4.6 Article

Soybean Seedling Root Segmentation Using Improved U-Net Network

Related references

Note: Only part of the references are listed.
Article Computer Science, Artificial Intelligence

Beyond Self-Attention: External Attention Using Two Linear Layers for Visual Tasks

Meng-Hao Guo et al.

Summary: Attention mechanisms, especially self-attention, are crucial for deep feature representation in visual tasks. This article proposes a novel attention mechanism called external attention, which uses shared memories to capture correlations between samples and has lower computational and memory costs compared to self-attention. Experimental results demonstrate that our method achieves comparable or superior results in various visual tasks.

IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE (2023)

Article Agronomy

Screening of Soybean Genotypes Based on Root Morphology and Shoot Traits Using the Semi-Hydroponic Phenotyping Platform and Rhizobox Technique

Mohammad Salim et al.

Summary: Root-system architecture is crucial for soybean growth and nutrient uptake. There is significant variation in root and shoot traits among different soybean genotypes. Genotypes with larger root systems exhibit higher harvest index and leaf area, possibly due to increased canopy photosynthesis providing carbon assimilates to the roots. Root traits such as total root length and root:shoot ratio measured in the rhizobox study are strongly correlated with the same traits in the semi-hydroponic system, suggesting their potential as predictive indicators for soybean genotypes.

AGRONOMY-BASEL (2022)

Article Chemistry, Analytical

AGs-Unet: Building Extraction Model for High Resolution Remote Sensing Images Based on Attention Gates U Network

Mingyang Yu et al.

Summary: This paper introduces a building contour extraction method based on the U-Net network and its application in remote sensing images. By introducing an attention mechanism and a novel Attention Gate module, the proposed AGs-Unet model can effectively improve the accuracy and performance of building extraction.

SENSORS (2022)

Article Chemistry, Analytical

Weld Feature Extraction Based on Semantic Segmentation Network

Bin Wang et al.

Summary: In this study, a weld tracking module was designed to capture real-time images of the weld in order to obtain accurate position information of the weld joint. By utilizing an encoder-decoder architecture to design a lightweight network structure and introducing a channel attention mechanism, the network model achieved faster segmentation speed and higher segmentation accuracy.

SENSORS (2022)

Review Computer Science, Software Engineering

Attention mechanisms in computer vision: A survey

Meng-Hao Guo et al.

Summary: Attention mechanisms, inspired by the human visual system, have been successfully applied in various computer vision tasks. This survey provides a comprehensive review of different types of attention mechanisms and suggests future research directions.

COMPUTATIONAL VISUAL MEDIA (2022)

Article Engineering, Environmental

TiO2-X mesoporous nanospheres/BiOI nanosheets S-scheme heterostructure for high efficiency, stable and unbiased photocatalytic hydrogen production

Bingke Zhang et al.

Summary: In this study, a hierarchical S-scheme photocatalyst composite was prepared, which achieved efficient hydrogen production through an optimized heterostructure.

CHEMICAL ENGINEERING JOURNAL (2022)

Article Agriculture, Multidisciplinary

Semantic segmentation model of cotton roots in-situ image based on attention mechanism

Jia Kang et al.

Summary: The growth and distribution of root system in soil plays a crucial role in plant growth and crop production. This study focused on the segmentation of cotton mature root system using a semantic segmentation model with attention mechanism, which showed higher accuracy and efficiency compared to other models. The proposed model accurately distinguishes cotton roots from complex soil background, providing important theoretical value and practical application reference for deep learning in plant root segmentation.

COMPUTERS AND ELECTRONICS IN AGRICULTURE (2021)

Article Robotics

Development and performance evaluation of a machine vision system and an integrated prototype for automated green shoot thinning in vineyards

Yaqoob Majeed et al.

Summary: The study introduced a machine vision-based system and integrated green shoot thinning system for accurately estimating vine cordon trajectories and automatically positioning the thinning end-effector. Field evaluations showed that the integrated system can achieve an RMSE of 1.47 cm in following the cordon trajectories at 6.6 cm/s forward speed. Future work will focus on incorporating additional sensing systems to achieve a higher level of precision in green shoot thinning.

JOURNAL OF FIELD ROBOTICS (2021)

Article Biotechnology & Applied Microbiology

Mapping and validation of a major QTL for primary root length of soybean seedlings grown in hydroponic conditions

Huatao Chen et al.

Summary: This study identified and validated a novel QTL, qRL16.1, associated with primary root length in soybean through hydroponic conditions. The results demonstrated that qRL16.1 is a major QTL influencing root development in soybean.

BMC GENOMICS (2021)

Article Agriculture, Multidisciplinary

Pixel level segmentation of early-stage in-bag rice root for its architecture analysis

Liang Gong et al.

Summary: The study of plant growth state relies on root architecture parameters, with root segmentation being crucial to measuring these parameters. A new method based on a convolutional neural network was proposed for pixel-level segmentation of rice roots under strong noise, achieving an intersection over union (IoU) of 87.4%. This approach provides an automatic and fast pixel-level root segmentation method, essential for root morphology analysis.

COMPUTERS AND ELECTRONICS IN AGRICULTURE (2021)

Article Plant Sciences

A Large Root Phenome Dataset Wide-Opened the Potential for Underground Breeding in Soybean

Ki-Seung Kim et al.

Summary: The study analyzed the root morphological traits of 150 wild and 50 cultivated soybean varieties, finding significant differences in root traits between cultivated and wild plants. Total root length was highly correlated with projected area in both cultivated (0.92) and wild (0.82) plants, with higher variability among cultivated plants compared to wild plants.

FRONTIERS IN PLANT SCIENCE (2021)

Review Biochemistry & Molecular Biology

Crop Phenomics and High-Throughput Phenotyping: Past Decades, Current Challenges, and Future Perspectives

Wanneng Yang et al.

MOLECULAR PLANT (2020)

Article Biochemical Research Methods

Computer vision and machine learning enabled soybean root phenotyping pipeline

Kevin G. Falk et al.

PLANT METHODS (2020)

Article Biochemical Research Methods

Segmentation of roots in soil with U-Net

Abraham George Smith et al.

PLANT METHODS (2020)

Article Computer Science, Artificial Intelligence

Squeeze-and-Excitation Networks

Jie Hu et al.

IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE (2020)

Article Agriculture, Multidisciplinary

Estimating the trajectories of vine cordons in full foliage canopies for automated green shoot thinning in vineyards

Yaqoob Majeed et al.

COMPUTERS AND ELECTRONICS IN AGRICULTURE (2020)

Article Agriculture, Multidisciplinary

SegRoot: A high throughput segmentation method for root image analysis

Tao Wang et al.

COMPUTERS AND ELECTRONICS IN AGRICULTURE (2019)

Article Computer Science, Artificial Intelligence

SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation

Vijay Badrinarayanan et al.

IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE (2017)

Review Multidisciplinary Sciences

Deep learning

Yann LeCun et al.

NATURE (2015)