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Smart Agriculture ›› 2020, Vol. 2 ›› Issue (1): 87-98.doi: 10.12133/j.smartag.2020.2.1.202002-SA004

• Topic--Agricultural Remote Sensing and Phenotyping Information Acquisition Analysis • Previous Articles     Next Articles

An algorithm for estimating field wheat canopy light interception based on Digital Plant Phenotyping Platform

Liu Shouyang1,2,3(), Jin Shichao5,6, Guo Qinghua5,6, Zhu Yan4, Baret Fred1,2,3()   

  1. 1.Plant Phenomics Research Centre, Nanjing Agricultural University, Nanjing 210095, China
    2.INRAE, EMMAH-CAPTE, Avignon 84914, France
    3.Jiangsu Collaborative Innovation Centre for Modern Crop Production, Nanjing Agricultural University, Nanjing 210095, China
    4.Nanjing Engineering and Technology Centre for Information Agriculture, Nanjing Agricultural University/Engineering Research Centre for Smart Agriculture, Ministry of Education, Nanjing 210095, China
    5.State Key Laboratory of Vegetation and Environmental Change, Institute of Botany, Chinese Academy of Sciences, Beijing 100093, China
    6.University of Chinese Academy of Sciences, Beijing 100049, China
  • Received:2020-02-12 Revised:2020-03-01 Online:2020-03-30
  • Supported by:
    Nanjing Agricultural University High-Level Talent Introduction Fund (440-804005)


The capacity of canopy light interception is a key functional trait to distinguish the phenotypic variation over genotypes. High-throughput phenotyping canopy light interception in the field, therefore, would be of high interests for breeders to increase the efficiency of crop improvement. In this research, the Digital Plant Phenotyping Platform(D3P) was used to conduct in-silico phenotyping experiment with LiDAR scans over a wheat field. In this experiment virtual 3D wheat canopies were generated over 100 wheat genotypes for 5 growth stages, representing wide range of canopy structural variation. Accordingly, the actual value of traits targeted were calculated including GAI (green area index), AIA (average inclination angle) and FIPARdif (the fraction of intercepted diffuse photosynthetically activate radiation). Then, virtual LiDAR scanning were accomplished over all the treatments and exported as 3D point cloud. Two types of features were extracted from point cloud, including height quantiles (H) and green fractions (GF). Finally, an artificial neural network was trained to predict the traits targeted from different combinations of LiDAR features. Results show that the prediction accuracy varies with the selection of input features, following the rank as GF + H > H > GF. Regarding the three traits, we achieved satisfactory accuracy for FIPARdif (R2=0.95) and GAI (R2=0.98) but not for AIA (R2=0.20). This highlights the importance of H feature with respect to the prediction accuracy. The results achieved here are based on in-silico experiments, further evaluation with field measurement would be necessary. Nontheless, as proof of concept, this work further demonstrates that D3P could greatly facilitate the algorithm development. Morever, it highlights the potential of LiDAR measurement in the high-throuhgput phenopyting of canopy light interpcetion and structural traits in the field.

Key words: canopy light interception, high-throughput phenotyoing, LiDAR, Digital Plant Phenotyping Platform (D3P), wheat canopy

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