TrichomeYOLO: A Neural Network for Automatic Maize Trichome Counting [0.03%]
基于神经网络的自动计算玉米气孔数的方法：TrichomeYOLO
Jie Xu,Jia Yao,Hang Zhai et al.
Jie Xu et al.
Plant trichomes are epidermal structures with a wide variety of functions in plant development and stress responses. Although the functional importance of trichomes has been realized, the tedious and time-consuming manual phenotyping proces...
Semi-Self-Supervised Learning for Semantic Segmentation in Images with Dense Patterns [0.03%]
具有密集图案的图像的语义分割的半自监督学习方法研究
Keyhan Najafian,Alireza Ghanbari,Mahdi Sabet Kish et al.
Keyhan Najafian et al.
Deep learning has shown potential in domains with large-scale annotated datasets. However, manual annotation is expensive, time-consuming, and tedious. Pixel-level annotations are particularly costly for semantic segmentation in images with...
The Gray Mold Spore Detection of Cucumber Based on Microscopic Image and Deep Learning [0.03%]
基于显微图像和深度学习的黄瓜灰霉病菌检测方法研究
Kaiyu Li,Xinyi Zhu,Chen Qiao et al.
Kaiyu Li et al.
Rapid and accurate detection of pathogen spores is an important step to achieve early diagnosis of diseases in precision agriculture. Traditional detection methods are time-consuming, laborious, and subjective, and image processing methods ...
SegVeg: Segmenting RGB Images into Green and Senescent Vegetation by Combining Deep and Shallow Methods [0.03%]
SegVeg：通过结合深度与浅层方法将RGB图像分割成绿色植被和枯萎植被
Mario Serouart,Simon Madec,Etienne David et al.
Mario Serouart et al.
Pixel segmentation of high-resolution RGB images into chlorophyll-active or nonactive vegetation classes is a first step often required before estimating key traits of interest. We have developed the SegVeg approach for semantic segmentatio...
BFP Net: Balanced Feature Pyramid Network for Small Apple Detection in Complex Orchard Environment [0.03%]
基于平衡特征金字塔的复杂果园环境下的小目标苹果检测方法(BFP Net)
Meili Sun,Liancheng Xu,Xiude Chen et al.
Meili Sun et al.
Despite of significant achievements made in the detection of target fruits, small fruit detection remains a great challenge, especially for immature small green fruits with a few pixels. The closeness of color between the fruit skin and the...
Deep Learning for Strawberry Canopy Delineation and Biomass Prediction from High-Resolution Images [0.03%]
基于高分辨率图像的深度学习草莓冠层分割及生物量预测方法
Caiwang Zheng,Amr Abd-Elrahman,Vance M Whitaker et al.
Caiwang Zheng et al.
Modeling plant canopy biophysical parameters at the individual plant level remains a major challenge. This study presents a workflow for automatic strawberry canopy delineation and biomass prediction from high-resolution images using deep n...
EasyDAM_V2: Efficient Data Labeling Method for Multishape, Cross-Species Fruit Detection [0.03%]
高效多形状跨物种水果检测数据标注方法EasyDAM_V2
Wenli Zhang,Kaizhen Chen,Chao Zheng et al.
Wenli Zhang et al.
In modern smart orchards, fruit detection models based on deep learning require expensive dataset labeling work to support the construction of detection models, resulting in high model application costs. Our previous work combined generativ...
Application of UAV Multisensor Data and Ensemble Approach for High-Throughput Estimation of Maize Phenotyping Traits [0.03%]
基于无人机多传感器数据和集成方法的玉米表型性状高通量估测应用研究
Meiyan Shu,Shuaipeng Fei,Bingyu Zhang et al.
Meiyan Shu et al.
High-throughput estimation of phenotypic traits from UAV (unmanned aerial vehicle) images is helpful to improve the screening efficiency of breeding maize. Accurately estimating phenotyping traits of breeding maize at plot scale helps to pr...
Spectral Preprocessing Combined with Deep Transfer Learning to Evaluate Chlorophyll Content in Cotton Leaves [0.03%]
基于光谱预处理的深度迁移学习棉花叶片叶绿素含量评价模型
Qinlin Xiao,Wentan Tang,Chu Zhang et al.
Qinlin Xiao et al.
Rapid determination of chlorophyll content is significant for evaluating cotton's nutritional and physiological status. Hyperspectral technology equipped with multivariate analysis methods has been widely used for chlorophyll content detect...
A Review of High-Throughput Field Phenotyping Systems: Focusing on Ground Robots [0.03%]
高通量田间表型系统的综述：聚焦地面机器人系统
Rui Xu,Changying Li
Rui Xu
Manual assessments of plant phenotypes in the field can be labor-intensive and inefficient. The high-throughput field phenotyping systems and in particular robotic systems play an important role to automate data collection and to measure no...