Yang Zongfeng, Gao Shang, Xiao Feng, Li Ganghua, Ding Yangfeng, Guo Qinghua, Paul Matthew J, Liu Zhenghui
College of Agriculture, Nanjing Agricultural University, Nanjing, 210095 China.
State Key Laboratory of Vegetation and Environmental Change, Institute of Botany, Chinese Academy of Sciences, Beijing, 100093 China.
Plant Methods. 2020 Aug 26;16:117. doi: 10.1186/s13007-020-00660-y. eCollection 2020.
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.
鉴定和表征具有良好生理基础的新性状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，旨在开发一种整合粳稻源库的冠层结构新性状。
我们应用深度学习方法准确分割叶片和稻穗，并随后开发了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田间试验中拍摄的，相机拍摄角度、太阳的仰角和方位角、水稻基因型以及植株物候期存在很大差异。通过手动标注稻穗和叶片区域进行准确标记，所得数据集用于训练FPN-Mask（特征金字塔网络掩码）模型，该模型由骨干网络和特定任务子网组成。然后选择准确率最高的模型来检查192份水稻种质以及农艺措施之间LPR的变化。尽管田间条件具有挑战性，但FPN-Mask模型仍实现了较高的检测准确率，稻穗的像素准确率为0.99，叶片的像素准确率为0.98。计算得出的LPR显示出较大的时空变化以及基因型差异。此外，它对氮肥施用和植物生长调节剂喷洒等农艺措施有响应。
深度学习技术能够在从复杂的稻田图像中同时检测稻穗和叶片数据方面实现高精度。所提出的FPN-Mask模型适用于在田间条件下检测和量化作物表现。新鉴定的LPR性状应为育种者选择优良水稻品种以及农艺学家精确管理具有良好源库平衡的田间作物提供一种高通量方案。
