Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

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

Version 1 : Received: 27 October 2022 / Approved: 31 October 2022 / Online: 31 October 2022 (10:00:24 CET)

A peer-reviewed article of this Preprint also exists.

Das Choudhury, S.; Guha, S.; Das, A.; Das, A.K.; Samal, A.; Awada, T. FlowerPhenoNet: Automated Flower Detection from Multi-View Image Sequences Using Deep Neural Networks for Temporal Plant Phenotyping Analysis. Remote Sens. 2022, 14, 6252. Das Choudhury, S.; Guha, S.; Das, A.; Das, A.K.; Samal, A.; Awada, T. FlowerPhenoNet: Automated Flower Detection from Multi-View Image Sequences Using Deep Neural Networks for Temporal Plant Phenotyping Analysis. Remote Sens. 2022, 14, 6252.

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.

Keywords

High Throughput Plant Phenotyping; Deep Neural Network; Flower Detection; Temporal Phenotypes; Benchmark Dataset; Flower Status Report

Subject

Computer Science and Mathematics, Analysis

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