Abstract
Accurate and timely detection of phenology at plot scale in rice breeding trails is crucial for understanding the heterogeneity of varieties and guiding field management. Traditionally, remote sensing studies of phenology detection have heavily relied on the time-series vegetation index (VI) data. However, the methodology based on time-series VI data was often limited by the temporal resolution. In this study, three types of ensemble models including hard voting (majority voting), soft voting (weighted majority voting) and model stacking, were proposed to identify the principal phenological stages of rice based on unmanned aerial vehicle (UAV) RGB imagery. These ensemble models combined RGB-VIs, color space (e.g., RGB and HSV) and textures derived from UAV-RGB imagery, and five machine learning algorithms (random forest; k-nearest neighbors; Gaussian naïve Bayes; support vector machine and logistic regression) as base models to estimate phenological stages in rice breeding. The phenological estimation models were trained on the dataset of late-maturity cultivars and tested independently on the dataset of early-medium-maturity cultivars. The results indicated that all ensemble models outperform individual machine learning models in all datasets. The soft voting strategy provided the best performance for identifying phenology with the overall accuracy of 90% and 93%, and the mean F1-scores of 0.79 and 0.81, respectively, in calibration and validation datasets, which meant that the overall accuracy and mean F1-scores improved by 5% and 7%, respectively, in comparison with those of the best individual model (GNB), tested in this study. Therefore, the ensemble models demonstrated great potential in improving the accuracy of phenology detection in rice breeding.
Highlights
In rice breeding, the accurate phenological information of rice is essential for breeders to make decisions in fertigation and breeding operations [1]
The results indicated that all ensemble models outperform individual machine learning models in all datasets
The soft voting strategy provided the best performance for identifying phenology with the overall accuracy of 90% and 93%, and the mean F1-scores of 0.79 and 0.81, respectively, in calibration and validation datasets, which meant that the overall accuracy and mean F1-scores improved by 5%
Summary
The accurate phenological information of rice is essential for breeders to make decisions in fertigation and breeding operations [1]. Crop phenological stages have a close relationship with climate conditions [2], but are affected by genetic variability [3]. The approach of detecting crop phenology uses visual observations to identify the timing of periodic events such as germination, flowering, and physiological maturity [1,4]. Remote sensing data based on satellites has been successfully used to detect landscape-scale phenology [2,6,7,8]. Landsat was firstly used to characterize the seasonal changes of vegetation at regional scales [9]. Some plant phenological stages changed rapidly, which limited the application of Landsat due to its long revisit time (16 days).
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