Research graph
References from Multi-source UAV remote sensing for cotton Verticillium wilt resistance grading using TPAC-Net. Local targets link to admitted publications; unresolved targets remain external evidence.
Multimodal machine learning: A survey and taxonomy
10.1109/tpami.2018.2798607 · 2019 · External reference
Coincident detection of crop water stress, nitrogen status and canopy density using ground-based multispectral data
2000 · External reference
Lka-gfnet: Language knowledge-augmented graph fusion for tri-source heterogeneous remote sensing data classification
10.1109/tgrs.2026.3668976 · 2026 · External reference
A new reflectance index for remote sensing of chlorophyll content in higher plants: Tests using eucalyptus leaves
10.1016/s0176-1617(99)80314-9 · 1999 · External reference
Rustqnet: Multimodal deep learning for quantitative inversion of wheat stripe rust disease index
10.1016/j.compag.2024.109245 · 2024 · External reference
Res2net: A new multi-scale backbone architecture
10.1109/tpami.2019.2938758 · 2019 · External reference
Use of a green channel in remote sensing of global vegetation from eos-modis
10.1016/s0034-4257(96)00072-7 · 1996 · External reference
A practical approach for grading cotton verticillium wilt severity for remote sensing monitoring
10.1016/j.agrformet.2025.110559 · 2025 · External reference
Mambavision: A hybrid mamba-transformer vision backbone
2025 · External reference
Deep residual learning for image recognition
2016 · External reference
Searching for mobilenetv3
10.1109/iccv.2019.00140 · 2019 · External reference
Attention-based deep multiple instance learning
2018 · External reference
Detection and monitoring wheat diseases using unmanned aerial vehicles (uavs)
10.1016/j.compag.2024.109158 · 2024 · External reference
Imagenet classification with deep convolutional neural networks
2012 · External reference
Deep learning in multimodal remote sensing data fusion: A comprehensive review
10.1016/j.jag.2022.102926 · 2022 · External reference
Severity assessment of cotton canopy verticillium wilt by machine learning based on feature selection and optimization algorithm using uav hyperspectral data
10.3390/rs16244637 · 2024 · External reference
Spectroscopic detection of cotton verticillium wilt by spectral feature selection and machine learning methods
10.3389/fpls.2025.1519001 · 2025 · External reference
Assessing the severity of verticillium wilt in cotton fields and constructing pesticide application prescription maps using unmanned aerial vehicle (uav) multispectral images
10.3390/drones8050176 · 2024 · External reference
A convnet for the 2020s
2022 · External reference
Cotton verticillium wilt monitoring based on uav multispectral-visible multi-source feature fusion
10.1016/j.compag.2024.108628 · 2024 · External reference
Representation learning with contrastive predictive coding
10.48550/arxiv.1807.03748 · 2018 · External reference
Digital assessment of plant diseases: A critical review and analysis of optical sensing technologies for early plant disease diagnosis
10.1016/j.compag.2025.110443 · 2025 · External reference
Recent advances in plant disease detection: challenges and opportunities
10.1186/s13007-025-01450-0 · 2025 · External reference
An overview of the molecular genetics of plant resistance to the verticillium wilt pathogen verticillium dahliae
10.3390/ijms21031120 · 2020 · External reference
Efficientnet: Rethinking model scaling for convolutional neural networks
2019 · External reference
Efficientnetv2: Smaller models and faster training
2021 · External reference
Review of crop phenotyping in field plot experiments using uav-mounted sensors and algorithms
10.3390/drones8060212 · 2024 · External reference
Red and photographic infrared linear combinations for monitoring vegetation
10.1016/0034-4257(79)90013-0 · 1979 · External reference
Deep learning and computer vision in plant disease detection: a comprehensive review of techniques, models, and trends in precision agriculture
10.1007/s10462-024-11100-x · 2025 · External reference
Modeling temporal resistance assessment of cotton to verticillium wilt using airborne hyperspectral data and disease progression rates
10.3390/rs17223701 · 2025 · External reference
Convnext v2: Co-designing and scaling convnets with masked autoencoders
2023 · External reference
A review on plant high-throughput phenotyping traits using uav-based sensors
10.1016/j.compag.2020.105731 · 2020 · External reference
Accurate cotton verticillium wilt segmentation in field background based on the two-stage lightweight deeplabv3+ model
10.1016/j.compag.2024.109814 · 2025 · External reference
Screening verticillium wilt-resistant germplasm by monitoring the time-series chlorophyll content of cotton canopies via a uav-based high-throughput platform
10.1016/j.compag.2025.110791 · 2025 · External reference
A comprehensive review on elucidating the host disease resistance mechanism from the perspective of the interaction between cotton and verticillium dahliae
10.1186/s42397-024-00207-9 · 2025 · External reference
Intelligent agriculture: Deep learning in uav-based remote sensing imagery for crop diseases and pests detection
10.3389/fpls.2024.1435016 · 2024 · External reference
Interactions between verticillium dahliae and cotton: pathogenic mechanism and cotton resistance mechanism to verticillium wilt
10.3389/fpls.2023.1174281 · 2023 · External reference
