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.

In-season nitrogen (N) status diagnosis is an effective way to guide split N applications for improved profitability and minimized negative environmental impacts. Petiole nitrate-N concentration (PNNC) has been an industry standard indicator for in-season potato (Solanum tuberosum L.) N status diagnosis but is limited because of destructive sampling and chemical processing needs. Leaf sensors can be used to predict PNNC and other N status indicators and overcome these challenges. The SPAD meter is a sensor commonly used to estimate leaf chlorophyll (Chl) based on transmittance, while Dualex is a newer leaf sensor that can also measure leaf flavanol (Flav) and anthocyanin (Anth) through Chl fluorescence. Limited research has been conducted to compare the two leaf sensors for potato N status assessment, despite their respective success in N status diagnosis for other crops. Therefore, the objectives of this study were to 1) compare the performance of the Dualex sensor relative to the SPAD meter for predicting potato N status indicators when only sensor data are used, 2) evaluate the potential of improving potato N status prediction using multi-source data fusion compared with only using leaf sensor data, and 3) develop practical strategies for leaf-sensor-based in-season potato N status diagnosis. The plot-scale experiments were conducted in Becker, Minnesota, USA in 2018, 2019, 2021, and 2023 involving different cultivars, N treatments, and irrigation treatments in a split plot design with three replications. Leaf sensor data and plant samples were simultaneously collected and processed multiple times at key growth stages each year. Daily weather data were also collected at the on-site weather station. Different in-season potato N status indicators including PNNC and N nutrition index (NNI) were derived from plant samples, while weather- and management-related parameters were calculated using the weather data and management records. 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 in-season potato N status diagnostic strategy was developed using linear support vector regression model with SPAD, cultivar information, accumulated growing degree days (GDDs), accumulated total moisture, and as-applied N rate to predict vine or whole plant NNI, achieving an R² of 0.80 - 0.82, accuracy of 0.75 - 0.77, and a Kappa statistic of 0.57 - 0.58 (near-substantial). Further research is also required to determine the critical N dilution curve and sufficiency ranges of NNI for potatoes based on different genetic, environmental, and management conditions to better support decision-making.

The nitrogen nutrition index (NNI) can comprehensively determine plant nitrogen (N) status and assist in determining the optimal N application. An innovative integrated active canopy sensor system, Crop Circle Phenom (CCP), capable of measuring canopy reflectance and meteorological parameters simultaneously, demonstrated the ability to predict NNI for corn and may also be useful for potatoes. This study aimed to evaluate the potential of the CCP system for potato N status diagnosis across different varieties. Four site-years of experiments were conducted involving different cultivars, N rates, and irrigation rates in Becker, MN, USA. Tree-based machine learning models using vegetation indices and meteorological parameters from CCP and ancillary information directly predicted NNI with an R2 of approximately 0.85. This scalable technology has the potential to diagnose in-season N status of potatoes and guide precision N management.