ArticlePlant methods2018
Holistic and component plant phenotyping using temporal image sequence.
Article in Plant methods, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 33 papers.
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33 citing papers in PubMed.
- Introducing Concurrent Imaging and Unidimensional Analytics for Plant Stress Responses.Plants (Basel, Switzerland) · 2026Article
- Seeing the unseen: A novel approach to extract latent plant root traits from digital images.Plant phenomics (Washington, D.C.) · 2025Article
- Innovative multi-scale approach to study the phenotypic variation of seedling leaves in four weedy Amaranthus species.Plant biology (Stuttgart, Germany) · 2025Article
- GCNet: A Deep Learning Framework for Enhanced Grape Cluster Segmentation and Yield Estimation Incorporating Occluded Grape Detection with a Correction Factor for Indoor Experimentation.Journal of imaging · 2025Article
- An automated phenotyping method for Chinese Cymbidium seedlings based on 3D point cloud.Plant methods · 2024Article
- Mapping and quantifying unique branching structures in lentil (Lens culinaris Medik.).Plant methods · 2024Article
- Stress phenotyping analysis leveraging autofluorescence image sequences with machine learning.Frontiers in plant science · 2024Article
- Early Identification of Root Damages Caused by Western Corn Rootworms Using a Minimally Invasive Root Phenotyping Robot-MISIRoot.Sensors (Basel, Switzerland) · 2023Article
- How to make sense of 3D representations for plant phenotyping: a compendium of processing and analysis techniques.Plant methods · 2023Review
- An Open-Source Package for Thermal and Multispectral Image Analysis for Plants in Glasshouse.Plants (Basel, Switzerland) · 2023Article
- High-Throughput Field Plant Phenotyping: A Self-Supervised Sequential CNN Method to Segment Overlapping Plants.Plant phenomics (Washington, D.C.) · 2023Article
- EmergeNet: A novel deep-learning based ensemble segmentation model for emergence timing detection of coleoptile.Frontiers in plant science · 2023Article
- Drought stress prediction and propagation using time series modeling on multimodal plant image sequences.Frontiers in plant science · 2023Article
- OSC-COFrontiers in plant science · 2023Article
- Plant science in the age of simulation intelligence.Frontiers in plant science · 2023Article
- Benchmarking Self-Supervised Contrastive Learning Methods for Image-Based Plant Phenotyping.Plant phenomics (Washington, D.C.) · 2023Article
- Proximal and remote sensing in plant phenomics: 20 years of progress, challenges, and perspectives.Plant communications · 2022Review
- Deep Learning Based Greenhouse Image Segmentation and Shoot Phenotyping (DeepShoot).Frontiers in plant science · 2022Article
- Discovery of spatial pattern of prickles on stem of Rosa hybrida 'Red Queen' and mathematical model of the pattern.Scientific reports · 2021Article
- Maize-IAS: a maize image analysis software using deep learning for high-throughput plant phenotyping.Plant methods · 2021Article
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5 authors.
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Abstract
backgroundImage-based plant phenotyping facilitates the extraction of traits noninvasively by analyzing large number of plants in a relatively short period of time. It has the potential to compute advanced phenotypes by considering the whole plant as a single object (holistic phenotypes) or as individual components, i.e., leaves and the stem (component phenotypes), to investigate the biophysical characteristics of the plants. The emergence timing, total number of leaves present at any point of time and the growth of individual leaves during vegetative stage life cycle of the maize plants are significant phenotypic expressions that best contribute to assess the plant vigor. However, image-based automated solution to this novel problem is yet to be explored.
resultsA set of new holistic and component phenotypes are introduced in this paper. To compute the component phenotypes, it is essential to detect the individual leaves and the stem. Thus, the paper introduces a novel method to reliably detect the leaves and the stem of the maize plants by analyzing 2-dimensional visible light image sequences captured from the side using a graph based approach. The total number of leaves are counted and the length of each leaf is measured for all images in the sequence to monitor leaf growth. To evaluate the performance of the proposed algorithm, we introduce University of Nebraska-Lincoln Component Plant Phenotyping Dataset (UNL-CPPD) and provide ground truth to facilitate new algorithm development and uniform comparison. The temporal variation of the component phenotypes regulated by genotypes and environment (i.e., greenhouse) are experimentally demonstrated for the maize plants on UNL-CPPD. Statistical models are applied to analyze the greenhouse environment impact and demonstrate the genetic regulation of the temporal variation of the holistic phenotypes on the public dataset called Panicoid Phenomap-1.
conclusionThe central contribution of the paper is a novel computer vision based algorithm for automated detection of individual leaves and the stem to compute new component phenotypes along with a public release of a benchmark dataset, i.e., UNL-CPPD. Detailed experimental analyses are performed to demonstrate the temporal variation of the holistic and component phenotypes in maize regulated by environment and genetic variation with a discussion on their significance in the context of plant science.
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