Research graph
References from MMTA-ACDD: Multi-teacher online adaptive learning and multimodal augmentation synergy for robust crop disease detection. Local targets link to admitted publications; unresolved targets remain external evidence.
A comparative study of Fourier transform and CycleGAN as domain adaptation techniques for weed segmentation
2023 · External reference
Parameter-free online test-time adaptation
2022 · External reference
A probabilistic framework for lifelong test-time adaptation
2023 · External reference
A simple framework for contrastive learning of visual representations
2020 · External reference
Heterogeneous domain adaptation method for tomato leaf disease classification base on CycleGAN
2023 · External reference
PDDD-pretrain: A series of commonly used pre-trained models support image-based plant disease diagnosis
10.34133/plantphenomics.0054 · 2023 · External reference
Robust crop disease detection using multi-domain data augmentation and isolated test-time adaptation
10.1016/j.eswa.2025.127324 · 2025 · External reference
Unresolved reference
2024 · External reference
PMJDM: A multi-task joint detection model for plant disease identification
10.3389/fpls.2025.1599671 · 2025 · External reference
Cloud-device collaborative adaptation to continual changing environments in the real-world
2023 · External reference
Not just selection, but exploration: Online class-incremental continual learning via dual view consistency
2022 · External reference
Not just selection, but exploration: Online class-incremental continual learning via dual view consistency
2025 · External reference
A simple background augmentation method for object detection with diffusion model
2024 · External reference
A comprehensive survey on test-time adaptation under distribution shifts
10.1007/s11263-024-02181-w · 2025 · External reference
MCDCNet: Multi-scale constrained deformable convolution network for apple leaf disease detection
10.1016/j.compag.2024.109028 · 2024 · External reference
MLFA: Toward realistic test time adaptive object detection by multi-level feature alignment
10.1109/tip.2024.3473532 · 2024 · External reference
Continual-MAE: Adaptive distribution masked autoencoders for continual test-time adaptation
2024 · External reference
SeptoSympto: A precise image analysis of septoria tritici blotch disease symptoms using deep learning methods on scanned images
10.1186/s13007-024-01136-z · 2024 · External reference
Recent advances in image processing techniques for automated leaf pest and disease recognition–a review
2021 · External reference
Unresolved reference
External reference
Distribution-aware continual test-time adaptation for semantic segmentation
2024 · External reference
Effective restoration of source knowledge in continual test time adaptation
2024 · External reference
Efficient test-time model adaptation without forgetting
2022 · External reference
On using artificial intelligence and the internet of things for crop disease detection: A contemporary survey
10.3390/agriculture12010009 · 2022 · External reference
FDRW-Net: A feature dynamic reweighting network for cotton disease detection in natural scenes
10.1016/j.compag.2025.110891 · 2025 · External reference
High-resolution image synthesis with latent diffusion models
2022 · External reference
PlantDoc: A dataset for visual plant disease detection
2020 · External reference
TEST: Test-time self-training under distribution shift
2023 · External reference
Ar-TTA: A simple method for real-world continual test-time adaptation
2023 · External reference
Multi-kernel inception aggregation diffusion network for tomato disease detection
10.1186/s12870-024-05797-9 · 2024 · External reference
Efficient deep learning-based tomato leaf disease detection through global and local feature fusion
10.1186/s12870-025-06247-w · 2025 · External reference
YOLO-FMDI: A lightweight YOLOv8 focusing on a multi-scale feature diffusion interaction neck for tomato pest and disease detection
10.3390/electronics13152974 · 2024 · External reference
Mean teachers are better role models: Weight-averaged consistency targets improve semi-supervised deep learning results
2017 · External reference
FCOS: Fully convolutional one-stage object detection
2019 · External reference
Continual test-time domain adaptation
2022 · External reference
Unresolved reference
2020 · External reference
Enhancing weed detection using UAV imagery and deep learning with weather-driven domain adaptation
10.1016/j.compag.2025.110673 · 2025 · External reference
CSPNet: A feature interaction network for tomato leaf disease detection in complex scenarios
10.1016/j.compag.2025.110823 · 2025 · External reference
What, how, and when should object detectors update in continually changing test domains?
2024 · External reference
What how and when should object detectors update in continually changing test domains?
2024 · External reference
Unresolved reference
2021 · External reference
Unresolved reference
2023 · External reference
Research on polygon pest-infected leaf region detection based on YOLOv8
10.3390/agriculture13122253 · 2023 · External reference
Unpaired image-to-image translation using cycle-consistent adversarial networks
2017 · External reference
A comprehensive survey on test-time adaptation under distribution shifts
10.1007/s11263-024-02181-w · ExternalCitation · doi-reference
MCDCNet: Multi-scale constrained deformable convolution network for apple leaf disease detection
10.1016/j.compag.2024.109028 · ExternalCitation · doi-reference
Enhancing weed detection using UAV imagery and deep learning with weather-driven domain adaptation
10.1016/j.compag.2025.110673 · ExternalCitation · doi-reference
CSPNet: A feature interaction network for tomato leaf disease detection in complex scenarios
10.1016/j.compag.2025.110823 · ExternalCitation · doi-reference
FDRW-Net: A feature dynamic reweighting network for cotton disease detection in natural scenes
10.1016/j.compag.2025.110891 · ExternalCitation · doi-reference
Robust crop disease detection using multi-domain data augmentation and isolated test-time adaptation
10.1016/j.eswa.2025.127324 · ExternalCitation · doi-reference
MLFA: Toward realistic test time adaptive object detection by multi-level feature alignment
10.1109/tip.2024.3473532 · ExternalCitation · doi-reference
Multi-kernel inception aggregation diffusion network for tomato disease detection
10.1186/s12870-024-05797-9 · ExternalCitation · doi-reference
Efficient deep learning-based tomato leaf disease detection through global and local feature fusion
10.1186/s12870-025-06247-w · ExternalCitation · doi-reference
SeptoSympto: A precise image analysis of septoria tritici blotch disease symptoms using deep learning methods on scanned images
10.1186/s13007-024-01136-z · ExternalCitation · doi-reference
PMJDM: A multi-task joint detection model for plant disease identification
10.3389/fpls.2025.1599671 · ExternalCitation · doi-reference
On using artificial intelligence and the internet of things for crop disease detection: A contemporary survey
10.3390/agriculture12010009 · ExternalCitation · doi-reference
Research on polygon pest-infected leaf region detection based on YOLOv8
10.3390/agriculture13122253 · ExternalCitation · doi-reference
YOLO-FMDI: A lightweight YOLOv8 focusing on a multi-scale feature diffusion interaction neck for tomato pest and disease detection
10.3390/electronics13152974 · ExternalCitation · doi-reference
PDDD-pretrain: A series of commonly used pre-trained models support image-based plant disease diagnosis
10.34133/plantphenomics.0054 · ExternalCitation · doi-reference
