Evaluating the Potential of Improving In-Season Potato Nitrogen Status Diagnosis Using Leaf Fluorescence Sensor as Compared with SPAD Meter
The petiole nitrate–nitrogen concentration (PNNC) has been an industry standard indicator for in-season potato (Solanum tuberosum L.) nitrogen (N) status diagnosis. Leaf sensors can be used to predict the PNNC and other N status indicators non-destructively. The SPAD meter is a common leaf chlorophyll (Chl) meter, while the Dualex is a newer leaf fluorescence sensor. Limited research has been conducted to compare the two leaf sensors for potato N status assessment. Therefore, the objectives of this study were to (1) compare SPAD and Dualex for predicting potato N status indicators, and (2) evaluate the potential prediction improvement using multi-source data fusion. The plot-scale experiments were conducted in Becker, Minnesota, USA, in 2018, 2019, 2021, and 2023, involving different cultivars, N treatments, and irrigation rates. The results indicated that Dualex’s N balance index (NBI; Chl/Flav) always outperformed Dualex Chl but did not consistently perform better than the SPAD meter. All N status indicators were predicted with significantly higher accuracy with multi-source data fusion using machine learning models. A practical strategy was developed using a linear support vector regression model with SPAD, cultivar information, accumulated growing degree days, accumulated total moisture, and an as-applied N rate to predict the vine or whole-plant N nutrition index (NNI), achieving an R2 of 0.80–0.82, accuracy of 0.75–0.77, and Kappa statistic of 0.57–0.58 (near-substantial). Further research is needed to develop an easy-to-use application and corresponding in-season N recommendation strategy to facilitate practical on-farm applications.
Authors
- Yuxin Miao (ORCID: https://orcid.org/0000-0001-8419-6511)
- Sanjay Gupta (ORCID: https://orcid.org/0000-0003-1714-3895)
- Jizong Zhang
- Carl J. Rosen (ORCID: https://orcid.org/0000-0002-0098-2230)
- Dan Li (ORCID: https://orcid.org/0000-0002-0391-6490)
- Seiya Wakahara
Institutions
- Hebei Agricultural University (CN)
- University of Minnesota (US)
- Guangdong Academy of Sciences (CN)
- Guangzhou Institute of Geography (CN)
Publication Details
- Journal
- Remote Sensing
- Published
- 2025-07-05
- DOI
- https://doi.org/10.3390/rs17132311
- Citations
- 2
- Primary Topic
- Potato Plant Research
- Type
- article
- Field-Weighted Citation Impact
- 2.32
Funders
- Minnesota Department of Agriculture
- National Institute of Food and Agriculture