Quantitative Analysis of Cadmium Content in Tomato Leaves Based on Hyperspectral Image and Feature Selection
Quantitative Analysis of Cadmium Content in Tomato Leaves Based on Hyperspectral Image and Feature Selection
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DOI：
10.13031/aea.12679
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发表时间：
2018
影响因子：
0.9
通讯作者：
Yeuchun Zhang;Jun Sun;Liang Junyan;Xiaohong Wu;Chunmei Dai
中科院分区：
文献类型：
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作者：
Yeuchun Zhang;Jun Sun;Liang Junyan;Xiaohong Wu;Chunmei Dai
为了保证向人们提供安全健康的番茄，提出了一种基于高光谱成像技术的番茄叶片镉含量定量检测方法。对番茄叶片进行了7个镉胁迫梯度的研究。首先利用高光谱成像系统获取所有样品的高光谱图像，然后从高光谱图像中提取光谱数据。为了简化模型，采用竞争自适应加权采样（汽车）、变量组合总体分析（VCPA）和自举软收缩（BOSS）三种算法对431 ~ 962 nm的特征波长进行选择。实验结果表明，与其他两种选择方法相比，BOSS方法可以提高预测性能，并大大减少特征。BOSS模型的校正和预测精度最高，R2 c为0.9907，RMSEC为0.4257mg/kg，R2 p为0.9821，RMSEP为0.6461mg/kg。因此，高光谱技术结合BOSS特征选择检测番茄叶片镉含量的方法是可行的，为其他农作物镉含量检测提供了新的方法和思路。
In order to ensure that safe and healthy tomatoes can be provided to people, a method for quantitative determination of cadmium content in tomato leaves based on hyperspectral imaging technology was put forward in this study. Tomato leaves with seven cadmium stress gradients were studied. Hyperspectral images of all samples were firstly acquired by the hyperspectral imaging system, then the spectral data were extracted from the hyperspectral images. To simplify the model, three algorithms of competitive adaptive reweighted sampling (CARS), variable combination population analysis (VCPA) and bootstrapping soft shrinkage (BOSS) were used to select the feature wavelengths ranging from 431 to 962 nm. Final results showed that BOSS can improve prediction performance and greatly reduce features when compared with the other two selection methods. The BOSS model got the best accuracy in calibration and prediction with R2c of 0.9907 and RMSEC of 0.4257mg/kg, R2p of 0.9821, and RMSEP of 0.6461 mg/kg. Hence, the method of hyperspectral technology combined with the BOSS feature selection is feasible for detecting the cadmium content of tomato leaves, which can potentially provide a new method and thought for cadmium content detection of other crops.