Land-based crop phenotyping by image analysis: Accurate estimation of canopy height distributions using stereo images.

David A Lightfoot,Jinhai Cai,Joshua Chopin,Stanley J Miklavcic,Pankaj Kumar

doi:10.1371/journal.pone.0196671

Abstract

In this paper we report on an automated procedure to capture and characterize the detailed structure of a crop canopy by means of stereo imaging. We focus attention specifically on the detailed characteristic of canopy height distribution—canopy shoot area as a function of height—which can provide an elaborate picture of canopy growth and health under a given set of conditions. We apply the method to a wheat field trial involving ten Australian wheat varieties that were subjected to two different fertilizer treatments. A novel camera self-calibration approach is proposed which allows the determination of quantitative plant canopy height data (as well as other valuable phenotypic information) by stereo matching. Utilizing the canopy height distribution to provide a measure of canopy height, the results compare favourably with manual measurements of canopy height (resulting in an R2 value of 0.92), and are indeed shown to be more consistent. By comparing canopy height distributions of different varieties and different treatments, the methodology shows that different varieties subjected to the same treatment, and the same variety subjected to different treatments can respond in much more distinctive and quantifiable ways within their respective canopies than can be captured by a simple trait measure such as overall canopy height.

Highlights

Plant breeders seek to identify new cereal plant varieties with potential for increased biomass, grain yield and greater resilience to adverse environmental conditions
In this paper we report on an automated procedure to capture and characterize the detailed structure of a crop canopy by means of stereo imaging
We focus attention on the detailed characteristic of canopy height distribution—canopy shoot area as a function of height—which can provide an elaborate picture of canopy growth and health under a given set of conditions

Summary

Introduction

Plant breeders seek to identify new cereal plant varieties with potential for increased biomass, grain yield and greater resilience to adverse environmental conditions. To support plant breeder efforts and to accelerate the plant breeding process itself, new software methodologies and hardware technologies for improved genotyping and phenotyping need to be developed [1]. Given the advances made in modern genetics and genomics during the last two decades, the bottleneck would appear to lie with the crop phenotyping pipeline. One small step toward achieving the ideal pipeline involves the quantitative tracking of a multitude of phenotypic traits during a season. In the particular context of in situ field studies and of cereal crop assessment, a number of determinant phenotypic crop traits are relevant: canopy coverage, canopy height, canopy health (NDVI), as well as the appearance of major growth stages.

Methods

Results

Conclusion