Estimation of Fv/Fm in Spring Wheat Using UAV-Based Multispectral and RGB Imagery with Multiple Machine Learning Methods

Abstract

The maximum quantum efficiency of photosystem II (Fv/Fm) is a widely used indicator of photosynthetic health in plants. Remote sensing of Fv/Fm using MS (multispectral) and RGB imagery has the potential to enable high-throughput screening of plant health in agricultural and ecological applications. This study aimed to estimate Fv/Fm in spring wheat at an experimental base in Hanghou County, Inner Mongolia, from 2020 to 2021. RGB and MS images were obtained at the wheat flowering stage using a Da-Jiang Phantom 4 multispectral drone. A total of 51 vegetation indices were constructed, and the measured Fv/Fm of wheat on the ground was obtained simultaneously using a Handy PEA plant efficiency analyzer. The performance of 26 machine learning algorithms for estimating Fv/Fm using RGB and multispectral imagery was compared. The findings revealed that a majority of the multispectral vegetation indices and approximately half of the RGB vegetation indices demonstrated a strong correlation with Fv/Fm, as evidenced by an absolute correlation coefficient greater than 0.75. The Gradient Boosting Regressor (GBR) was the optimal estimation model for RGB, with the important features being RGBVI and ExR. The Huber model was the optimal estimation model for MS, with the important feature being MSAVI2. The Automatic Relevance Determination (ARD) was the optimal estimation model for the combination (RGB + MS), with the important features being SIPI, ExR, and VEG. The highest accuracy was achieved using the ARD model for estimating Fv/Fm with RGB + MS vegetation indices on the test sets (Test set MAE = 0.019, MSE = 0.001, RMSE = 0.024, R2 = 0.925, RMSLE = 0.014, MAPE = 0.026). The combined analysis suggests that extracting vegetation indices (SIPI, ExR, and VEG) from RGB and MS remote images by UAV as input variables of the model and using the ARD model can significantly improve the accuracy of Fv/Fm estimation at flowering stage. This approach provides new technical support for rapid and accurate monitoring of Fv/Fm in spring wheat in the Hetao Irrigation District.

Design and Experiment of a Portable Near-Infrared Spectroscopy Device for Convenient Prediction of Leaf Chlorophyll Content

“…The entire dataset was randomly divided into a 70:30 ratio for model training and prediction, respectively. Twelve regression algorithms were used to analyze the spectral data and SPAD values, including linear regression (LR) [25], K-nearest neighbor regression (KNN) [26], support vector regression (SVR) [27], ridge regression (RR) [28], Lasso regression (Lasso) [29], decision tree regression (DTR) [30], extremely randomized tree regression (ETR) [31], random forest regression (RFR) [32], AdaBoost regression (ABR) [33], gradient boosting regression (GBR) [34], bagging regression (BAR) [35], and partial least squares regression (PLSR) [36] (see Appendix C for details). For each collection band and all band combinations between 415 nm and 940 nm, the twelve regression algorithms were used for analysis and prediction.…”

New Trends and Challenges in Precision and Digital Agriculture

“…[4,37,[54][55][56][57][58]. The above solutions are practiced extensively in cereal and rapeseed crops, cotton, and soybeans [15,59,60]. The most popular implementations involve the use of detailed images to assess the state of vegetation by analyzing vegetation indices; yield predictions in qualitative and quantitative terms; and combining the work of various systems that collect important environmental data-meteorological, soil, and yields-quantitatively and qualitatively.…”

“…More and more producers are successfully applying information technology to their farms, so the concept of digital agriculture is gaining importance. This solution focuses the automation of machinery and processes, involving the latest developments in artificial intelligence: classical and convolutional neural networks [1,[6][7][8][9][10]; analysis of diverse images [8,[11][12][13][14]; cloud computing and unmanned aerial vehicles [15][16][17][18][19], etc. Digital technologies in agriculture enable a better understanding of the interdependence of factors that determine various aspects of the business.…”