ArticleJournal of integrative bioinformatics2017
Digital Biomass Accumulation Using High-Throughput Plant Phenotype Data Analysis.
Article in Journal of integrative bioinformatics, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- The use of chitosan oligosaccharide to improve artemisinin yield in well-watered and drought-stressed plants.Frontiers in plant science · 2023Article
- Phenomics based prediction of plant biomass and leaf area in wheat using machine learning approaches.Frontiers in plant science · 2023Article
- High-throughput phenotyping to dissect genotypic differences in safflower for drought tolerance.PloS one · 2021Article
- Use of Phenomics for Differentiation of Mungbean (Frontiers in plant science · 2021Article
- Integration of Phenomics and Metabolomics Datasets Reveals Different Mode of Action of Biostimulants Based on Protein Hydrolysates inFrontiers in plant science · 2021Article
- Understanding the Biostimulant Action of Vegetal-Derived Protein Hydrolysates by High-Throughput Plant Phenotyping and Metabolomics: A Case Study on Tomato.Frontiers in plant science · 2019Article
- Characterization of Biostimulant Mode of Action Using Novel Multi-Trait High-Throughput Screening ofFrontiers in plant science · 2018Article
- Biological Big Bytes: Integrative Analysis of Large Biological Datasets.Journal of integrative bioinformatics · 2017Article
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Biomass is an important phenotypic trait in functional ecology and growth analysis. The typical methods for measuring biomass are destructive, and they require numerous individuals to be cultivated for repeated measurements. With the advent of image-based high-throughput plant phenotyping facilities, non-destructive biomass measuring methods have attempted to overcome this problem. Thus, the estimation of plant biomass of individual plants from their digital images is becoming more important. In this paper, we propose an approach to biomass estimation based on image derived phenotypic traits. Several image-based biomass studies state that the estimation of plant biomass is only a linear function of the projected plant area in images. However, we modeled the plant volume as a function of plant area, plant compactness, and plant age to generalize the linear biomass model. The obtained results confirm the proposed model and can explain most of the observed variance during image-derived biomass estimation. Moreover, a small difference was observed between actual and estimated digital biomass, which indicates that our proposed approach can be used to estimate digital biomass accurately.
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Registered trials
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