ArticlePlant methods2019
Estimation of crop plant density at early mixed growth stages using UAV imagery.
Article in Plant methods, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- Towards precision agriculture for assessing germination rates and density of rice seedling using hierarchical convolutional neural network on drone imagery.Scientific reports · 2026Article
- Visualizing Plant Responses: Novel Insights Possible Through Affordable Imaging Techniques in the Greenhouse.Sensors (Basel, Switzerland) · 2024Article
- Integrating Automated Labeling Framework for Enhancing Deep Learning Models to Count Corn Plants Using UAS Imagery.Sensors (Basel, Switzerland) · 2024Article
- Improving the accuracy of cotton seedling emergence rate estimation by fusing UAV-based multispectral vegetation indices.Frontiers in plant science · 2024Article
- A cooperative scheme for late leaf spot estimation in peanut using UAV multispectral images.PloS one · 2023Article
- Music Choreography Algorithm Based on Feature Matching and Fragment Segmentation.Computational intelligence and neuroscience · 2022Article
- Scaling up high-throughput phenotyping for abiotic stress selection in the field.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2021Review
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4 authors.
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No grant is acknowledged in the PubMed record.
Abstract
backgroundUnmanned aerial vehicles (UAVs) equipped with lightweight sensors are making a significant impact in field-based crop phenotyping. UAV platforms have been successfully deployed to acquire phenotypic data in a precise and efficient manner that would otherwise be time-consuming and costly to acquire when undertaken through manual assessment. One example is the estimation of plant density (or counts) in field experiments. Challenges posed to digital plant counting models are heterogenous germination and mixed growth stages that are present in field experiments with diverse genotypes. Here we describe, using safflower as an example, a method based on template matching for seedling count estimation at early mixed growth stages using UAV imagery.
resultsAn object-based image analysis algorithm based on template matching was developed for safflower seedling detection at early mixed growth stages in field experiments conducted in 2017 and 2018. Seedling detection was successful when tested using a grouped template type with 10 subgroups representing safflower at 2-4 leaves growth stage in 100 selected plots from the 2017 field experiment. The algorithm was validated for 300 plots each from the 2017 and 2018 field experiments, where estimated seedling counts correlated closely with manual counting; R
conclusionA method for safflower seedling count at early mixed growth stages using UAV imagery was developed and validated. The model performed well across heterogenous growth stages and has the potential to be used for plant density estimation across various crop species.
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