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Dashuai Wang, Changxing Geng
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Smart farming using artificial intelligence: a review
10.1016/j.engappai.2023.105899 · 2023
LodgeNet: an automated framework for precise detection and classification of wheat lodging severity levels in precision farming
10.3389/fpls.2023.1255961 · 2023
Impact of climate change on agriculture production and its sustainable solutions
10.1007/s42398-019-00078-w · 2019
Comprehensive wheat lodging detection after initial lodging using UAV RGB images
10.1016/j.eswa.2023.121788 · 2024
Estimation of soybean yield parameters under lodging conditions using RGB information from unmanned aerial vehicles
10.3389/fpls.2022.1012293 · 2022
A method for the assessment of the risk of wheat lodging
10.1006/jtbi.1998.0778 · 1998
A generalised model of crop lodging
10.1016/j.jtbi.2014.07.032 · 2014
Development and application of a model for calculating the risk of stem and root lodging in maize
10.1016/j.fcr.2020.108037 · 2021
Predicting yield losses caused by lodging in wheat
10.1016/j.fcr.2012.07.019 · 2012
A comparison of root and stem lodging risks among winter wheat cultivars
10.1017/s002185960300354x · 2003
Understanding and reducing lodging in cereals
2004
Damage assessment due to wheat lodging using UAV-based multispectral and thermal imageries
10.1007/s12524-023-01680-6 · 2023
Timely assessment of maize lodging severity with limited samples using multi-temporal Sentinel-1 and Sentinel-2 data across large spatial extents
10.1016/j.compag.2025.110671 · 2025
Use of unmanned aerial vehicle imagery and a hybrid algorithm combining a watershed algorithm and adaptive threshold segmentation to extract wheat lodging
10.1016/j.pce.2021.103016 · 2021
Estimation of crop angle of inclination for lodged wheat using multi-sensor SAR data
10.1016/j.rse.2019.111488 · 2020
Discriminant analysis for lodging severity classification in wheat using RADARSAT-2 and Sentinel-1 data
10.1016/j.isprsjprs.2020.04.012 · 2020
Remote sensing-based crop lodging assessment: current status and perspectives
10.1016/j.isprsjprs.2019.03.005 · 2019
Understanding wheat lodging using multi-temporal Sentinel-1 and Sentinel-2 data
10.1016/j.rse.2020.111804 · 2020
Mapping of wheat lodging susceptibility with synthetic aperture radar data
10.1016/j.rse.2021.112427 · 2021
UAV remote sensing imagery-based semantic segmentation approach for lodged rice region
2025
Methods and datasets on semantic segmentation for unmanned aerial vehicle remote sensing images: a review
10.1016/j.isprsjprs.2024.03.012 · 2024
A decision-tree approach to identifying paddy rice lodging with multiple pieces of polarization information derived from Sentinel-1
10.3390/rs15010240 · 2022
Segmentation of wheat lodging areas from UAV imagery using an ultra-lightweight network
10.3390/agriculture14020244 · 2024
RTAL: an edge computing method for real-time rice lodging assessment
10.1016/j.compag.2023.108386 · 2023
Food security: the challenge of feeding 9 billion people
10.1126/science.1185383 · 2010
A novel approach to estimate maize lodging area with PolSAR data
2022
An improved approach to estimating crop lodging percentage with Sentinel-2 imagery using machine learning
2022
A quantitative monitoring method for determining maize lodging in different growth stages
10.3390/rs12193149 · 2020
SWRD-YOLO: a lightweight instance segmentation model for estimating rice lodging degree in UAV remote sensing images with real-time edge deployment
10.3390/agriculture15151570 · 2025
Automatic detection of crop lodging from multitemporal satellite data based on the isolation forest algorithm
10.1016/j.compag.2023.108415 · 2023
SegNeXt-RCMSCA: an improved SegNeXt network for detecting winter wheat lodging from UAS RGB images
2025
What’s next in agri-vision: a review of Vision Transformers and Vision Mamba in crop management
10.1016/j.compag.2026.111551 · 2026
Quantitative identification of maize lodging-causing feature factors using unmanned aerial vehicle images and a nomogram computation
10.3390/rs10101528 · 2018
An explainable XGBoost model improved by SMOTE-ENN technique for maize lodging detection based on multi-source unmanned aerial vehicle images
10.1016/j.compag.2022.106804 · 2022
Review of synthetic aperture radar with deep learning in agricultural applications
10.1016/j.isprsjprs.2024.08.018 · 2024
Comparison of the performance of multi-source three-dimensional structural data in the application of monitoring maize lodging
10.1016/j.compag.2023.107782 · 2023
Assessing the self-recovery ability of maize after lodging using UAV-LiDAR data
10.3390/rs13122270 · 2021
Identifying key factors influencing maize stalk lodging resistance through wind tunnel simulations with machine learning algorithms
2025
Radar taking off: New capabilities for UAVs
10.1109/mmm.2018.2862558 · 2018
State-of-the-art and future research challenges in UAV swarms
10.1109/jiot.2024.3364230 · 2024
WLUSNet: a lightweight wheat lodging segmentation network based on UAV image
10.1016/j.compag.2025.110587 · doi-reference
Soybean yield estimation and lodging classification based on UAV multi-source data and self-supervised contrastive learning
10.1016/j.compag.2024.109822 · doi-reference
Soybean yield estimation and lodging discrimination based on lightweight UAV and point cloud deep learning
