{"id":716,"date":"2025-11-11T18:02:31","date_gmt":"2025-11-11T17:02:31","guid":{"rendered":"https:\/\/sylphagro.fr\/?page_id=716"},"modified":"2025-11-14T19:03:18","modified_gmt":"2025-11-14T18:03:18","slug":"bibliography","status":"publish","type":"page","link":"https:\/\/sylphagro.fr\/index.php\/bibliography\/","title":{"rendered":"Bibliography"},"content":{"rendered":"\n

Une s\u00e9lection de publications scientifiques (open access) concernant le ph\u00e9notypage par drone, et des th\u00e8mes associ\u00e9s
A selection of scientific publications (open access) on drone phenotyping and related topics<\/em><\/p>\n\n\n\n\n\n\n\t\n\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t
Title and URL<\/strong><\/th>Year-Month<\/strong><\/th>Crop<\/strong><\/th>Keywords<\/strong><\/th><\/td>\n<\/tr>\n<\/thead>\n
Integrating Remote-Sensing Data: UAV Multispectral Imagery, Drone-Derived 3D Canopy Traits and Gridded Climate Variables to Support Potassium Management and Soybean Yield Estimation<\/a><\/td>26-04<\/td>Soybean<\/td>fertilization, multispectral<\/td><\/td>\n<\/tr>\n
Analysis of Spectral Reflectance Derived from UAV-Embedded Multispectral and Thermal Sensors as a Function of Soil Moisture Gradient<\/a><\/td>26-03<\/td>N\/A<\/td>multispectral, soil moisture, thermal<\/td><\/td>\n<\/tr>\n
Effects of Cane Density on Primocane Raspberry Assessed Using UAV-Based Multispectral Imaging<\/a><\/td>26-03<\/td>raspberry<\/td>multispectral, yield<\/td><\/td>\n<\/tr>\n
Estimation of Cotton Above-Ground Biomass Based on Fusion of UAV Spectral and Texture Features<\/a><\/td>26-03<\/td>Cotton<\/td>biomass, multispectral<\/td><\/td>\n<\/tr>\n
Estimation of Crop Coefficients of a High-Density Hazelnut Orchard Using Traditional Methods vs. UAV-Derived Thermal and Spectral Indices<\/a><\/td>26-03<\/td>Hazelnut<\/td>multispectral, thermal<\/td><\/td>\n<\/tr>\n
Inversion of kiwifruit canopy nitrogen using UAV multispectral technology and ensemble learning<\/a><\/td>26-03<\/td>Kiwi<\/td>multispectral, nitrogen<\/td><\/td>\n<\/tr>\n
Multi-Class Weed Quantification Based on U-Net Convolutional Neural Networks Using UAV Imagery<\/a><\/td>26-03<\/td>Potato<\/td>weed<\/td><\/td>\n<\/tr>\n
Spatial and temporal scales in plant phenotyping for crop water stress assessment: A review<\/a><\/td>26-03<\/td>N\/A<\/td>water stress<\/td><\/td>\n<\/tr>\n
Characterizing Cotton Defoliation Progress via UAV-Based Multispectral-Derived Leaf Area Index and Analysis of Influencing Factors<\/a><\/td>26-02<\/td>Cotton<\/td>LAI, multispectral<\/td><\/td>\n<\/tr>\n
Decoupling canopy structure effects from vegetation indices for robust assessment of potato plant nitrogen content across growth stages<\/a><\/td>26-02<\/td>Potato<\/td>multispectral, nitrogen<\/td><\/td>\n<\/tr>\n
Estimation of Canopy Traits and Yield in Maize\u2013Soybean Intercropping Systems Using UAV Multispectral Imagery and Machine Learning<\/a><\/td>26-02<\/td>maize, soybean<\/td>multispectral, yield<\/td><\/td>\n<\/tr>\n
