Estimation of Maize LAI Using Ensemble Learning and UAV Multispectral Imagery under Different Water and Fertilizer Treatments

• Published in: Agriculture (Basel) Vol. 12; no. 8; p. 1267
• Main Authors: Cheng, Qian, Xu, Honggang, Fei, Shuaipeng, Li, Zongpeng, Chen, Zhen
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.08.2022
• Subjects: Accuracy; Algorithms; China; Corn; Correlation coefficient; Correlation coefficients; Crop growth; Crop management; Crops; Data mining; Decision trees; Droughts; Ensemble learning; Experiments; Fertilizers; Flowering; Gaussian process; Ground truth; Growth stage; Imagery; Irrigation; LAI; Leaf area; Leaf area index; Learning algorithms; Loam soils; Machine learning; maize; Potassium; Precision farming; Prediction models; Regression; Remote sensing; Satellites; Stacking; Support vector machines; unmanned aerial vehicle; Unmanned aerial vehicles; Vegetation index; water and fertilizer stress; Water management; Water treatment; China
• ISSN: 2077-0472; 2077-0472
• DOI: 10.3390/agriculture12081267

The leaf area index (LAI), commonly used as an indicator of crop growth and physiological development, is mainly influenced by the degree of water and fertilizer stress. Accurate assessment of the LAI can help to understand the state of crop water and fertilizer deficit, which is important for crop management and the precision agriculture. The objective of this study is to evaluate the unmanned aerial vehicle (UAV)-based multispectral imaging to estimate the LAI of maize under different water and fertilizer stress conditions. For this, multispectral imagery of the field was conducted at different growth stages (jointing, trumpet, silking and flowering) of maize under three water treatments and five fertilizer treatments. Subsequently, a stacking ensemble learning model was built with Gaussian process regression (GPR), support vector regression (SVR), random forest (RF), least absolute shrinkage and selection operator (Lasso) and cubist regression as primary learners to predict the LAI using UAV-based vegetation indices (VIs) and ground truth data. Results showed that the LAI was influenced significantly by water and fertilizer stress in both years’ experiments. Multispectral VIs were significantly correlated with maize LAI at multiple growth stages. The Pearson correlation coefficients between UAV-based VIs and ground truth LAI ranged from 0.64 to 0.89. Furthermore, the fusion of multiple stage data showed that the correlations were significantly higher between ground truth LAI and UAV-based VIs than that of single growth stage data. The ensemble learning algorithm with MLR as the secondary learner outperformed as a single machine learning algorithm with high prediction accuracy R2 = 0.967 and RMSE = 0.198 in 2020, and R2 = 0.897 and RMSE = 0.220 in 2021. We believe that the ensemble learning algorithm based on stacking is preferable to the single machine learning algorithm to build the LAI prediction model. This study can provide certain theoretical guidance for the rapid and precise management of water and fertilizer for large experimental fields.