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.

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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

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article

Evaluating the Potential of Improving In-Season Potato Nitrogen Status Diagnosis Using Leaf Fluorescence Sensor as Compared with SPAD Meter

Yuxin Miao, Sanjay Gupta, Jizong Zhang, Carl J. Rosen et al.
2 citations
Remote Sensing
Potato Plant Research
2.32
article

Evaluating the Potential of Improving In-Season Potato Nitrogen Status Diagnosis Using Leaf Fluorescence Sensor as Compared with SPAD Meter

Yuxin Miao, Sanjay Gupta, Jizong Zhang, Carl J. Rosen, Dan Li, Seiya Wakahara
article en
2 citations

Abstract

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.

Remote SensingVol. 17(13)
Hebei Agricultural University (CN), University of Minnesota (US), Guangdong Academy of Sciences (CN), Guangzhou Institute of Geography (CN)
Minnesota Department of Agriculture, National Institute of Food and Agriculture
Openalex Percentile: Top 12%
Potato Plant Research
2.32
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