From single to multi-sensor UAV strategies: Growth-stage-specific AI modeling improves soil moisture estimation in maize field<\/a><\/td>26-02<\/td>maize<\/td>soil moisture, thermal<\/td><\/td>\n<\/tr>\n
Full growth period inversion of peanut canopy chlorophyll content based on UAV multispectral data and machine learning: A stage-specific optimization strategy<\/a><\/td>26-02<\/td>peanut<\/td>chlorophyll, multispectral<\/td><\/td>\n<\/tr>\n
Monitoring of Summer Maize Growth Status and Nitrogen Based on Field Characteristic Data and UAV Multispectral Technology<\/a><\/td>26-02<\/td>maize<\/td>multispectral, nitrogen<\/td><\/td>\n<\/tr>\n
Multiple ortho-mosaicking software pipelines produce comparable imagery-derived wheat phenotypes<\/a><\/td>26-02<\/td>Wheat<\/td>photogrammetry<\/td><\/td>\n<\/tr>\n
Potential of RGB-Derived Vegetation Indices as an Alternative to NIR-Based Vegetation Indices to Monitor Nitrogen Status in Maize<\/a><\/td>26-02<\/td>maize<\/td>nitrogen<\/td><\/td>\n<\/tr>\n
Temporal Dynamics of UAV Multispectral Vegetation Indices for Accurate Machine Learning-Based Wheat Yield Prediction<\/a><\/td>26-02<\/td>Wheat<\/td>multispectral, yield<\/td><\/td>\n<\/tr>\n
UAV multispectral sensing and data-driven modeling for precision onion yield prediction<\/a><\/td>26-02<\/td>onion<\/td>multispectral, yield<\/td><\/td>\n<\/tr>\n
UAV-Based Computer Vision Approach for Melon Fruit Detection and Yield Estimation<\/a><\/td>26-02<\/td>melon<\/td>counting, yield<\/td><\/td>\n<\/tr>\n
Accurate Estimation of Spring Maize Aboveground Biomass in Arid Regions Based on Integrated UAV Remote Sensing Feature Selection<\/a><\/td>26-01<\/td>Maize<\/td>biomass, multispectral<\/td><\/td>\n<\/tr>\n
Ambrosia artemisiifolia\u00a0in Hungary: A Review of Challenges, Impacts, and Precision Agriculture Approaches for Sustainable Site-Specific Weed Management Using UAV Technologies<\/a><\/td>26-01<\/td>multiple<\/td>weed<\/td><\/td>\n<\/tr>\n
Assessment of Premium Citrus Fruit Production Potential Based on Multi-Spectral Remote Sensing with Unmanned Aerial Vehicles<\/a><\/td>26-01<\/td>Citrus<\/td>yield<\/td><\/td>\n<\/tr>\n
Attention-based pretrained deep learning framework for nutrient deficiency diagnosis in oilseed rape using UAV multispectral imagery<\/a><\/td>26-01<\/td>Oilseed rape<\/td>multispectral, nitrogen, nutrient deficiency<\/td><\/td>\n<\/tr>\n
CNN-based wheat yield prediction using multi-source and multi-stage data integration from UAV imagery and sensors<\/a><\/td>26-01<\/td>Wheat<\/td>LAI, multispectral, NDVI, yield<\/td><\/td>\n<\/tr>\n
Cross-calibration of UAV multispectral sensors for green area index estimation<\/a><\/td>26-01<\/td>multiple<\/td>multispectral<\/td><\/td>\n<\/tr>\n
Deep Learning for Palm Tree Health Assessment: UAV-Based Segmentation in the Figuig Region of Morocco<\/a><\/td>26-01<\/td>Palm tree<\/td>disease<\/td><\/td>\n<\/tr>\n
Direct UAV-Based Detection of\u00a0Botrytis cinerea\u00a0in Vineyards Using Chlorophyll-Absorption Indices and YOLO Deep Learning<\/a><\/td>26-01<\/td>Grape<\/td>disease, multispectral<\/td><\/td>\n<\/tr>\n
