Abstract
In this paper, we define a new problem domain, called visual growth tracking, to track different parts of an object that grow non-uniformly over space and time for application in image-based plant phenotyping. The paper introduces a novel method to reliably detect and track individual leaves of a maize plant based on a graph theoretic approach for automated leaf stage monitoring. The method has four phases: optimal view selection, plant architecture determination, leaf tracking, and generation of a leaf status report. The method accepts an image sequence of a plant as the input and automatically generates a leaf status report containing the phenotypes, which are crucial in the understanding of a plant’s growth, i.e., the emergence timing of each leaf, total number of leaves present at any time, the day on which a particular leaf ceased to grow, and the length and relative growth rate of individual leaves. Based on experimental study, three types of leaf intersections are identified, i.e., tip-contact, tangential-contact, and crossover, which pose challenges to accurate leaf tracking in the late vegetative stage. Thus, we introduce a novel curve tracing approach based on an angular consistency check to address the challenges due to intersecting leaves for improved performance. The proposed method shows high accuracy in detecting leaves and tracking them through the vegetative stages of maize plants based on experimental evaluation on a publicly available benchmark dataset.
Highlights
Visual tracking is an emerging research field that deals with the problem of localizing a pre-specified object in a video sequence
Motivated by the unavailability of any previous study on the automated growth stage determination of cereal crops, we introduce in this paper a novel algorithm to accurately detect the emergence timing of individual leaves and track them over the vegetative stage life cycle of the plant, based on plant architecture determination using a graph theoretic approach
We evaluated the performance of the proposed method using University of Nebraska-Lincoln (UNL)-CPPD-I and provide improvement directions to handling the leaf tracking challenges due to the presence of intersecting leaves using UNL-CPPD-II
Summary
Visual tracking is an emerging research field that deals with the problem of localizing a pre-specified object in a video sequence. Different plants exhibit different architectures, the complexity of which gradually increases with time This results in automated growth monitoring of a plant being challenging, as a whole and its parts (e.g., leaves, flowers, roots), based on image sequence analysis. The application of visual tracking in automated growth stage determination of economically important crops, e.g., maize and sorghum, for plant phenotyping is yet to be explored despite their role as the source of staple foods in most areas of the world
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