What can we learn from a multi-season-stage-variety potato (Solanum tuberosum L.) study using aerial hyperspectral imagery? - Crosby, Trevor; Townsend, Philip A.; Ravindran, Prabu; Heberlein, Brendan C.; Hills, W. Beckett; Wang, Yi (Frontiers in Remote Sensing, 2025)
Proper monitoring of plant nitrogen (N) status and yield forecasting is essential to achieving a healthy crop and to maximizing profitability, especially in N-demanding crops such as potato. The most common method of monitoring potato N status (nitrate-N analysis of petioles) by the potato farmers in Wisconsin is time-consuming, destructive, and is impractical to sufficiently characterize spatial-temporal variability. This study utilized narrow-band hyperspectral imagery (including the visible and near-infrared (VNIR) and shortwave infrared (SWIR) spectral regions) collected over two growing seasons from two potato varieties (Russet Burbank and Soraya) grown under varied N treatments to develop robust partial least squares regression (PLSR) models for predicting potato in-season and at-harvest traits related to N. The results indicate that some traits such as leaf total N content, within-season tuber yield, and the marketable yield and quality at harvest could be well predicted for both varieties (R2 up to 0.78). The best spectral regions for those predictions varied depending on the growth stages of the plants, with VNIR predominating during early and mid-tuber, and SWIR during late tuber bulking. Our research suggests that the narrow-band hyperspectral imaging technique could be utilized to develop robust models to assist and potentially improve crop N fertilization decision-making, which will eventually result in higher input use efficiency of the cropping systems and better environmental stewardship for the farmers.