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ORIGINAL RESEARCH article

Front. Plant Sci.
Sec. Technical Advances in Plant Science
Volume 15 - 2024 | doi: 10.3389/fpls.2024.1470719

A Combined Model of Shoot Phosphorus Uptake based on Sparse Data and Active Learning Algorithm

Provisionally accepted
Tianli Wang Tianli Wang 1*Yi Zhang Yi Zhang 1Haiyan Liu Haiyan Liu 2*Fei Li Fei Li 3Dayong Guo Dayong Guo 2*Ning Cao Ning Cao 1*Yubin Zhang Yubin Zhang 1*
  • 1 Jilin University, Changchun, China
  • 2 Henan University of Science and Technology, Luoyang, Henan Province, China
  • 3 Inner Mongolia Agricultural University, Hohhot, Inner Mongolia Autonomous Region, China

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

    The soil ecosystem has been severely damaged because of the increasingly severe environmental problems caused by excessive application of phosphorus (P) fertilizer, which seriously hinders soil fertility restoration and sustainable farmland development. Shoot P uptake (SPU) is an important parameter for monitoring crop growth and health and for improving field nutrition management and fertilization strategies. Achieving on-site measurement of large-scale data is difficult, and effective nondestructive prediction methods are lacking. Improving spatiotemporal SPU estimation at the regional scale still poses challenges. In this study, we proposed a combination prediction model based on some representative samples. Furthermore, using the experimental area of Henan Province, as an example, we explored the potential of the hyperspectral prediction of maize SPU at the canopy scale. The combination model comprises predicted P uptake by maize leaves, stems, and grains. Results show that (1) the prediction accuracy of the combined prediction model has been greatly improved compared with simple empirical prediction models, with accuracy test results of R2 = 0.87, root mean square error = 2.39 kg/ha, and relative percentage difference = 2.71. (2) In performance tests with different sample sizes, two-dimensional correlation spectroscopy i.e., first-order differentially enhanced two-dimensional correlation spectroscopy (1Der-2DCOS) and two-trace 2DCOS of enhanced filling and milk stages (filling-milk-2T2DCOS)) can effectively and robustly extract spectral trait relationships, with good robustness, and can achieve efficient prediction based on small samples. (3) The hybrid model constrained by the Newton-Raphson-based optimizer's active learning method can effectively filter localized simulation data and achieve localization of simulation data in different regions when solving practical problems, improving the hybrid model’s prediction accuracy. The practice has shown that with a small number of representative samples, this method can fully utilize remote sensing technology to predict SPU, providing an evaluation tool for the sustainable use of agricultural P. Therefore, this method has good application prospects and is expected to become an important means of monitoring global soil P surplus, promoting sustainable agricultural development.

    Keywords: 2DCOS, 2T2DCOS, Active Learning, phosphorus uptake, PROSAIL-5B

    Received: 26 Jul 2024; Accepted: 23 Dec 2024.

    Copyright: © 2024 Wang, Zhang, Liu, Li, Guo, Cao and Zhang. 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:
    Tianli Wang, Jilin University, Changchun, China
    Haiyan Liu, Henan University of Science and Technology, Luoyang, 471003, Henan Province, China
    Dayong Guo, Henan University of Science and Technology, Luoyang, 471003, Henan Province, China
    Ning Cao, Jilin University, Changchun, China
    Yubin Zhang, Jilin University, Changchun, China

    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.