Early detection of\u00a0Hellula undalis\u00a0in\u00a0Brassica oleracea var capitata L.\u00a0: A real-time drone based approach using YOLOv8<\/a><\/td>26-01<\/td>Cabbage<\/td>pest<\/td><\/td>\n<\/tr>\n
Enhancing multi-stage and multi-depth soil moisture estimation in winter wheat fields with UAV remote sensing fusion and ensemble learning strategy<\/a><\/td>26-01<\/td>Wheat<\/td>soil moisture<\/td><\/td>\n<\/tr>\n
Estimation of soybean phenotypic parameters across growth stages using UAV-based multi-source feature fusion and XGBoost<\/a><\/td>26-01<\/td>Soybean<\/td>LAI, biomass<\/td><\/td>\n<\/tr>\n
Grassland ecosystem assessments: integrating UAV-derived features for aboveground biomass estimation<\/a><\/td>26-01<\/td>Grassland<\/td>biomass<\/td><\/td>\n<\/tr>\n
High-throughput UAV phenotyping for plot-level harvest index estimation in wheat fields<\/a><\/td>26-01<\/td>Wheat<\/td>NDVI, NDRE<\/td><\/td>\n<\/tr>\n
Integration of UAV Multispectral and Meteorological Data to Improve Maize Yield Prediction Accuracy<\/a><\/td>26-01<\/td>Maize<\/td>multispectral, yield<\/td><\/td>\n<\/tr>\n
Leaf wilting as a phenotypic indicator of heat and drought stress in crops: an overview of physiological mechanisms and machine learning applications<\/a><\/td>26-01<\/td>N\/A<\/td>heat stress, water stress<\/td><\/td>\n<\/tr>\n
Maize phenological stage recognition via coordinated UAV and UGV multi-view sensing and deep learning<\/a><\/td>26-01<\/td>Maize<\/td>growth, UGV<\/td><\/td>\n<\/tr>\n
Normalized Difference Vegetation Index Monitoring for Post-Harvest Canopy Recovery of Sweet Orange: Response to an On-Farm Residue-Based Organic Biostimulant<\/a><\/td>26-01<\/td>orange<\/td>NDVI, biostimulant<\/td><\/td>\n<\/tr>\n
SPAD Retrieval of Jujube Canopy Using UAV-Based Multispectral and RGB Features with Genetic Algorithm\u2013Optimized Ensemble Learning<\/a><\/td>26-01<\/td>Jujube<\/td>multispectral<\/td><\/td>\n<\/tr>\n
UAV-based integration of RGB, thermal, and structural features with machine learning for multi-class basal stem rot (BSR) severity detection in oil palm<\/a><\/td>26-01<\/td>Oil Palm<\/td>disease, thermal<\/td><\/td>\n<\/tr>\n
Application of UAVs and Machine Learning Methods for Mapping and Assessing Salinity in Agricultural Fields in Southern Kazakhstan<\/a><\/td>25-12<\/td>N\/A<\/td>soil salinity, multispectral<\/td><\/td>\n<\/tr>\n
Automated Calculation of Rice-Lodging Rates Within a Parcel Area in a Mobile Environment Using Aerial Imagery<\/a><\/td>25-12<\/td>Rice<\/td>lodging<\/td><\/td>\n<\/tr>\n
Machine learning to incorporate root morphology with UAV multispectral imaging for yield and nitrogen prediction in cereals<\/a><\/td>25-12<\/td>cereals<\/td>multispectral, nitrogen, root, yield<\/td><\/td>\n<\/tr>\n
Prediction of soil moisture content using unmanned aerial vehicle technology<\/a><\/td>25-12<\/td>wheat<\/td>multispectral, soil moisture<\/td><\/td>\n<\/tr>\n
