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

• Published in: Precision agriculture Vol. 24; no. 1; pp. 187 - 212
• Main Authors: Fei, Shuaipeng, Hassan, Muhammad Adeel, Xiao, Yonggui, Su, Xin, Chen, Zhen, Cheng, Qian, Duan, Fuyi, Chen, Riqiang, Ma, Yuntao
• Format: Journal Article
• Language: English
• Published: New York Springer US 01.02.2023; Springer Nature B.V
• Subjects: Accuracy; Agriculture; Algorithms; altitude; Artificial neural networks; Atmospheric Sciences; Biomedical and Life Sciences; Chemistry and Earth Sciences; Computer Science; Crop yield; Cultivars; Data integration; Decision trees; Grain; grain yield; Irrigation; Learning algorithms; Life Sciences; Low altitude; Machine learning; Multisensor fusion; Neural networks; phenotype; Phenotyping; Physics; Plant breeding; precision; prediction; Predictions; Remote Sensing/Photogrammetry; Root-mean-square errors; Sensors; Soil Science & Conservation; Statistics for Engineering; Support vector machines; Unmanned aerial vehicles; Wheat; yield forecasting; Data fusion; Unmanned aerial vehicle; Wheat; Machine learning; Phenotyping
• ISSN: 1385-2256; 1573-1618; 1573-1618
• DOI: 10.1007/s11119-022-09938-8

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.