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

• Published in: Remote sensing (Basel, Switzerland) Switzerland), 2022-12, Vol.14 (24), p.6252
• Main Authors: Das Choudhury, Sruti, Guha, Samarpan, Das, Aankit, Das, Amit Kumar, Samal, Ashok, Awada, Tala
• Format: Article
• Language: English
• Subjects: Algorithms; Artificial neural networks; Automation; benchmark dataset; Benchmarks; Computation; Datasets; Deep learning; deep neural network; flower detection; flower status report; Flowering; Flowering plants; Flowers; Flowers & plants; Fruits; Gene expression; Genomes; Growth patterns; high-throughput plant phenotyping; Image sequencing; Life cycles; Neural networks; Performance evaluation; Phenotypes; Phenotyping; Physiology; Plant sciences; Plant species; Plants (botany); Remote sensing; Senescence; temporal phenotypes; Time series; Unmanned aerial vehicles
• ISSN: 2072-4292; 2072-4292
• DOI: 10.3390/rs14246252

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.