ArticleJournal of experimental botany2016
Image Harvest: an open-source platform for high-throughput plant image processing and analysis.
Article in Journal of experimental botany, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 32 papers.
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Who cites it
32 citing papers in PubMed, 97 citations in OpenAlex.
- Artificial intelligence in plant science: from image-based phenotyping to yield and trait prediction.Frontiers in plant science · 2025Review
- Current methods and future needs for visible and non-visible detection of plant stress responses.Frontiers in plant science · 2025Review
- From single- to multi-omics: future research trends in medicinal plants.Briefings in bioinformatics · 2023Article
- Genetic resources and breeding of maize forFrontiers in plant science · 2023Review
- PhytoOracle: Scalable, modular phenomics data processing pipelines.Frontiers in plant science · 2023Article
- Multi-Source Data Fusion Improves Time-Series Phenotype Accuracy in Maize under a Field High-Throughput Phenotyping Platform.Plant phenomics (Washington, D.C.) · 2023Article
- Proximal and remote sensing in plant phenomics: 20 years of progress, challenges, and perspectives.Plant communications · 2022Review
- Development of a plant conveyance system using an AGV and a self-designed plant-handling device: A case study of DIY plant phenotyping.Breeding science · 2022Article
- Gene Region Association Analysis of Longitudinal Quantitative Traits Based on a Function-On-Function Regression Model.Frontiers in genetics · 2022Article
- Ready, Steady, Go AI: A practical tutorial on fundamentals of artificial intelligence and its applications in phenomics image analysis.Patterns (New York, N.Y.) · 2021Review
- Maize-IAS: a maize image analysis software using deep learning for high-throughput plant phenotyping.Plant methods · 2021Article
- Detection of the Progression of Anthesis in Field-Grown Maize Tassels: A Case Study.Plant phenomics (Washington, D.C.) · 2021Article
- Leveraging genome-enabled growth models to study shoot growth responses to water deficit in rice.Journal of experimental botany · 2020Article
- Wheat ear counting using K-means clustering segmentation and convolutional neural network.Plant methods · 2020Article
- Article
- Development of a novel and rapid phenotype-based screening method to assess rice seedling growth.Plant methods · 2020Article
- A High-Throughput Phenotyping Pipeline for Image Processing and Functional Growth Curve Analysis.Plant phenomics (Washington, D.C.) · 2020Article
- Predicting Longitudinal Traits Derived from High-Throughput Phenomics in Contrasting Environments Using Genomic Legendre Polynomials and B-Splines.G3 (Bethesda, Md.) · 2019Article
- Leveraging Breeding Values Obtained from Random Regression Models for Genetic Inference of Longitudinal Traits.The plant genome · 2019Article
- Genetic and environmental dissection of biomass accumulation in multi-genotype maize canopies.Journal of experimental botany · 2019Article
Corrections and comments
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Authors and funding
5 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
High-throughput plant phenotyping is an effective approach to bridge the genotype-to-phenotype gap in crops. Phenomics experiments typically result in large-scale image datasets, which are not amenable for processing on desktop computers, thus creating a bottleneck in the image-analysis pipeline. Here, we present an open-source, flexible image-analysis framework, called Image Harvest (IH), for processing images originating from high-throughput plant phenotyping platforms. Image Harvest is developed to perform parallel processing on computing grids and provides an integrated feature for metadata extraction from large-scale file organization. Moreover, the integration of IH with the Open Science Grid provides academic researchers with the computational resources required for processing large image datasets at no cost. Image Harvest also offers functionalities to extract digital traits from images to interpret plant architecture-related characteristics. To demonstrate the applications of these digital traits, a rice (Oryza sativa) diversity panel was phenotyped and genome-wide association mapping was performed using digital traits that are used to describe different plant ideotypes. Three major quantitative trait loci were identified on rice chromosomes 4 and 6, which co-localize with quantitative trait loci known to regulate agronomically important traits in rice. Image Harvest is an open-source software for high-throughput image processing that requires a minimal learning curve for plant biologists to analyzephenomics datasets.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.