Research Article | Open Access

Volume 2023 |Article ID 0019 | https://doi.org/10.34133/plantphenomics.0019

Dynamic UAV Phenotyping for Rice Disease Resistance Analysis Based on Multisource Data

Xiulin Bai,¹ Hui Fang,² Yong He,¹ Jinnuo Zhang,¹ Mingzhu Tao,¹ Qingguan Wu,¹ Guofeng Yang,¹ Yuzhen Wei,¹ Yu Tang,⁴ Lie Tang,⁵ Binggan Lou,⁶ Shuiguang Deng,⁷ Yong Yang ,⁸ and Xuping Feng ¹

¹College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 310058, China
²Huzhou Institute of Zhejiang University, Huzhou 313000, China
³School of Information Engineering, Huzhou University, Huzhou 313000, China
⁴Academy of Interdisciplinary Studies, Guangdong Polytechnic Normal University, Guangzhou 510665, China
⁵Department of Agricultural and Biosystems Engineering, Iowa State University, Ames, IA 50011-3270, USA
⁶College of Agriculture and Biotechnology, Zhejiang University, Hangzhou 310058, China
⁷College of Computer Science and Technology, Zhejiang University, Hangzhou 310058, China
⁸State Key Laboratory for Managing Biotic and Chemical Treats to the Quality and Safety of Agro-Products, Key Laboratory of Biotechnology for Plant Protection, Ministry of Agriculture, and Rural Affairs, Zhejiang Provincial Key Laboratory of Biotechnology for Plant Protection, Institute of Virology and Biotechnology, Zhejiang Academy of Agricultural Science, Hangzhou 31002, China

Abstract

Bacterial blight poses a threat to rice production and food security, which can be controlled through large-scale breeding efforts toward resistant cultivars. Unmanned aerial vehicle (UAV) remote sensing provides an alternative means for the infield phenotype evaluation of crop disease resistance to relatively time-consuming and laborious traditional methods. However, the quality of data acquired by UAV can be affected by several factors such as weather, crop growth period, and geographical location, which can limit their utility for the detection of crop disease and resistant phenotypes. Therefore, a more effective use of UAV data for crop disease phenotype analysis is required. In this paper, we used time series UAV remote sensing data together with accumulated temperature data to train the rice bacterial blight severity evaluation model. The best results obtained with the predictive model showed an Rp2 of 0.86 with an RMSEp of 0.65. Moreover, model updating strategy was used to explore the scalability of the established model in different geographical locations. Twenty percent of transferred data for model training was useful for the evaluation of disease severity over different sites. In addition, the method for phenotypic analysis of rice disease we built here was combined with quantitative trait loci (QTL) analysis to identify resistance QTL in genetic populations at different growth stages. Three new QTLs were identified, and QTLs identified at different growth stages were inconsistent. QTL analysis combined with UAV high-throughput phenotyping provides new ideas for accelerating disease resistance breeding.