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ORIGINAL RESEARCH article

Front. Remote Sens.
Sec. Multi- and Hyper-Spectral Imaging
Volume 6 - 2025 | doi: 10.3389/frsen.2025.1484582
This article is part of the Research Topic Advancements in Agricultural Monitoring with AI Enhanced Remote Sensing Techniques View all 4 articles

What can we learn from a multi-season-stage-variety potato (Solanum tuberosum L.) study using aerial hyperspectral imagery?

Provisionally accepted
Trevor W Crosby Trevor W Crosby Philip A Townsend Philip A Townsend Prabu Ravindran Prabu Ravindran Brendan C Heberlein Brendan C Heberlein W Beckett Hills W Beckett Hills Yi Wang Yi Wang *
  • University of Wisconsin-Madison, Madison, United States

The final, formatted version of the article will be published soon.

    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 short-wave 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 (R 2 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 decisionmaking, which will eventually result in higher input use efficiency of the cropping systems and better environmental stewardship for the farmers.

    Keywords: Hyperspectral image, modeling, Nitrogen, partial least squares regression, Potato, quality, yield Deleted: potato, °12.1413 89° 53.6840 Moved (insertion) [5] Formatted Table

    Received: 22 Aug 2024; Accepted: 28 Jan 2025.

    Copyright: © 2025 Crosby, Townsend, Ravindran, Heberlein, Hills and Wang. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

    * Correspondence: Yi Wang, University of Wisconsin-Madison, Madison, United States

    Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.