Wang Botao, Yang Chenghai, Zhang Jian, You Yunhao, Wang Hongming, Yang Wanneng
Macro Agriculture Research Institute, College of Resources and Environment, Huazhong Agricultural University, 1 Shizishan Street, Wuhan, Hubei 430070, China.
Key Laboratory of Farmland Conservation in the Middle and Lower Reaches of the Ministry of Agriculture, Wuhan, Hubei 430070, China.
Plant Phenomics. 2024 May 15;6:0164. doi: 10.34133/plantphenomics.0164. eCollection 2024.
With the threshold for crop growth data collection having been markedly decreased by sensor miniaturization and cost reduction, unmanned aerial vehicle (UAV)-based low-altitude remote sensing has shown remarkable advantages in field phenotyping experiments. However, the requirement of interdisciplinary knowledge and the complexity of the workflow have seriously hindered researchers from extracting plot-level phenotypic data from multisource and multitemporal UAV images. To address these challenges, we developed the Integrated High-Throughput Universal Phenotyping (IHUP) software as a data producer and study accelerator that included 4 functional modules: preprocessing, data extraction, data management, and data analysis. Data extraction and analysis requiring complex and multidisciplinary knowledge were simplified through integrated and automated processing. Within a graphical user interface, users can compute image feature information, structural traits, and vegetation indices (VIs), which are indicators of morphological and biochemical traits, in an integrated and high-throughput manner. To fulfill data requirements for different crops, extraction methods such as VI calculation formulae can be customized. To demonstrate and test the composition and performance of the software, we conducted case-related rice drought phenotype monitoring experiments. In combination with a rice leaf rolling score predictive model, leaf rolling score, plant height, VIs, fresh weight, and drought weight were efficiently extracted from multiphase continuous monitoring data. Despite the significant impact of image processing during plot clipping on processing efficiency, the software can extract traits from approximately 500 plots/min in most application cases. The software offers a user-friendly graphical user interface and interfaces for customizing or integrating various feature extraction algorithms, thereby significantly reducing barriers for nonexperts. It holds the promise of significantly accelerating data production in UAV phenotyping experiments.
随着传感器小型化和成本降低，作物生长数据收集的阈值显著降低，基于无人机（UAV）的低空遥感在田间表型实验中显示出显著优势。然而，跨学科知识的需求和工作流程的复杂性严重阻碍了研究人员从多源和多时间的无人机图像中提取地块级表型数据。为应对这些挑战，我们开发了集成高通量通用表型分析（IHUP）软件，作为数据生成器和研究加速器，它包括4个功能模块：预处理、数据提取、数据管理和数据分析。通过集成和自动化处理，简化了需要复杂多学科知识的数据提取和分析。在图形用户界面中，用户可以以集成和高通量的方式计算图像特征信息、结构性状和植被指数（VI），这些指数是形态和生化性状的指标。为满足不同作物的数据需求，可以定制诸如VI计算公式等提取方法。为了演示和测试该软件的组成和性能，我们进行了与水稻干旱表型监测相关的案例实验。结合水稻叶片卷曲评分预测模型，从多阶段连续监测数据中高效提取了叶片卷曲评分、株高、植被指数、鲜重和干旱重量。尽管地块裁剪过程中的图像处理对处理效率有显著影响，但在大多数应用案例中，该软件仍能以每分钟约500个地块的速度提取性状。该软件提供了用户友好的图形用户界面以及用于定制或集成各种特征提取算法的接口，从而显著降低了非专业人员的使用门槛。它有望显著加速无人机表型实验中的数据生成。
