Automated Leaf Tracking using Multi-view Image Sequences of Maize Plants for Leaf-growth Monitoring
Abstract
Extraction of phenotypes with botanical importance by analyzing plant image sequences has the desirable advantages of non-destructive temporal phenotypic measurements of a large number of plants with little or no manual intervention in a relatively short period of time. The health of a plant is best interpreted by the emergence timing and temporal growth of individual leaves. For automated leaf growth monitoring, it is essential to track each leaf throughout the life cycle of the plant. Plants are constantly changing organisms with increasing complexity in architecture due to variations in self-occlusions and phyllotaxy, i.e., arrangements of leaves around the stem. The leaf cross-overs pose challenges to accurately track each leaf using single view image sequence. Thus, we introduce a novel automated leaf tracking algorithm using a graph theoretic approach by multi-view image sequence analysis based on the determination of leaf-tips and leaf-junctions in the 3D space. The basis of the leaf tracking algorithm is: the leaves emerge using bottom-up approach in the case of a maize plant, and the direction of leaf emergence strictly alternates in terms of direction. The algorithm involves labeling of the individual parts of a plant, i.e., leaves and stem, following graphical representation of the plant skeleton, i.e., one-pixel wide connected line obtained from the binary image. The length of the leaf is measured by the number of pixels in the leaf skeleton. To evaluate the performance of the algorithm, a benchmark dataset is indispensable. Thus, we publicly release University of Nebraska-Lincoln Component Plant Phenotyping dataset-2 (UNL-CPPD-2) consisting of images of the 20 maize plants captured by visible light camera of the Lemnatec Scanalyzer 3D high throughout plant phenotyping facility once daily for 60 days from 10 different views. The dataset is aimed to facilitate the development and evaluation of leaf tracking algorithms and their uniform comparisons.
- Publication:
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AGU Fall Meeting Abstracts
- Pub Date:
- December 2017
- Bibcode:
- 2017AGUFM.B51A1781D
- Keywords:
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- 0402 Agricultural systems;
- BIOGEOSCIENCES;
- 0422 Bio-optics;
- BIOGEOSCIENCES;
- 0465 Microbiology: ecology;
- physiology and genomics;
- BIOGEOSCIENCES;
- 0480 Remote sensing;
- BIOGEOSCIENCES