%A WANG Ze, ZHOU Qin-Yang, LIU Cong, MU Yue, GUO Wei, DING Yan-Feng, NINOMIYA Seishi %T Estimation and evaluation of paddy rice canopy characteristics based on images from UAV and ground camera %0 Journal Article %D 2022 %J Acta Agronomica Sinica %R 10.3724/SP.J.1006.2022.02065 %P 1248-1261 %V 48 %N 5 %U {https://zwxb.chinacrops.org/CN/abstract/article_7350.shtml} %8 2022-05-12 %X

Phenotypic monitoring of rice in the field can be used to analyze traits related to rice yield, which is of great significance to guide rice cultivation management and yield prediction. In this study, to explore the applicability of image analysis methods to evaluate rice growth in different fields under multiple cultivars and cultivation environments, we estimated and evaluated the main phenotypic parameters of rice canopy in paddy fields with six different cultivars under three nitrogen treatments. Based on UAV and field fixed camera images, this study used image processing, three-dimensional modeling and machine learning to automatically estimate rice canopy coverage, plant height, and panicle number in the field, and evaluated the accuracy combining with the actual measurement results. The results showed that the rice canopy segmentation based on the decision tree classification model using UAV images were consistent with the manually segmented results (mean value and variance of Qseg was 0.75 and 0.08), and the rice canopy coverage calculated by this method had a relatively high correlation with that calculated by manually segmentation (R2= 0.83, RMSE = 5.36%). The average rice plant height estimated by the canopy height model in each plot had a high correlation with the mean plant height measured in the field (R2= 0.81, RMSE = 9.81 cm), but it was underestimated as a whole. Based on the ground image, the panicle count results obtained by decision tree classification and morphological parameter filter had a relatively high correlation with the measured panicle number (R2= 0.83, RMSE = 10.99). Overall, combined with image analysis algorithm, using low-altitude UAV remote sensing technology to high-throughput and automatically estimate rice canopy coverage and plant height can achieve relatively high accuracy; using image from ground camera to accurately count the rice panicle number is of significant potentiality. The proposed pipeline in this research could be used to analyze nitrogen effect on rice growth status and evaluate nitrogen response of different rice cultivars, and it is of great significance for mining rice field phenotypic information and yield prediction.