基于经济型低空无人机对小麦重要产量表型性状的多生育时期获取和自动化分析

doi:10.19788/j.issn.2096-6369.190202

Abstract

Abstract:

[Multi-scale plant phenotyping technologies are capable of collecting big vision-based and spectroscopic datasets, based on which reliable phenotypic analysis can be carried out through a range of computational algorithms based on computer vision and machine learning techniques. Quantitative traits measurement is key to crop genetics, breeding, cultivation and agricultural practices, because they can be used to dynamically evaluate yield, quality and stress resistance in a high-throughput and reproducible manner. As an important staple crop in China, it is essential to establish a systematic approach to monitor wheat growth and quantify yield-related traits during key growth stages. In this work, we firstly reviewed important yield-related traits for bread wheat and then developed a field phenotyping approach to collect a number of common traits using cost-effective and low-altitude unmanned aerial vehicles (UAVs). Based on the visible spectrum images acquired in a field experiment, we utilized professional software (i.e. Pix4Dmapper) to stitch UAV sub-images as well as to reconstruct 3D point cloud to represent the whole experimental field. After this phase, we developed an automated traits analysis pipeline to produce the vegetation map (e.g. Excess-Green index, ExG) and measure important yield-related traits. We have quantified plant height, vegetation index (e.g. ExG) and leaf area index at five key growth stages for 18 wheat genotypes. Our work validates that yield-related traits can be acquired through cost-effective UAVs, which can lower the threshold of conducting field phenotyping and reliable phenotypic analysis. Our work also exhibits a promising approach for research groups and organizations to follow standardize data collection, phenotyping data ontology, as well as the utilization of open-source analytic libraries to develop high-throughput phenotypic analysis techniques in crop phenotyping research.

Key words: wheat, yield-related traits, UAV aerial phenotyping, high-throughput phenotypic analysis, image processing, phenotyping, plant phenomics

CLC Number:

Guohui Ding,Hao Xu,Mingxing Wen,Jiawei Chen,Xiue Wang,Ji Zhou. Developing cost-effective and low-altitude UAV aerial phenotyping and automated phenotypic analysis to measure key yield-related traits for bread wheat[J].Journal of Agricultural Big Data, 2019, 1(2): 19-31.

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生育时期	小麦产量性状
分蘖期	分孽形态、株高、叶色、叶面积指数、植被指数。
拔节期	株高、叶色、叶面积指数、植被指数。
孕穗期	株高、叶色、叶面积指数、植被指数、旗叶形态。
抽穗期	株高、叶色、叶面积指数、植被指数。
开花期	株高、叶色、叶面积指数、植被指数。

Developing cost-effective and low-altitude UAV aerial phenotyping and automated phenotypic analysis to measure key yield-related traits for bread wheat

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