Fusion of Three Optical Sensors for Nondestructive Detection of Water Content in Lettuce Canopies
Fusion of Three Optical Sensors for Nondestructive Detection of Water Content in Lettuce Canopies
复制标题
三个光学传感器融合无损检测生菜冠层水分含量
DOI：
10.1007/s10812-021-01158-8
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发表时间：
2021-03
影响因子：
0.7
通讯作者：
X. H. Wei
中科院分区：
文献类型：
--
作者：
H. Y. Gao;H. P. Mao;X. D. Zhang;I. Ullah;X. H. Wei
利用光谱、RGB图像和冠层温度相结合的方法，对生菜冠层的水分进行了无损检测和估计。为此，采集了生长在4个不同基质含水率水平上的130个生菜样品进行数据采集。在光谱过程中，通过反向区间偏最小二乘法选择了5个光谱区间(380个变量)，并利用基于Savitzky-Golay平滑和log(1/R)变换的遗传算法选择了48个波长变量。然后通过逐次投影算法选择了967、1170、1221、1406、1484、1942和1985 nm的最佳光谱变量。从俯视和前视RGB图像中提取了13个植物特征。这些特征包括形态、颜色和质地特征。通过热成像，建立了基于干湿参考面的作物水分胁迫经验指数。随后，对光谱变量和图像特征进行主成分分析，并使用极端学习机建立多传感器模型和单传感器模型。结果表明，多传感器模型的预测相关系数为0.9018，分别比光谱模型和图像模型的预测效果好9.4%和15.7%。这项工作表明，将光谱、RGB图像和冠层温度与适当的算法相结合，在生菜水分的无损测量中具有很高的潜力，与使用单传感器模式相比，大大提高了精度。
Experiments were conducted to develop and assess a method by which the water content of a lettuce canopy can be nondestructively detected and estimated using a combination of spectra, RGB images, and canopy temperature. To this end, 130 lettuce samples grown in four different substrate water content levels were collected for data acquisition. In the spectroscopy procedure, five spectral intervals (380 variables) were selected by backward interval partial least squares and were further reduced to 48 wavelength variables, chosen using a genetic algorithm based on Savitzky–Golay smoothing and log (1/R) transformation. Then, 967, 1170, 1221, 1406, 1484, 1942, and 1985 nm optimum spectral variables were selected by the successive projection algorithm. Thirteen plant features were extracted from top- and front-view RGB images. These features comprised morphological, color, and textural features. An empirical crop water stress index was established based on dry and wet reference surfaces via thermal imagery. Subsequently, a principal component analysis was applied to the spectral variables and the image features, and an extreme learning machine was used to construct the multisensor and single-sensor models. The results show that the multisensor model had a correlation coefficient of prediction of 0.9018, which was found to be approximately 9.4 and 15.7% better than that of the spectral and image models. This work demonstrates that integrating spectra, RGB images, and canopy temperature with suitable algorithms offers a high potential for use in the nondestructive measurement of water content in lettuce, considerably improving accuracy over that using a single-sensor modality.
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影响因子：
6.1
作者：
Galvao, RKH;Araujo, MCU;Saldanha, TCB
通讯作者：
Saldanha, TCB
影响因子：
8.3
作者：
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DOI：
10.1016/j.jag.2010.01.007
发表时间：
2010-04-01
影响因子：
7.5
作者：
Clevers, J. G. P. W.;Kooistra, L.;Schaepman, M. E.
通讯作者：
Schaepman, M. E.
影响因子：
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作者：
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通讯作者：
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