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

doi:10.12133/j.smartag.2020.2.1.202002-SA004

Abstract

Abstract:

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 FIPAR_dif (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 FIPAR_dif (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

CLC Number:

TP391.4

Liu Shouyang, Jin Shichao, Guo Qinghua, Zhu Yan, Baret Fred. An algorithm for estimating field wheat canopy light interception based on Digital Plant Phenotyping Platform[J]. Smart Agriculture, 2020, 2(1): 87-98.

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