Combining rEW-2DCOS and mechanism-guided adaptive ensemble learning to improve the retrieval of leaf nitrogen, phosphorus, and potassium contents [0.03%]
结合rEW-2DCOS和机制引导的自适应集成学习改进叶氮磷钾含量检测
Bolin Fu,Yawei Zhu,Yeqiao Wang et al.
Bolin Fu et al.
Leaf nitrogen, phosphorus, and potassium content (LNC, LPC, LKC) are core nutrient elements and measurable trait parameters essential for assessing vegetation growth status and understanding hydrology-vegetation interactions. However, the s...
Multi-trait spectral modeling for estimating grapevine leaf traits and nutrients [0.03%]
用于估计葡萄叶性状和营养的多特征光谱模型
Parastoo Farajpoor,Alireza Pourreza,Mohammadreza Narimani et al.
Parastoo Farajpoor et al.
Analysis of leaf hemispherical radiative properties for retrieval of its biochemical and mineral nutrients could lead to a powerful monitoring approach for precise farm management. This study explores the potential of leaf spectral modeling...
panomiX: Investigating mechanisms of trait emergence through multi-omics data integration [0.03%]
panomiX：通过组学数据集成研究性状出现机制
Ankur Sahu,Dennis Psaroudakis,Hardy Rolletschek et al.
Ankur Sahu et al.
Complex omics approaches and high-throughput phenotyping generate large, heterogeneous datasets that make linking molecular signatures to plant traits challenging. To address this challenge, here we introduce panomiX, a user-friendly toolbo...
Structural parameter determination and pruning pattern analysis of pear tree shoots for dormant pruning [0.03%]
基于休眠期修剪的梨树发育枝结构参数确定及截形分析
Jiaqi Li,Hao Sun,Gengchen Wu et al.
Jiaqi Li et al.
The comprehensive understanding of the dormant pruning patterns in pear trees, along with the accurate identification of shoots suitable for pruning, is essential for implementing automated pruning and fruit production. Due to the complexit...
Grading evaluation of haploid fertility restoration traits based on inception-ResNet in maize [0.03%]
基于Inception-ResNet的玉米恢复系单倍体 fertile性状分级评价
Yizheng Wang,Zhou Yao,Wenhao Song et al.
Yizheng Wang et al.
Double haploid (DH) technology can significantly shorten the breeding cycle and improve the breeding efficiency, and it is favored by breeders. The metrics for evaluating the effect of haploid genome doubling mainly include anther emergence...
Jordan Ubbens,Ian Stavness,Michael P Pound et al.
Jordan Ubbens et al.
As with many fields of science, plant science and agriculture have seen a rapid adoption of deep learning in recent years. The present moment is significant as it marks one decade since the first applications of deep learning began to appea...
A survey on 3D reconstruction techniques in plant phenotyping: From classical methods to Neural Radiance Fields (NeRF), 3D Gaussian Splatting (3DGS), and beyond [0.03%]
植物表型分析中的三维重建技术综述：从经典方法到神经辐射场（NeRF）、3DGaussian Splatting（3DGS）及未来趋势
Jiajia Li,Xinda Qi,Seyed Hamidreza Nabaei et al.
Jiajia Li et al.
Plant phenotyping plays a pivotal role in understanding plant traits and their interactions with the environment, making it crucial for advancing precision agriculture and crop improvement. 3D reconstruction technologies have emerged as pow...
Leaf bidirectional reflectance distribution function (BRDF) prediction with phenotypic traits in four species: Development of a novel measuring and analyzing framework [0.03%]
结合表型性状的四种植物叶片双向反射分布函数(BRDF)预测：一种新的测量与分析框架的发展
Liangchao Deng,Leo Xinqi Yu,Linxiong Mao et al.
Liangchao Deng et al.
Light intensity and spectral distribution within plant canopies provides insights into the effects of optimizing canopy architecture on light use efficiency. Breeding crop varieties with a "smart" canopy, characterized by erect upper-layer ...
WPDSI: A deep learning method for wheat phenology detection from single-temporal images [0.03%]
基于单时相遥感影像的小麦生育期识别深度学习方法研究
Yan Li,Yucheng Cai,Xuerui Qi et al.
Yan Li et al.
Accurate monitoring of wheat phenology is critical for ensuring wheat production. Recent advances in deep learning have enabled the automated detection of wheat phenology in the field. In particular, deep learning models using multi-tempora...
Establishment of a high-throughput field defoliation data survey strategy combined with genome-wide association studies to reveal the genetic basis of defoliation in cotton [0.03%]
结合全基因组关联分析建立高效的棉花落叶性状田间高通量调查策略以发掘落叶性状的关键基因型变异
Bowei Xu,Le Liu,Rumeng Zhao et al.
Bowei Xu et al.
Pre-harvest defoliation of cotton is a key agricultural measure to improve mechanical harvesting efficiency and raw cotton purity. Collecting data on cotton defoliation traits for genetic localization and thus breeding defoliation-prone var...