890 3。回归模型中,吸收带最小反射率位置和吸收带反射率总和建立的最小二乘回归模型预测效果最好(R2c=0.856 8,RMSEc=0.219 5;R2v=0.771 2,RMSEv=0.286 2),其校正集和验证集的决定系数均高于单一参数建立的预测模型。在所有预测模型中,结合“图谱信息”的SAE模型预测效果最好(R2c=0.979 6,RMSEc=0.171 2;R2v=0.911 2,RMSEv=0.211 5),且预测性能受叶片成熟度影响相对较小,3个生长阶段R2v的标准偏差仅为最小二乘回归模型标准偏差的29.9%。
      结论  提出了一种自动提取感兴趣区域的方法,成功率为100%。基于光谱特征的回归模型对不同生长阶段的叶片预测效果变化较大,而基于“图谱信息”融合的SAE模型预测性能受叶片成熟度影响相对较小且预测精度较高,SAE模型适用于不同成熟度的龙眼叶片叶绿素含量分布预测。

       

      Abstract:
      Objective  To study the distribution of chlorophyll content of Longan (Dimocarpus longan Lour) leaves in different growth periods, realize non-destructive measurement of the influence of pests and diseases on chlorophyll distribution, and provide a reference for evaluating the cold-resistant ability of young leaves, fertilizing amount in the fruiting period and pruning of mature leaves.
      Method  Hyperspectral images of Longan leaves in three growth periods were acquired via an online hyperspectral imaging system within the spectral region of 369–988 nm wavelength. An automatic masking method was used to extract the interest regions. The chlorophyll content was measured by the spectrophotometric method. The relationships between the spectral response characteristics and chlorophyll contents of Longan leaves in three growth periods were measured based on Pearson correlation coefficient (r). A partial least squares regression (LSR) model was established. The relationship between the texture feature of selected image and chlorophyll content was analyzed. The spectroscopy and texture features were imported to the spare auto-encoder (SAE) model in deep learning to predict the chlorophyll content of Longan leaves. The distribution of chlorophyll content was predicted using SAE model based on the mapping information.
      Result  The peaks of correlation coefficient curves of Longan leaves in three growth periods appeared in the vicinity of 700 nm. The wavelength of the highest correlation coefficient for young, mature and old ripe leaves was 692, 698 and 705 nm, respectively. The correlation coefficient (r) of the most sensitive band in full period was higher than those in three growth periods, which was up to 0.890 3. Among all regression models, the prediction effect of LSR model based on the absorption band of the minimum reflectivity and total reflectivity was the best (R2c=0.856 8, RMSEc=0.219 5; R2v=0.771 2, RMSEv=0.286 2), and the determination coefficients of its calibration and validation sets were higher than those based on a single parameter. SAE model importing spectroscopy and texture features performed the best (R2c=0.979 6, RMSEc=0.171 2; R2v=0.911 2, RMSEv=0.211 5) and the most stable to predict chlorophyll contents of Longan leaves in different growth periods, its standard deviation was only 29.9% of LSR model.
      Conclusion  A method automatically extracting interest region was proposed, its success rate was 100%. The performance of SAE model based on spectroscopy and texture features was more stable than those of regression models based on spectroscopy to predict chlorophyll contents of Longan leaves in different growth periods. SAE model is suitable for predicting the distribution of chlorophyll content of Longan leaves as a non-destructive method.

       

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