Claim · model-written summary
DeepSpecN had the highest maize leaf nitrogen estimation accuracy among tested methods, with RMSE 0.247 g/m 2 and R 2 0.665.
- Outcome
- estimation accuracye0
- Direction
- higheste0
- Value
- 0.247 g/m 2e0
- Qualifier
- R 2 = 0.665e0
- Comparator
- all the methodse0
Zea maysNot reportedMaterialGeneCompoundOrgan / stageInteractorInteractor roleSecond interactorSecond interactor roleTreatmentSetting
Organisms pictured · Representative pictures of the matched species, not figures from the paper.
Verbatim
e0“when trained on representative samples, DeepSpecN achieved the highest estimation accuracy among all the methods (RMSE = 0.247 g/m 2 , R 2 = 0.665)”
t0“leaf nitrogen content”
e1“a novel hybrid method for maize LNC estimation”
Title
Shuai Yang; Anirudh Belwalkar; Dong Li; et al. Plant Phenomics 2025. doi:10.1016/j.plaphe.2025.100125
Other claims from this paper (3)
- Method / resource Spectral similarity sample selection mitigated domain shift by identifying representative simulated training samples.
- Method / resource Chlorophyll-based empirical formulas estimated maize leaf nitrogen content more accurately than leaf-protein formulas.
- Method / resource Validation on four crop species supported generalizability of DeepSpecN.