基于无人机高光谱图像和倒伏特征的水稻产量分类

摘要：水稻高产栽培是解决全球粮食需求增长的有效途径。对高产水稻进行正确的分类是育种的关键步骤。然而，在育种 计划中，人工测量耗时长，成本高，产量低，限制了在大规模田间表型分析中的应用。在这项研究中，我们开发了一种 精确的大规模方法，并提出了使用XGBoost算法来测量水稻产量的高光谱数据的潜在用途，以加快许多育种者的水稻育 种过程。在华北地区试验的13个粳稻品系中，根据人工产量测定将其划分为不同的品类。利用搭载高光谱相机的无人机 (UAV)平台进行多时间序列图像采集，提出了一种基于 XGBoost 算法的水稻产量分类模型。通过线内试验和线间试验进 行了考虑和不考虑中成熟期倒伏特性的 4 个对比试验。结果表明，水稻中熟期的倒伏程度是影响分类精度的重要特征。 因此，我们将基于无人机的高光谱测量与机器学习相结合，开发了一种低成本，高通量和无损的水稻产量估计方法，以 提高水稻育种效率。

期刊：Plant Phenomics

DOI：https://DOI.org/10.34133/2021/9765952

设备：GaiaSky

Classification of Rice Yield Using UAV-Based Hyperspectral Imagery and Lodging Feature

Abstract：High-yield rice cultivation is an effective way to address the increasing food demand worldwide. Correct classification of high-yield rice is a key step of breeding. However, manual measurements within breeding programs are time consuming and have high cost and lowthroughput, which limit the application in large-scale field phenotyping. In this study, we developed an accurate large-scale approach and presented the potential usage of hyperspectral data for rice yield measurement using the XGBoost algorithm to speed up the rice breeding process for many breeders. In total, 13 japonica rice lines in regional trials in northern China were divided into different categories according to the manual measurement of yield. Using an Unmanned Aerial Vehicle (UAV) platform equipped with a hyperspectral camera to capture images over multiple time series, a rice yield classification model based on the XGBoost algorithm was proposed. Four comparison experiments were carried out through the intraline test and the interline test considering lodging characteristics at the midmature stage or not. The result revealed that the degree of lodging in the midmature stage was an important feature affecting the classification accuracy of rice. Thus, we developed a low-cost, high-throughput phenotyping and nondestructive method by combining UAV-based hyperspectral measurements and machine learning for estimation of rice yield to improve rice breeding efficiency.