Weng Shizhuang, Ma Junjie, Tao Wentao, Tan Yujian, Pan Meijing, Zhang Zixi, Huang Linsheng, Zheng Ling, Zhao Jinling
National Engineering Research Center for Agro-Ecological Big Data Analysis & Application, Anhui University, Hefei, China.
Front Plant Sci. 2023 Feb 28;14:1073530. doi: 10.3389/fpls.2023.1073530. eCollection 2023.
Drought stress (DS) is one of the most frequently occurring stresses in tomato plants. Detecting tomato plant DS is vital for optimizing irrigation and improving fruit quality. In this study, a DS identification method using the multi-features of hyperspectral imaging (HSI) and subsample fusion was proposed. First, the HSI images were measured under imaging condition with supplemental blue lights, and the reflectance spectra were extracted from the HSI images of young and mature leaves at different DS levels (well-watered, reduced-watered, and deficient-watered treatment). The effective wavelengths (EWs) were screened by the genetic algorithm. Second, the reference image was determined by ReliefF, and the first four reflectance images of EWs that are weakly correlated with the reference image and mutually irrelevant were obtained using Pearson's correlation analysis. The reflectance image set (RIS) was determined by evaluating the superposition effect of reflectance images on identification. The spectra of EWs and the image features extracted from the RIS by LeNet-5 were adopted to construct DS identification models based on support vector machine (SVM), random forest, and dense convolutional network. Third, the subsample fusion integrating the spectra and image features of young and mature leaves was used to improve the identification further. The results showed that supplemental blue lights can effectively remove the high-frequency noise and obtain high-quality HSI images. The positive effect of the combination of spectra of EWs and image features for DS identification proved that RIS contains feature information pointing to DS. Global optimal classification performance was achieved by SVM and subsample fusion, with a classification accuracy of 95.90% and 95.78% for calibration and prediction sets, respectively. Overall, the proposed method can provide an accurate and reliable analysis for tomato plant DS and is hoped to be applied to other crop stresses.
干旱胁迫(DS)是番茄植株中最常出现的胁迫之一。检测番茄植株的干旱胁迫对于优化灌溉和提高果实品质至关重要。在本研究中,提出了一种利用高光谱成像(HSI)多特征和子样本融合的干旱胁迫识别方法。首先,在补充蓝光的成像条件下测量高光谱图像,并从不同干旱胁迫水平(充分浇水、减少浇水和缺水处理)的幼叶和成熟叶的高光谱图像中提取反射光谱。通过遗传算法筛选有效波长(EWs)。其次,通过ReliefF确定参考图像,并使用Pearson相关分析获得与参考图像弱相关且相互无关的前四个有效波长的反射图像。通过评估反射图像对识别的叠加效果来确定反射图像集(RIS)。采用有效波长的光谱和通过LeNet-5从RIS中提取的图像特征,构建基于支持向量机(SVM)、随机森林和密集卷积网络的干旱胁迫识别模型。第三,使用整合幼叶和成熟叶光谱及图像特征的子样本融合进一步提高识别效果。结果表明,补充蓝光可以有效去除高频噪声并获得高质量的高光谱图像。有效波长光谱和图像特征组合对干旱胁迫识别的积极作用证明RIS包含指向干旱胁迫的特征信息。支持向量机和子样本融合实现了全局最优分类性能,校准集和预测集的分类准确率分别为95.90%和95.7%。总体而言,所提出的方法可以为番茄植株的干旱胁迫提供准确可靠的分析,并有望应用于其他作物胁迫。