ArticleFrontiers in plant science2016
Evaluation of the SeedCounter, A Mobile Application for Grain Phenotyping.
Article in Frontiers in plant science, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 26 papers.
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Who cites it
26 citing papers in PubMed.
- Integrating AI in seed science: Toward an intelligent design paradigm.Plant communications · 2026Review
- Investigating performance and key factors for real-world deployment of grain image classification using convolutional neural networks.Scientific reports · 2026Article
- SeedSeg: image-based transgenic seed counting for segregation analysis of T-DNA loci.Plant methods · 2025Article
- Analysis of coat texture characteristics of bread wheat grains obtained from digital images.Frontiers in plant science · 2025Article
- An android-smartphone application for rice panicle detection and rice growth stage recognition using a lightweight YOLO network.Frontiers in plant science · 2025Article
- Identification of Novel Loci Precisely Modulating Pre-Harvest Sprouting Resistance and Red Color Components of the Seed Coat inPlants (Basel, Switzerland) · 2024Article
- QTL Analysis for Bread Wheat Seed Size, Shape and Color Characteristics Estimated by Digital Image Processing.Plants (Basel, Switzerland) · 2022Article
- Deep learning based high-throughput phenotyping of chalkiness in rice exposed to high night temperature.Plant methods · 2022Article
- An Intelligent Analysis Method for 3D Wheat Grain and Ventral Sulcus Traits Based on Structured Light Imaging.Frontiers in plant science · 2022Article
- Fine Mapping of a Vigor QTL in Chickpea (Frontiers in plant science · 2022Article
- Article
- Phenotyping Fusarium head blight through seed morphology characteristics using RGB imaging.Frontiers in plant science · 2022Article
- Relationship between the Characteristics of Bread Wheat Grains, Storage Time and Germination.Plants (Basel, Switzerland) · 2021Article
- Integrating Genomic and Phenomic Approaches to Support Plant Genetic Resources Conservation and Use.Plants (Basel, Switzerland) · 2021Review
- TaGRF3-2A Improves Some Agronomically Valuable Traits in Semi-Dwarf Spring Triticale.Plants (Basel, Switzerland) · 2021Article
- Computer Vision and Machine Learning Analysis of Commercial Rice Grains: A Potential Digital Approach for Consumer Perception Studies.Sensors (Basel, Switzerland) · 2021Article
- Review
- GridFree: a python package of imageanalysis for interactive grain counting and measuring.Plant physiology · 2021Article
- Effects of Rht17 in combination with Vrn-B1 and Ppd-D1 alleles on agronomic traits in wheat in black earth and non-black earth regions.BMC plant biology · 2020Article
- QuantifyingBiology open · 2020Article
Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Grain morphometry in cereals is an important step in selecting new high-yielding plants. Manual assessment of parameters such as the number of grains per ear and grain size is laborious. One solution to this problem is image-based analysis that can be performed using a desktop PC. Furthermore, the effectiveness of analysis performed in the field can be improved through the use of mobile devices. In this paper, we propose a method for the automated evaluation of phenotypic parameters of grains using mobile devices running the Android operational system. The experimental results show that this approach is efficient and sufficiently accurate for the large-scale analysis of phenotypic characteristics in wheat grains. Evaluation of our application under six different lighting conditions and three mobile devices demonstrated that the lighting of the paper has significant influence on the accuracy of our method, unlike the smartphone type.
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