数字农科院2.0

UAV-based multi-sensor data fusion and machine learning algorithm for yield prediction in wheat

文献类型： 外文期刊

作者： Fei, Shuaipeng;Hassan, Muhammad Adeel;Xiao, Yonggui;Su, Xin;Chen, Zhen;Cheng, Qian;Duan, Fuyi;Chen, Riqiang;Ma, Yuntao

作者机构：

关键词： Data fusion;Machine learning;Phenotyping;Wheat;Unmanned aerial vehicle

期刊名称： PRECISION AGRICULTURE

ISSN： 1385-2256

年卷期： 2022 年

页码：

收录情况： SCIE（2022版）

摘要： Early prediction of grain yield helps scientists to make better breeding decisions for wheat. Use of machine learning (ML) methods for fusion of unmanned aerial vehicle (UAV)-based multi-sensor data can improve the prediction accuracy of crop yield. For this, five ML algorithms including Cubist, support vector machine (SVM), deep neural network (DNN), ridge regression (RR) and random forest (RF) were used for multi-sensor data fusion and ensemble learning for grain yield prediction in wheat. A set of thirty wheat cultivars and breeding lines were grown under three irrigation treatments i.e., light, moderate and high irrigation treatments to evaluate the yield prediction capabilities of a low-cost multi-sensor (RGB, multi-spectral and thermal infrared) UAV platform. Multi-sensor data fusion-based yield prediction showed higher accuracy compared to individual-sensor data in each ML model. The coefficient of determination (R-2) values for Cubist, SVM, DNN and RR models regarding grain yield prediction were observed from 0.527 to 0.670. Moreover, the results of ensemble learning through integrating the above models illustrated further increase in accuracy. The predictions of ensemble learning showed high R-2 values up to 0.692, which was higher as compared to individual ML models across the multi-sensor data. Root mean square error (RMSE), residual prediction deviation (RPD) and ratio of prediction performance to inter-quartile range (RPIQ) were calculated to be 0.916 t ha(-1), 1.771 and 2.602, respectively. The results proved that low altitude UAV-based multi-sensor data can be used for early grain yield prediction using data fusion and an ensemble learning framework with high accuracy. This high-throughput phenotyping approach is valuable for improving the efficiency of selection in large breeding activities.

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