Leaf to panicle ratio (LPR): a new physiological trait for rice plant architecture based on deep learning [post]

Zongfeng Yang, Shang Gao, Feng Xiao, Ganghua Li, Yangfeng Ding, Qinghua Guo, Matthew J. Paul, Zhenghui Liu
2020 unpublished
Background: Identification and characterization of new traits with a sound physiological foundation for crop breeding and management is one of the primary objectives for crop physiology. New technological advances in high throughput phenotyping have strengthened the power of physiological breeding. Methods for data mining of the big data acquired by various phenotyping platforms are developed, among which deep learning is used in image data analysis to explore spatial and temporal information
more » ... poral information concerning crop growth and development. However, method development is still necessary to enable simultaneous extraction of both leaf and panicle data from the complex field backgrounds, as required by the breeder for the adoption of physiological strategies to balance source and sink for yield improvement.Results: We applied a deep learning approach to accurately extract leaf and panicle data and subsequently developed the procedure of GvCrop to calculate the leaf to panicle ratio (LPR) of rice populations during grain filling. Images of the training data set were captured in the field experiments, with large variations in camera shooting angle, the elevation angle and the azimuth angle of the sun, rice genotype, and plant phenological stages. Accurately labeled by manually annotating all the panicle and leaf regions, the resulting dataset were used to train FPN-Mask models, consisting of a backbone network and a task-specific sub-network. The model with the highest accuracy is then selected to study the variations in LPR among 192 rice germplasms and among agronomical practices. Despite the challenging field conditions, FPN-Mask models achieved a high detection accuracy, with Pixel Accuracy being 0.99 for panicles and 0.98 for leaves. The calculated LPRs showed large spatial and temporal variations as well as genotypic differences. Conclusion: Deep learning techniques can achieve high accuracy in simultaneously detecting panicle and leaf data from complex rice field images. The proposed FPN-Mask model is applicable to detect and quantify crop performance under field conditions. The proposed trait of LPR should provide a high throughput protocol for breeders to select superior rice cultivars as well as for agronomists to precisely manage field crops that have a good balance of source and sink.
doi:10.21203/rs.3.rs-25185/v1 fatcat:7jmzhpt7pvfthhlch66ktzglfu