Liu Yiwen, Ban Songtao, Wei Shiwei, Li Linyi, Tian Minglu, Hu Dong, Liu Weizhen, Yuan Tao
College of Information Technology, Shanghai Ocean University, Shanghai, China.
Institute of Agricultural Science and Technology Information, Shanghai Academy of Agricultural Sciences, Shanghai, China.
Front Plant Sci. 2024 Jan 4;14:1242948. doi: 10.3389/fpls.2023.1242948. eCollection 2023.
The cold stress is one of the most important factors for affecting production throughout year, so effectively evaluating frost damage is great significant to the determination of the frost tolerance in lettuce.
We proposed a high-throughput method to estimate lettuce FDI based on remote sensing. Red-Green-Blue (RGB) and multispectral images of open-field lettuce suffered from frost damage were captured by Unmanned Aerial Vehicle platform. Pearson correlation analysis was employed to select FDI-sensitive features from RGB and multispectral images. Then the models were established for different FDI-sensitive features based on sensor types and different groups according to lettuce colors using multiple linear regression, support vector machine and neural network algorithms, respectively.
Digital number of blue and red channels, spectral reflectance at blue, red and near-infrared bands as well as six vegetation indexes (VIs) were found to be significantly related to the FDI of all lettuce groups. The high sensitivity of four modified VIs to frost damage of all lettuce groups was confirmed. The average accuracy of models were improved by 3% to 14% through a combination of multisource features. Color of lettuce had a certain impact on the monitoring of frost damage by FDI prediction models, because the accuracy of models based on green lettuce group were generally higher. The MULTISURCE-GREEN-NN model with R of 0.715 and RMSE of 0.014 had the best performance, providing a high-throughput and efficient technical tool for frost damage investigation which will assist the identification of cold-resistant green lettuce germplasm and related breeding.
冷胁迫是影响全年产量的最重要因素之一，因此有效评估霜冻损害对于确定生菜的抗冻性具有重要意义。
我们提出了一种基于遥感估算生菜霜冻损害指数（FDI）的高通量方法。利用无人机平台获取遭受霜冻损害的露地生菜的红-绿-蓝（RGB）和多光谱图像。采用皮尔逊相关分析从RGB和多光谱图像中选择对FDI敏感的特征。然后根据传感器类型和生菜颜色的不同组，分别使用多元线性回归、支持向量机和神经网络算法，为不同的FDI敏感特征建立模型。
发现蓝色和红色通道的数字值、蓝色、红色和近红外波段的光谱反射率以及六个植被指数（VIs）与所有生菜组的FDI显著相关。证实了四种修正的VIs对所有生菜组霜冻损害的高敏感性。通过多源特征的组合，模型的平均准确率提高了3%至14%。生菜颜色对FDI预测模型监测霜冻损害有一定影响，因为基于绿色生菜组的模型准确率普遍较高。R为0.715、RMSE为0.014的多源-绿色-神经网络模型性能最佳，为霜冻损害调查提供了一种高通量、高效的技术工具，有助于鉴定抗寒绿色生菜种质及相关育种。
