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Open AccessArticle

Crop Monitoring Using Satellite/UAV Data Fusion and Machine Learning

1
Geospatial Institute, Saint Louis University, St. Louis, MO 63108, USA
2
Department of Earth and Atmospheric Sciences, Saint Louis University, St. Louis, MO 63108, USA
3
Department of Electrical and Computer Engineering, Purdue University Northwest, Hammond, IN 46323, USA
4
Division of Plant Sciences, University of Missouri, Columbia, MO 65211, USA
*
Author to whom correspondence should be addressed.
Remote Sens. 2020, 12(9), 1357; https://doi.org/10.3390/rs12091357
Received: 8 March 2020 / Revised: 17 April 2020 / Accepted: 17 April 2020 / Published: 25 April 2020
Non-destructive crop monitoring over large areas with high efficiency is of great significance in precision agriculture and plant phenotyping, as well as decision making with regards to grain policy and food security. The goal of this research was to assess the potential of combining canopy spectral information with canopy structure features for crop monitoring using satellite/unmanned aerial vehicle (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) was extracted from Worldview-2/3 data, and canopy structure information (i.e., canopy height and canopy cover) was derived from UAV RGB imagery. Canopy spectral and structure information and their combination were used to predict soybean leaf area index (LAI), aboveground biomass (AGB), and leaf nitrogen concentration (N) using partial least squares regression (PLSR), random forest regression (RFR), support vector regression (SVR), 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, canopy height, and canopy coverage were significant indicators for crop growth 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 on using satellite or UAV data alone, (3) adding canopy structure information to spectral features reduced background soil effect and asymptotic saturation issue to some extent and led to better model performance, (4) the ELR model with the newly proposed activated function slightly outperformed PLSR, RFR, and SVR in the prediction of AGB and LAI, while RFR provided the best result for N estimation. This study introduced opportunities and limitations of satellite/UAV data fusion using machine learning in the context of crop monitoring. View Full-Text
Keywords: data fusion; machine learning; activation function; crop monitoring; extreme learning machine (ELM); unmanned aerial vehicle (UAV) data fusion; machine learning; activation function; crop monitoring; extreme learning machine (ELM); unmanned aerial vehicle (UAV)
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MDPI and ACS Style

Maimaitijiang, M.; Sagan, V.; Sidike, P.; Daloye, A.M.; Erkbol, H.; Fritschi, F.B. Crop Monitoring Using Satellite/UAV Data Fusion and Machine Learning. Remote Sens. 2020, 12, 1357. https://doi.org/10.3390/rs12091357

AMA Style

Maimaitijiang M, Sagan V, Sidike P, Daloye AM, Erkbol H, Fritschi FB. Crop Monitoring Using Satellite/UAV Data Fusion and Machine Learning. Remote Sensing. 2020; 12(9):1357. https://doi.org/10.3390/rs12091357

Chicago/Turabian Style

Maimaitijiang, Maitiniyazi; Sagan, Vasit; Sidike, Paheding; Daloye, Ahmad M.; Erkbol, Hasanjan; Fritschi, Felix B. 2020. "Crop Monitoring Using Satellite/UAV Data Fusion and Machine Learning" Remote Sens. 12, no. 9: 1357. https://doi.org/10.3390/rs12091357

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Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

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