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Ensemble of Machine Learning Algorithms for Rice Grain Yield Prediction Using UAV-Based Remote Sensing

Authors
Sarkar, Tapash KumarRoy, Dilip KumarKang, Ye SeongJun, Sae RomPark, Jun WooRyu, Chan Seok
Issue Date
Nov-2023
Publisher
Springer Nature
Keywords
BMA; Image processing; Precision agriculture; Sensor; Vegetation indices
Citation
Journal of Biosystems Engineering
Indexed
SCOPUS
KCI
Journal Title
Journal of Biosystems Engineering
URI
https://scholarworks.gnu.ac.kr/handle/sw.gnu/69066
DOI
10.1007/s42853-023-00209-6
ISSN
1738-1266
2234-1862
Abstract
Purpose: Accurately estimating rice yield before harvesting is crucial for effective crop management, food trade assessment, and national food policy planning to ensure food security. Remotely sensed spectral information such as vegetation index (VI)-based approaches for yield prediction are adequate during mid-stage growth but not during ripening due to leaf senescence, canopy coverage, panicle abundance, and other factors. To fill this research gap, this study aims to predict rice yield during ripening stage using an ensemble of machine learning (ML) algorithms. Methods: A fixed-wing unmanned aerial vehicle (UAV) was employed to acquire spectral features from red-green-blue, near-infrared, and red-edge images. In this study, we utilized state-of-the-art ML-based algorithms, such long short term memory (LSTM), bi-directional LSTM (Bi-LSTM), Gaussian process regression (GPR), fuzzy inference system (FIS), adaptive neuro FIS (ANFIS), M5 model tree (M5 Tree), support vector regression (SVR), random forest (RF), and the powerful ensemble techniques based on Bayesian model averaging (BMA), and simple averaging (SA) to aid in improving rice yield prediction more precisely at the ripening stage. Results: The findings demonstrate that the ensemble model based on BMA excelled all other models on all evaluation criteria. BMA accomplished the most accurate yield prediction with correlation coefficient, root mean squared error (RMSE), normalized RMSE, mean absolute error, median absolute deviation, index of agreement, and a-10 values of 0.958, 0.187 t ha−1, 0.031, 0.158 t ha−1, 0.088 t ha−1, 0.957, and 1.00, respectively. Conclusion: Employing a combination of ML algorithms for predicting rice grain yield using UAV-based remote sensing proves to be a powerful and effective approach. The ensemble method improves forecast accuracy, mitigates individual algorithm limitations, and produces trustworthy outcomes for smart agricultural decisions by integrating the strengths of multiple algorithms. This comprehensive technique has the potential to adapt rice yield estimation and contribute to sustainable food production systems. © 2023, The Author(s), under exclusive licence to The Korean Society for Agricultural Machinery.
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농업생명과학대학 (생물산업기계공학과)
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