ArticlePlant methods2018
Estimation of vegetation indices for high-throughput phenotyping of wheat using aerial imaging.
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 22 papers.
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Who cites it
22 citing papers in PubMed.
- A cotton organ segmentation method with phenotypic measurements from a point cloud using a transformer.Plant methods · 2025Article
- Advancing Crop Resilience Through High-Throughput Phenotyping for Crop Improvement in the Face of Climate Change.Plants (Basel, Switzerland) · 2025Review
- Molecular breeding of tomato: Advances and challenges.Journal of integrative plant biology · 2025Review
- Reviewing the essential roles of remote phenotyping, GWAS and explainable AI in practical marker-assisted selection for drought-tolerant winter wheat breeding.Frontiers in plant science · 2024Review
- Enhancing the potential of phenomic and genomic prediction in winter wheat breeding using high-throughput phenotyping and deep learning.Frontiers in plant science · 2024Article
- Use of Acheta domesticus meal as a full soybean substitute in the feeding of slow-growing chicks.Poultry science · 2023Article
- Utilization of Spectral Indices for High-Throughput Phenotyping.Plants (Basel, Switzerland) · 2022Review
- Utilizing spectral vegetation indices for yield assessment of tomato genotypes grown in arid conditions.Saudi journal of biological sciences · 2022Article
- Statistical Methods for the Quantitative Genetic Analysis of High-Throughput Phenotyping Data.Methods in molecular biology (Clifton, N.J.) · 2022Article
- Advanced high-throughput plant phenotyping techniques for genome-wide association studies: A review.Journal of advanced research · 2022Review
- High-Throughput Phenotyping Methods for Breeding Drought-Tolerant Crops.International journal of molecular sciences · 2021Review
- Biomass estimation of cultivated red algae Pyropia using unmanned aerial platform based multispectral imaging.Plant methods · 2021Article
- Combining Genomic and Phenomic Information for Predicting Grain Protein Content and Grain Yield in Spring Wheat.Frontiers in plant science · 2021Article
- UAV-Based Thermal, RGB Imaging and Gene Expression Analysis Allowed Detection of Fusarium Head Blight and Gave New Insights Into the Physiological Responses to the Disease in Durum Wheat.Frontiers in plant science · 2021Article
- Increased Prediction Accuracy Using Combined Genomic Information and Physiological Traits in A Soft Wheat Panel Evaluated in Multi-Environments.Scientific reports · 2020Article
- Development and Validation of a Phenotyping Computational Workflow to Predict the Biomass Yield of a Large Perennial Ryegrass Breeding Field Trial.Frontiers in plant science · 2020Article
- A Physio-Morphological Trait-Based Approach for Breeding Drought Tolerant Wheat.Frontiers in plant science · 2020Review
- Combining self-organizing maps and biplot analysis to preselect maize phenotypic components based on UAV high-throughput phenotyping platform.Plant methods · 2019Article
- Computational aspects underlying genome to phenome analysis in plants.The Plant journal : for cell and molecular biology · 2019Review
- An Automatic Field Plot Extraction Method From Aerial Orthomosaic Images.Frontiers in plant science · 2019Article
Corrections and comments
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Authors and funding
5 authors.
Funding
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Abstract
backgroundUnmanned aerial vehicles offer the opportunity for precision agriculture to efficiently monitor agricultural land. A vegetation index (VI) derived from an aerially observed multispectral image (MSI) can quantify crop health, moisture and nutrient content. However, due to the high cost of multispectral sensors, alternate, low-cost solutions have lately received great interest. We present a novel method for model-based estimation of a VI using RGB color images. The non-linear spatio-spectral relationship between the RGB image of vegetation and the index computed by its corresponding MSI is learned through deep neural networks. The learned models can be used to estimate VI of a crop segment.
resultsAnalysis of images obtained in wheat breeding trials show that the aerially observed VI was highly correlated with ground-measured VI. In addition, VI estimates based on RGB images were highly correlated with VI deduced from MSIs. Spatial, spectral and temporal information of images contributed to estimation of VI. Both intra-variety and inter-variety differences were preserved by estimated VI. However, VI estimates were reliable until just before significant appearance of senescence.
conclusionThe proposed approach validates that it is reasonable to accurately estimate VI using deep neural networks. The results prove that RGB images contain sufficient information for VI estimation. It demonstrates that low-cost VI measurement is possible with standard RGB cameras.
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