A Double Swath Configuration for Improving Throughput and Accuracy of Trait Estimate from UAV Images [0.03%]
一种双带宽配置方法以提高无人机图像表型信息的通量和精度
Wenjuan Li,Alexis Comar,Marie Weiss et al.
Wenjuan Li et al.
Multispectral observations from unmanned aerial vehicles (UAVs) are currently used for precision agriculture and crop phenotyping applications to monitor a series of traits allowing the characterization of the vegetation status. However, th...
Automatic Microplot Localization Using UAV Images and a Hierarchical Image-Based Optimization Method [0.03%]
基于图像的层次优化方法用于利用无人机图像自动定位小区地块位置
Sara Mardanisamani,Tewodros W Ayalew,Minhajul Arifin Badhon et al.
Sara Mardanisamani et al.
To develop new crop varieties and monitor plant growth, health, and traits, automated analysis of aerial crop images is an attractive alternative to time-consuming manual inspection. To perform per-microplot phenotypic analysis, localizing ...
Complementary Phenotyping of Maize Root System Architecture by Root Pulling Force and X-Ray Imaging [0.03%]
通过根拉力和X射线成像对玉米根系结构进行互补表型分析
M R Shao,N Jiang,M Li et al.
M R Shao et al.
The root system is critical for the survival of nearly all land plants and a key target for improving abiotic stress tolerance, nutrient accumulation, and yield in crop species. Although many methods of root phenotyping exist, within field ...
Global Wheat Head Detection 2021: An Improved Dataset for Benchmarking Wheat Head Detection Methods [0.03%]
2021年全球小麦穗检测：用于改进小麦穗检测方法基准的数据库
Etienne David,Mario Serouart,Daniel Smith et al.
Etienne David et al.
The Global Wheat Head Detection (GWHD) dataset was created in 2020 and has assembled 193,634 labelled wheat heads from 4700 RGB images acquired from various acquisition platforms and 7 countries/institutions. With an associated competition ...
GANana: Unsupervised Domain Adaptation for Volumetric Regression of Fruit [0.03%]
GANana：用于水果体积回归的无监督领域自适应
Zane K J Hartley,Aaron S Jackson,Michael Pound et al.
Zane K J Hartley et al.
3D reconstruction of fruit is important as a key component of fruit grading and an important part of many size estimation pipelines. Like many computer vision challenges, the 3D reconstruction task suffers from a lack of readily available t...
Detecting Sorghum Plant and Head Features from Multispectral UAV Imagery [0.03%]
从多光谱无人机图像中检测高粱植物和穗特征
Yan Zhao,Bangyou Zheng,Scott C Chapman et al.
Yan Zhao et al.
In plant breeding, unmanned aerial vehicles (UAVs) carrying multispectral cameras have demonstrated increasing utility for high-throughput phenotyping (HTP) to aid the interpretation of genotype and environment effects on morphological, bio...
Exploring Seasonal and Circadian Rhythms in Structural Traits of Field Maize from LiDAR Time Series [0.03%]
基于LiDAR时间序列的田间玉米结构特征的季节和昼夜节律研究
Shichao Jin,Yanjun Su,Yongguang Zhang et al.
Shichao Jin et al.
Plant growth rhythm in structural traits is important for better understanding plant response to the ever-changing environment. Terrestrial laser scanning (TLS) is a well-suited tool to study structural rhythm under field conditions. Recent...
David M Deery,Hamlyn G Jones
David M Deery
Field phenomics has been identified as a promising enabling technology to assist plant breeders with the development of improved cultivars for farmers. Yet, despite much investment, there are few examples demonstrating the application of ph...
Estimates of Maize Plant Density from UAV RGB Images Using Faster-RCNN Detection Model: Impact of the Spatial Resolution [0.03%]
基于Faster-RCNN检测模型的UAV RGB图像对玉米植株密度的估算：空间分辨率的影响
K Velumani,R Lopez-Lozano,S Madec et al.
K Velumani et al.
Early-stage plant density is an essential trait that determines the fate of a genotype under given environmental conditions and management practices. The use of RGB images taken from UAVs may replace the traditional visual counting in field...
Classification of Soybean Pubescence from Multispectral Aerial Imagery [0.03%]
基于多光谱航空影像的 soybean pubescence 级别分类研究
Robert W Bruce,Istvan Rajcan,John Sulik
Robert W Bruce
The accurate determination of soybean pubescence is essential for plant breeding programs and cultivar registration. Currently, soybean pubescence is classified visually, which is a labor-intensive and time-consuming activity. Additionally,...