FlowerPhenoNet: Automated Flower Detection from Multi-View Image Sequences Using Deep Neural Networks for Temporal Plant Phenotyping Analysis

Sruti Das Choudhury, Samarpan Guha, Aankit Das, Amit Kumar Das, Ashok Samal, Tala Awada

Research output: Contribution to journalArticlepeer-review

4 Scopus citations

Abstract

A phenotype is the composite of an observable expression of a genome for traits in a given environment. The trajectories of phenotypes computed from an image sequence and timing of important events in a plant’s life cycle can be viewed as temporal phenotypes and indicative of the plant’s growth pattern and vigor. In this paper, we introduce a novel method called FlowerPhenoNet, which uses deep neural networks for detecting flowers from multiview image sequences for high-throughput temporal plant phenotyping analysis. Following flower detection, a set of novel flower-based phenotypes are computed, e.g., the day of emergence of the first flower in a plant’s life cycle, the total number of flowers present in the plant at a given time, the highest number of flowers bloomed in the plant, growth trajectory of a flower, and the blooming trajectory of a plant. To develop a new algorithm and facilitate performance evaluation based on experimental analysis, a benchmark dataset is indispensable. Thus, we introduce a benchmark dataset called FlowerPheno, which comprises image sequences of three flowering plant species, e.g., sunflower, coleus, and canna, captured by a visible light camera in a high-throughput plant phenotyping platform from multiple view angles. The experimental analyses on the FlowerPheno dataset demonstrate the efficacy of the FlowerPhenoNet.

Original languageEnglish (US)
Article number6252
JournalRemote Sensing
Volume14
Issue number24
DOIs
StatePublished - Dec 2022

Keywords

  • benchmark dataset
  • deep neural network
  • flower detection
  • flower status report
  • high-throughput plant phenotyping
  • temporal phenotypes

ASJC Scopus subject areas

  • General Earth and Planetary Sciences

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