High-quality images and data augmentation based on inverse projection transformation significantly improve the estimation accuracy of biomass and leaf area index

文献类型： 外文期刊

第一作者： Che, Yingpu

作者： Che, Yingpu;Wang, Qing;Xie, Ziwen;Li, Shilin;Li, Baoguo;Ma, Yuntao;Zhu, Jinyu

作者机构：

关键词： UAV; Maize; Inverse projection transformation; Data augmentation; Above-ground biomass; Leaf area index

期刊名称：COMPUTERS AND ELECTRONICS IN AGRICULTURE （ 影响因子：8.3； 五年影响因子：8.3 ）

ISSN： 0168-1699

年卷期： 2023 年 212 卷

页码：

收录情况： SCI

摘要： Estimating above-ground biomass (AGB) and leaf area index (LAI) is crucial for determining crop developmental status and estimating grain yield, thus facilitating high-throughput phenotyping of maize (Zea Mays L.). Recently, unmanned aerial vehicle (UAV) have been widely used to monitor crop growth and estimate LAI and AGB. However, few studies have reported on the quantitative analysis of improved accuracy in biomass and leaf area index estimation using undistorted images obtained through inverse projection transformation algorithms. Additionally, there is limited research on predicting biomass and LAI during various growth stages by replacing traditional data augmentation methods with images captured from different azimuth and zenith angles. The results showed that when using undistorted image at single solar zenith angle of 0 degrees, the accuracy of AGB and LAI estimation was improved by 20.3% and 7.3%, respectively, compared to the models estimated from orthoimages. The undistorted images with various azimuth and zenith angles were 9-15 times larger than the orthomosaic image dataset, and could be used as a new training data that is similar to the original data but with variations. The AGB and LAI estimation model constructed using the data augmentation method achieved better performance with higher average R2 (0.98). Model performance was increased by 40% and 16.7% for estimating AGB and by 19.3% and 11.3% for estimating LAI based on undistorted images, respectively, in comparison to models based on orthophotos and undistorted images due to the increased diversity and quantity of training data. Canopy structural including canopy coverage and plant height, characterized by 96.9%, was found to be the most reliable indication for estimating AGB and LAI, while other RGB sensor-derived feature combinations contributed only 3.1% to the estimation. Spectral features took the second place, and the textural features was the weakest. In summary, the data processing framework using data augmentation based on various azimuth and zenith angles' images can improve the performance of crop growth estimation and provide a mechanism for precision agriculture under field conditions.

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