ArticleThe New phytologist2022
High-throughput measurement of plant fitness traits with an object detection method using Faster R-CNN.
Article in The New phytologist, 2022. 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.
- Computer Vision from Tea Cultivation to Quality Evaluation.Foods (Basel, Switzerland) · 2026Review
- Evaluating phenotypic plasticity of reproductive traits among Korean rice cultivars under diverse climatic conditions.Frontiers in plant science · 2026Article
- The analysis of landscape design and plant selection under deep learning.Scientific reports · 2025Article
- SeedSeg: image-based transgenic seed counting for segregation analysis of T-DNA loci.Plant methods · 2025Article
- Deep learning driven, image-based phenotyping of seed processing efficiency in sainfoin (Frontiers in plant science · 2025Article
- Ecological trade-offs drive phenotypic and genetic differentiation of Arabidopsis thaliana in Europe.Nature communications · 2024Article
- Phenotyping of Drought-Stressed Poplar Saplings Using Exemplar-Based Data Generation and Leaf-Level Structural Analysis.Plant phenomics (Washington, D.C.) · 2024Article
- An iterative noisy annotation correction model for robust plant disease detection.Frontiers in plant science · 2023Article
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Authors and funding
11 authors.
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Abstract
Revealing the contributions of genes to plant phenotype is frequently challenging because loss-of-function effects may be subtle or masked by varying degrees of genetic redundancy. Such effects can potentially be detected by measuring plant fitness, which reflects the cumulative effects of genetic changes over the lifetime of a plant. However, fitness is challenging to measure accurately, particularly in species with high fecundity and relatively small propagule sizes such as Arabidopsis thaliana. An image segmentation-based method using the software ImageJ and an object detection-based method using the Faster Region-based Convolutional Neural Network (R-CNN) algorithm were used for measuring two Arabidopsis fitness traits: seed and fruit counts. The segmentation-based method was error-prone (correlation between true and predicted seed counts, r
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