ArticleCommunications biology2020
Training instance segmentation neural network with synthetic datasets for crop seed phenotyping.
Article in Communications biology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 33 papers, 1 of them a synthesis that pooled it.
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Who cites it
33 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Genomic co-localization of variation affecting agronomic and human gut microbiome traits in a meta-analysis of diverse sorghum.G3 (Bethesda, Md.) · 2024Pooled it
- Harnessing Artificial Intelligence and Machine Learning for Identifying Quantitative Trait Loci (QTL) Associated with Seed Quality Traits in Crops.Plants (Basel, Switzerland) · 2025Review
- PodNet: Pod real-time instance segmentation in pre-harvest soybean fields.Plant phenomics (Washington, D.C.) · 2025Article
- Deep Hair Phenomics: Implications in Endocrinology, Development, and Aging.The Journal of investigative dermatology · 2025Article
- MRI-Seed-Wizard: combining deep learning algorithms with magnetic resonance imaging enables advanced seed phenotyping.Journal of experimental botany · 2025Article
- Article
- VitiForge: a new procedural pipeline approach for grapevine disease identification under data scarcity.Frontiers in plant science · 2025Article
- Sensor-based characterization of construction and demolition waste at high occupancy densities using synthetic training data and deep learning.Waste management & research : the journal of the International Solid Wastes and Public Cleansing Association, ISWA · 2024Article
- An exploration of the influence of ZnO NPs treatment on germination of radish seeds under salt stress based on the YOLOv8-R lightweight model.Plant methods · 2024Article
- Deep learning for automated segmentation and counting of hypocotyl and cotyledon regions in matureFrontiers in plant science · 2024Article
- Phenotyping of Drought-Stressed Poplar Saplings Using Exemplar-Based Data Generation and Leaf-Level Structural Analysis.Plant phenomics (Washington, D.C.) · 2024Article
- Deep learning-based high-throughput detection of in vitro germination to assess pollen viability from microscopic images.Journal of experimental botany · 2023Article
- UTILE-Gen: Automated Image Analysis in Nanoscience Using Synthetic Dataset Generator and Deep Learning.ACS nanoscience Au · 2023Article
- Convolutional neural networks in the qualitative improvement of sweet potato roots.Scientific reports · 2023Article
- Image-Based High-Throughput Phenotyping in Horticultural Crops.Plants (Basel, Switzerland) · 2023Review
- Automatic Image Generation Pipeline for Instance Segmentation of Deformable Linear Objects.Sensors (Basel, Switzerland) · 2023Article
- Classification of Fluorescently Labelled Maize Kernels Using Convolutional Neural Networks.Sensors (Basel, Switzerland) · 2023Article
- Deep Learning Enables Instant and Versatile Estimation of Rice Yield Using Ground-Based RGB Images.Plant phenomics (Washington, D.C.) · 2023Article
- Multipurpose monitoring system for edible insect breeding based on machine learning.Scientific reports · 2022Article
- High-throughput measurement of plant fitness traits with an object detection method using Faster R-CNN.The New phytologist · 2022Article
Corrections and comments
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Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
In order to train the neural network for plant phenotyping, a sufficient amount of training data must be prepared, which requires time-consuming manual data annotation process that often becomes the limiting step. Here, we show that an instance segmentation neural network aimed to phenotype the barley seed morphology of various cultivars, can be sufficiently trained purely by a synthetically generated dataset. Our attempt is based on the concept of domain randomization, where a large amount of image is generated by randomly orienting the seed object to a virtual canvas. The trained model showed 96% recall and 95% average Precision against the real-world test dataset. We show that our approach is effective also for various crops including rice, lettuce, oat, and wheat. Constructing and utilizing such synthetic data can be a powerful method to alleviate human labor costs for deploying deep learning-based analysis in the agricultural domain.
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Registered trials
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.