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Leaf to panicle ratio (LPR): a new physiological trait indicative of source and sink relation in japonica rice based on deep learning.
Plant Methods ( IF 5.1 ) Pub Date : 2020-08-26 , DOI: 10.1186/s13007-020-00660-y
Zongfeng Yang 1 , Shang Gao 2 , Feng Xiao 1 , Ganghua Li 1 , Yangfeng Ding 1 , Qinghua Guo 2 , Matthew J Paul 3 , Zhenghui Liu 1, 4
Affiliation  

Identification and characterization of new traits with sound physiological foundation is essential for crop breeding and production management. Deep learning has been widely used in image data analysis to explore spatial and temporal information on crop growth and development, thus strengthening the power of identification of physiological traits. Taking the advantage of deep learning, this study aims to develop a novel trait of canopy structure that integrate source and sink in japonica rice. We applied a deep learning approach to accurately segment leaf and panicle, and subsequently developed the procedure of GvCrop to calculate the leaf to panicle ratio (LPR) of rice canopy during grain filling stage. Images of training dataset were captured in the field experiments, with large variations in camera shooting angle, the elevation and the azimuth angles of the sun, rice genotype, and plant phenological stages. Accurately labeled by manually annotating the panicle and leaf regions, the resulting dataset were used to train FPN-Mask (Feature Pyramid Network Mask) models, consisting of a backbone network and a task-specific sub-network. The model with the highest accuracy was then selected to check 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 LPR displayed large spatial and temporal variations as well as genotypic differences. In addition, it was responsive to agronomical practices such as nitrogen fertilization and spraying of plant growth regulators. Deep learning technique can achieve high accuracy in simultaneous detection of 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 newly identified 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.

中文翻译:

叶穗比(LPR):基于深度学习的粳稻源库关系新生理特征

具有良好生理基础的新性状的鉴定和表征对于作物育种和生产管理至关重要。深度学习已广泛应用于图像数据分析,以探索作物生长发育的时空信息,从而增强生理性状的识别能力。本研究利用深度学习的优势,旨在开发一种新的粳稻冠层结构性状,整合源和汇。我们应用深度学习方法准确分割叶片和穗,随后开发了 GvCrop 程序来计算灌浆期水稻冠层的叶穗比 (LPR)。训练数据集的图像是在现场实验中捕获的,相机拍摄角度变化很大,太阳的仰角和方位角、水稻基因型和植物物候阶段。通过手动注释穗和叶区域进行准确标记,生成的数据集用于训练 FPN-Mask(特征金字塔网络掩码)模型,该模型由主干网络和特定任务子网络组成。然后选择具有最高准确度的模型来检查 192 种水稻种质和农艺实践中 LPR 的变化。尽管田间条件具有挑战性,但 FPN-Mask 模型实现了很高的检测精度,穗的像素精度为 0.99,叶子的像素精度为 0.98。计算的 LPR 显示出较大的空间和时间变化以及基因型差异。此外,它对农艺实践(例如施氮肥和喷洒植物生长调节剂)也有反应。深度学习技术可以实现从复杂稻田图像中同时检测穗和叶数据的高精度。所提出的 FPN-Mask 模型适用于检测和量化田间条件下的作物性能。新发现的 LPR 性状应该为育种者选择优良水稻品种以及农艺师精确管理具有良好源和库平衡的大田作物提供高通量协议。
更新日期:2020-08-26
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