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Crop monitoring using Satellite/UAV data fusion and machine learning

Authors: Maitiniyazi Maimaitijiang*, Saint Louis University, Vasit Sagan, Saint Louis University, Paheding Sidike, Purdue University Northwest, Ahmad Majeed Daloye, Saint Louis University, Hasanjan Erkbol, Saint Louis University, Felix B. Fritschi, University of Missouri, Columbia
Topics: Remote Sensing
Keywords: Satellite/UAV, data fusion, machine learning, crop monitoring
Session Type: Poster
Presentation File: No File Uploaded


Non-destructive crop monitoring over large areas with high efficiency is of great significance in precision agriculture and plant phenotyping. The goal of this research is to assess the potential of combining canopy spectral information with canopy structure features for crop monitoring within the framework of satellite/UAV data fusion and machine learning. Worldview-2/3 satellite data were tasked synchronized with high-resolution RGB image collection using an inexpensive Unmanned Aerial Vehicle (UAV) at a heterogeneous soybean [Glycine max (L.) Merr.] field. Canopy spectral information (i.e., vegetation indices) extracted from Worldview-2/3 data, and canopy structure information (i.e., canopy height and canopy cover) derived from UAV RGB imagery, as well as their combination were used to predict soybean aboveground biomass (AGB), leaf area index (LAI) and leaf nitrogen concentration (N) using Partial Least Squares Regression (PLSR), Support Vector Regression (SVR), Random Forest Regression (RFR), and Extreme Learning Regression (ELR) with a newly proposed activation function. The results revealed that: (1) UAV imagery-derived high-resolution and detailed canopy structure features are significant indicators for crop monitoring (2) integration of satellite imagery-based rich canopy spectral information with UAV-derived canopy structural features using machine learning improved soybean AGB, LAI and leaf N estimation; (3) adding canopy structure information to spectral features reduced background soil effect and asymptotic saturation issue to some extent, and led to a better model performance. This study introduces the opportunities and limitations of satellite/UAV data fusion using machine learning in the context of crop monitoring.

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