Ensemble Learning for Pea Yield Estimation Using Unmanned Aerial Vehicles, Red Green Blue, and Multispectral Imagery
文献类型： 外文期刊
作者： Liu, Zehao 1 ; Ji, Yishan 1 ; Ya, Xiuxiu 2 ; Liu, Rong 1 ; Liu, Zhenxing 2 ; Zong, Xuxiao 1 ; Yang, Tao 1 ;
作者机构： 1.Chinese Acad Agr Sci, Inst Crop Sci, State Key Lab Crop Gene Resources & Breeding, Beijing 100081, Peoples R China
2.Tangshan Acad Agr Sci TAAS, Tangshan 036001, Peoples R China
关键词： machine learning; fusion data; unmanned aerial vehicles; cold-tolerant peas; common peas
期刊名称：DRONES （ 影响因子：4.4； 五年影响因子：4.8 ）
ISSN：
年卷期： 2024 年 8 卷 6 期
页码：
收录情况： SCI
摘要： Peas are one of the most important cultivated legumes worldwide, for which early yield estimations are helpful for agricultural planning. The unmanned aerial vehicles (UAVs) have become widely used for crop yield estimations, owing to their operational convenience. In this study, three types of sensor data (red green blue [RGB], multispectral [MS], and a fusion of RGB and MS) across five growth stages were applied to estimate pea yield using ensemble learning (EL) and four base learners (Cubist, elastic net [EN], K nearest neighbor [KNN], and random forest [RF]). The results showed the following: (1) the use of fusion data effectively improved the estimation accuracy in all five growth stages compared to the estimations obtained using a single sensor; (2) the mid filling growth stage provided the highest estimation accuracy, with coefficients of determination (R2) reaching up to 0.81, 0.8, 0.58, and 0.77 for the Cubist, EN, KNN, and RF algorithms, respectively; (3) the EL algorithm achieved the best performance in estimating pea yield than base learners; and (4) the different models were satisfactory and applicable for both investigated pea types. These results indicated that the combination of dual-sensor data (RGB + MS) from UAVs and appropriate algorithms can be used to obtain sufficiently accurate pea yield estimations, which could provide valuable insights for agricultural remote sensing research.
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