ArticleSensors (Basel, Switzerland)2020
The Performances of Hyperspectral Sensors for Proximal Sensing of Nitrogen Levels in Wheat.
Article in Sensors (Basel, Switzerland), 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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13 citing papers in PubMed, 43 citations in OpenAlex.
- Hyperspectral-based classification of individual wheat plants into fine-scale reproductive stages.Plant methods · 2025Article
- Salinity Tolerance in Wheat: Mechanisms and Breeding Approaches.Plants (Basel, Switzerland) · 2025Review
- Progress in the application of hyperspectral imaging technology in quality detection and in the modernization of Chinese herbal medicines.Frontiers in chemistry · 2025Review
- Deep learning-based text generation for plant phenotyping and precision agriculture.Frontiers in plant science · 2025Article
- Automatic optimization of regions of interest in hyperspectral images for detecting vegetative indices in soybeans.Frontiers in plant science · 2025Article
- Development of a vegetation canopy reflectance sensor and its diurnal applicability under clear sky conditions.Frontiers in plant science · 2024Article
- Estimation of Biochemical Pigment Content in Poplar Leaves Using Proximal Multispectral Imaging and Regression Modeling Combined with Feature Selection.Sensors (Basel, Switzerland) · 2023Article
- Hyperspectral images of grapevine leaves including healthy leaves and leaves with biotic and abiotic symptoms.Scientific data · 2023Article
- Editorial: Machine vision and machine learning for plant phenotyping and precision agriculture.Frontiers in plant science · 2023Article
- Pitfalls and potential of high-throughput plant phenotyping platforms.Frontiers in plant science · 2023Article
- Proximal and remote sensing in plant phenomics: 20 years of progress, challenges, and perspectives.Plant communications · 2022Review
- Epidemic of Wheat Stripe Rust Detected by Hyperspectral Remote Sensing and Its Potential Correlation with Soil Nitrogen during Latent Period.Life (Basel, Switzerland) · 2022Article
- Capturing crop adaptation to abiotic stress using image-based technologies.Open biology · 2022Review
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Authors and funding
4 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The accurate and high throughput quantification of nitrogen (N) content in wheat using non-destructive methods is an important step towards identifying wheat lines with high nitrogen use efficiency and informing agronomic management practices. Among various plant phenotyping methods, hyperspectral sensing has shown promise in providing accurate measurements in a fast and non-destructive manner. Past applications have utilised non-imaging instruments, such as spectrometers, while more recent approaches have expanded to hyperspectral cameras operating in different wavelength ranges and at various spectral resolutions. However, despite the success of previous hyperspectral applications, some important research questions regarding hyperspectral sensors with different wavelength centres and bandwidths remain unanswered, limiting wide application of this technology. This study evaluated the capability of hyperspectral imaging and non-imaging sensors to estimate N content in wheat leaves by comparing three hyperspectral cameras and a non-imaging spectrometer. This study answered the following questions: (1) How do hyperspectral sensors with different system setups perform when conducting proximal sensing of N in wheat leaves and what aspects have to be considered for optimal results? (2) What types of photonic detectors are most sensitive to N in wheat leaves? (3) How do the spectral resolutions of different instruments affect N measurement in wheat leaves? (4) What are the key-wavelengths with the highest correlation to N in wheat? Our study demonstrated that hyperspectral imaging systems with satisfactory system setups can be used to conduct proximal sensing of N content in wheat with sufficient accuracy. The proposed approach could reduce the need for chemical analysis of leaf tissue and lead to high-throughput estimation of N in wheat. The methodologies here could also be validated on other plants with different characteristics. The results can provide a reference for users wishing to measure N content at either plant- or leaf-scales using hyperspectral sensors.
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