Sun Xinkai, Yang Zhongyu, Su Pengyan, Wei Kunxi, Wang Zhigang, Yang Chenbo, Wang Chao, Qin Mingxing, Xiao Lujie, Yang Wude, Zhang Meijun, Song Xiaoyan, Feng Meichen
College of Agriculture, Shanxi Agricultural University, Jinzhong, Taigu, Shanxi, China.
College of Resources and Environment, Shanxi Agricultural University, Jinzhong, Taigu, Shanxi, China.
Front Plant Sci. 2023 Mar 31;14:1158837. doi: 10.3389/fpls.2023.1158837. eCollection 2023.
Leaf area index (LAI) is an essential indicator for crop growth monitoring and yield prediction. Real-time, non-destructive, and accurate monitoring of crop LAI is of great significance for intelligent decision-making on crop fertilization, irrigation, as well as for predicting and warning grain productivity. This study aims to investigate the feasibility of using spectral and texture features from unmanned aerial vehicle (UAV) multispectral imagery combined with machine learning modeling methods to achieve maize LAI estimation. In this study, remote sensing monitoring of maize LAI was carried out based on a UAV high-throughput phenotyping platform using different varieties of maize as the research target. Firstly, the spectral parameters and texture features were extracted from the UAV multispectral images, and the Normalized Difference Texture Index (NDTI), Difference Texture Index (DTI) and Ratio Texture Index (RTI) were constructed by linear calculation of texture features. Then, the correlation between LAI and spectral parameters, texture features and texture indices were analyzed, and the image features with strong correlation were screened out. Finally, combined with machine learning method, LAI estimation models of different types of input variables were constructed, and the effect of image features combination on LAI estimation was evaluated. The results revealed that the vegetation indices based on the red (650 nm), red-edge (705 nm) and NIR (842 nm) bands had high correlation coefficients with LAI. The correlation between the linearly transformed texture features and LAI was significantly improved. Besides, machine learning models combining spectral and texture features have the best performance. Support Vector Machine (SVM) models of vegetation and texture indices are the best in terms of fit, stability and estimation accuracy (R = 0.813, RMSE = 0.297, RPD = 2.084). The results of this study were conducive to improving the efficiency of maize variety selection and provide some reference for UAV high-throughput phenotyping technology for fine crop management at the field plot scale. The results give evidence of the breeding efficiency of maize varieties and provide a certain reference for UAV high-throughput phenotypic technology in crop management at the field scale.
叶面积指数（LAI）是作物生长监测和产量预测的重要指标。对作物LAI进行实时、无损且准确的监测，对于作物施肥、灌溉的智能决策以及粮食生产力的预测和预警具有重要意义。本研究旨在探讨利用无人机（UAV）多光谱影像的光谱和纹理特征结合机器学习建模方法来实现玉米LAI估算的可行性。在本研究中，以不同品种的玉米为研究对象，基于无人机高通量表型平台对玉米LAI进行遥感监测。首先，从无人机多光谱影像中提取光谱参数和纹理特征，并通过纹理特征的线性计算构建归一化差异纹理指数（NDTI）、差异纹理指数（DTI）和比值纹理指数（RTI）。然后，分析LAI与光谱参数、纹理特征和纹理指数之间的相关性，筛选出相关性较强的图像特征。最后，结合机器学习方法，构建不同类型输入变量的LAI估算模型，并评估图像特征组合对LAI估算的影响。结果表明，基于红（650 nm）、红边（705 nm）和近红外（842 nm）波段的植被指数与LAI具有较高的相关系数。线性变换后的纹理特征与LAI的相关性显著提高。此外，结合光谱和纹理特征的机器学习模型性能最佳。植被和纹理指数的支持向量机（SVM）模型在拟合度、稳定性和估算精度方面表现最佳（R = 0.813，RMSE = 0.297，RPD = 2.084）。本研究结果有助于提高玉米品种选择效率，并为田间尺度精细作物管理的无人机高通量表型技术提供一些参考。研究结果证明了玉米品种的育种效率，并为田间尺度作物管理中的无人机高通量表型技术提供了一定参考。
