Combining machine learning with a mechanistic model to estimate maize nitrogen content from UAV-acquired hyperspectral imagery

Jiating Li, Yufeng Ge, Laila Puntel, Derek Heeren, Guillermo Balboa, Yeyin Shi

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Crop nitrogen (N) content reflects crop nutrient status and is an important trait in crop management. Over the decades, non-destructive N estimation has greatly benefited from remote sensing and data-intensive computational approaches. However, previous studies mostly focused on the estimation accuracy under a specific environment; few of them considered estimation robustness across varying growth conditions. As climate change intensifies, crops are facing more unexpected stresses. It is critical to improve N estimation under changing environments with better model generalizability. Thus, we proposed a novel hybrid method with merits of both mechanistic and machine learning models and integrating in-situ data and simulated data for an improved model training. The in-situ data were the canopy reflectance extracted from hyperspectral images collected by an Unmanned Aerial Vehicle (UAV) and destructively sampled plant N content; the simulated data referred to the canopy reflectance simulated by a mechanistic model, the PROSAIL-PRO. The performance of the hybrid method was compared with one of the most popular machine learning models (i.e., Gaussian Process Regression, GPR) across three study sites. Results showed that the hybrid method outperformed the GPR by reducing RRMSE up to 6.84% on canopy nitrogen content (CNC) estimation. It also achieved more stable performances across varying soil water and N availabilities. Altogether, we demonstrated an approach to estimate CNC under diverse soil and environmental conditions from remotely sensed spectral data with better accuracy and generalizability. It leverages the robustness of mechanistic models and the computational efficiency of machine learning models and has great potential to be transferred to other crops and many common crop traits.

Original languageEnglish (US)
Title of host publicationAutonomous Air and Ground Sensing Systems for Agricultural Optimization and Phenotyping VIII
EditorsJ. Alex Thomasson, Christoph Bauer
PublisherSPIE
ISBN (Electronic)9781510661943
DOIs
StatePublished - 2023
EventAutonomous Air and Ground Sensing Systems for Agricultural Optimization and Phenotyping VIII 2023 - Orlando, United States
Duration: May 1 2023May 2 2023

Publication series

NameProceedings of SPIE - The International Society for Optical Engineering
Volume12539
ISSN (Print)0277-786X
ISSN (Electronic)1996-756X

Conference

ConferenceAutonomous Air and Ground Sensing Systems for Agricultural Optimization and Phenotyping VIII 2023
Country/TerritoryUnited States
CityOrlando
Period5/1/235/2/23

Keywords

  • Crop traits
  • Hyperspectral imager
  • Machine learning
  • Mechanistic model
  • UAS

ASJC Scopus subject areas

  • Electronic, Optical and Magnetic Materials
  • Condensed Matter Physics
  • Computer Science Applications
  • Applied Mathematics
  • Electrical and Electronic Engineering

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