10.1016/j.plaphe.2025.100028 · doi-reference
Analysis of plant height changes of lodged maize using UAV-LiDAR data
10.3390/agriculture10050146 · doi-reference
Remote sensing of regional-scale maize lodging using multitemporal GF-1 images
10.1117/1.jrs.14.014514 · doi-reference
Collaborative wheat lodging segmentation semi-supervised learning model based on RSE-BiSeNet using UAV imagery
10.3390/agronomy13112772 · doi-reference
Use of unmanned aerial vehicle imagery and deep learning unet to extract rice lodging
10.3390/s19183859 · doi-reference
A systematic review and assessment of inverse crop parameter modeling based on synthetic aperture radar data: research advances, existing problems, and future directions
10.1109/mgrs.2024.3454317 · doi-reference
UAV-multispectral based maize lodging stress assessment with machine and deep learning methods
10.3390/agriculture15010036 · doi-reference
Application of UAV RGB images and improved PSPNet network to the identification of wheat lodging areas
10.3390/agronomy13051309 · doi-reference
Automatic wheat lodging detection and mapping in aerial imagery to support high-throughput phenotyping and in-season crop management
10.3390/agronomy10111762 · doi-reference
10.3390/rs12111838
10.3390/rs12111838 · doi-reference
A multi-scale rice lodging monitoring method based on MSR-Lodfnet
10.3390/agriculture15232487 · doi-reference
A review of the application of UAV multispectral remote sensing technology in precision agriculture
10.1007/978-981-96-5747-6 · doi-reference
Unraveling the spatial-temporal patterns of typhoon impacts on maize during the milk stage in Northeast China in 2020
10.1016/j.eja.2024.127169 · doi-reference
Identifying rice lodging based on semantic segmentation architecture optimization with UAV remote sensing imaging
10.1016/j.compag.2024.109570 · doi-reference
AAUConvNeXt: Enhancing crop lodging segmentation with optimized deep learning architectures
10.34133/plantphenomics.0182 · doi-reference
Efficient wheat lodging detection using UAV remote sensing images and an innovative multi-branch classification framework
10.3390/rs15184572 · doi-reference
Enhancing model performance in detecting lodging areas in wheat fields using UAV RGB Imagery: considering spatial and temporal variations
10.1016/j.compag.2023.108297 · doi-reference
Automatic extraction of wheat lodging area based on transfer learning method and deeplabv3+ network
10.1016/j.compag.2020.105845 · doi-reference
Automatic grading evaluation of winter wheat lodging based on deep learning
10.3389/fpls.2024.1284861 · doi-reference
Wheat lodging segmentation based on Lstm_PSPNet deep learning network
10.3390/drones7020143 · doi-reference
Classification of maize lodging extents using deep learning algorithms by UAV-based RGB and multispectral images
10.3390/agriculture12070970 · doi-reference
Semantic segmentation using deep learning with vegetation indices for rice lodging identification in multi-date UAV visible images
10.3390/rs12040633 · doi-reference
Rule-based multi-task deep learning for highly efficient rice lodging segmentation
10.3390/rs17091505 · doi-reference
Spatial and spectral hybrid image classification for rice lodging assessment through UAV imagery
10.3390/rs9060583 · doi-reference
Adaptive autonomous UAV scouting for rice lodging assessment using edge computing with deep learning EDANet
10.1016/j.compag.2020.105817 · doi-reference
An integrated strategy coordinating endogenous and exogenous approaches to alleviate crop lodging
10.1016/j.stress.2023.100197 · doi-reference
Accurate wheat lodging extraction from multi-channel UAV images using a lightweight network model
10.3390/s21206826 · doi-reference
Soybean lodging classification and yield prediction using multimodal UAV data fusion and deep learning
10.3390/rs17091490 · doi-reference
FarmSeg_VLM: a farmland remote sensing image segmentation method considering vision-language alignment
10.1016/j.isprsjprs.2025.05.010 · doi-reference
Quantifying lodging percentage and lodging severity using a UAV-based canopy height model combined with an objective threshold approach
10.3390/rs11050515 · doi-reference
Remote sensing for agricultural applications: a meta-review
10.1016/j.rse.2019.111402 · doi-reference
Spatial-temporal distribution and hazard assessment of maize lodging in a synergistic disaster environment
10.1016/j.agrformet.2023.109730 · doi-reference
Detection and analysis of degree of maize lodging using UAV-RGB image multi-feature factors and various classification methods
10.3390/ijgi10050309 · doi-reference
MSSVT: Multi-branch spatial-spectral volumetric transformer for rice lodging segmentation with UAV multimodal fusion
10.1016/j.compag.2026.112087 · doi-reference
UAV LiDAR-based automated detection of maize lodging in complex agroecosystems
10.3390/drones9120876 · doi-reference
Complementary-aware collaborative fusion: advancing precise wheat lodging mapping using UAV-borne RGB imagery and LiDAR data
10.1016/j.compag.2026.112109 · doi-reference
Wind-induced response of rapeseed seedling stage and lodging prediction based on UAV imagery and machine learning methods
10.1016/j.compag.2024.108637 · doi-reference
A grid-level segmentation model based on encoder-decoder structure with multi-source features for crop lodging detection
10.1016/j.asoc.2023.111113 · doi-reference