Soybean Yield Prediction with High-Throughput Phenotyping Data and Machine Learning<\/a><\/td>25-12<\/td>Soybean<\/td>multispectral, yield<\/td><\/td>\n<\/tr>\n
Spatio-temporal prediction of total and legume dry matter yield using UAV-borne RGB and multispectral images in alfalfa-grass mixtures<\/a><\/td>25-12<\/td>alfalfa<\/td>biomass, multispectral, yield<\/td><\/td>\n<\/tr>\n
Vegetation Indices from UAV Imagery: Emerging Tools for Precision Agriculture and Forest Management<\/a><\/td>25-12<\/td>multiple<\/td>phenotyping, review<\/td><\/td>\n<\/tr>\n
Winter Wheat Yield Estimation Under Different Management Practices Using Multi-Source Data Fusion<\/a><\/td>25-12<\/td>Wheat<\/td>multispectral, thermal, yield<\/td><\/td>\n<\/tr>\n
A Dataset of Aligned RGB and Multispectral UAV Imagery for Semantic Segmentation of Weedy Rice<\/a><\/td>25-11<\/td>Rice<\/td>dataset, weed, multispectral<\/td><\/td>\n<\/tr>\n
A survey on advances and insights of image analysis techniques for phenotyping in maize research: systematic review<\/a><\/td>25-11<\/td>maize<\/td>phenotyping<\/td><\/td>\n<\/tr>\n
An Effective Process to Use Drones for Above-Ground Biomass Estimation in Agroforestry Landscapes<\/a><\/td>25-11<\/td>multiple<\/td>biomass, agroforestry, multispectral<\/td><\/td>\n<\/tr>\n
Enhancing weed detection accuracy under tree crop canopies integrating nano-drone and UAV imaging in a multi-scale 3D point cloud framework<\/a><\/td>25-11<\/td>blueberry<\/td>weed<\/td><\/td>\n<\/tr>\n
Estimation of cotton leaf area index under Verticillium wilt stress using UAVbased multispectral remote sensing<\/a><\/td>25-11<\/td>Cotton<\/td>disease, LAI, multispectral<\/td><\/td>\n<\/tr>\n
Integrating Vegetation Indices and Texture Features from UAV multispectral image for Nondestructive Peanut Aboveground Biomass Estimation <\/a><\/td>25-11<\/td>Peanut<\/td>biomass, multipectral<\/td><\/td>\n<\/tr>\n
Mapping Oak Seedling Health: NDVI Characterization of Powdery Mildew in Valea Iusului Nursery<\/a><\/td>25-11<\/td>oak<\/td>disease, NDVI<\/td><\/td>\n<\/tr>\n
Two-year remote sensing and ground verification: Estimating chlorophyll content in winter wheat using UAV multi-spectral imagery<\/a><\/td>25-11<\/td>Wheat<\/td>chlorophyll, multispectral<\/td><\/td>\n<\/tr>\n
3D\u00a0thermal\u00a0volume mapping to assess the biological and physical characteristics of\u00a0olive\u00a0crops using remote sensing and photogrammetric methods<\/a><\/td>25-10<\/td>Olive<\/td><\/td><\/td>\n<\/tr>\n
Advancing UAV-based\u00a0wheat\u00a0phenology monitoring: A dual-mode framework integrating time-series reconstruction, noise augmentation, and deep learning for robust BBCH estimation<\/a><\/td>25-10<\/td>Wheat<\/td><\/td><\/td>\n<\/tr>\n
Aerial imaging for early assessment of yield potential in maize<\/a><\/td>25-10<\/td>maize<\/td>yield<\/td><\/td>\n<\/tr>\n
Assessing Nitrogen Fertilizer Efficacy In Hardy\u00a0Kiwi\u00a0(Actinidia Arguta): A UAV-Multispectral Approach For Chlorophyll-Based Nutrient Monitoring<\/a><\/td>25-10<\/td>Kiwi<\/td>nitrogen, multispectral<\/td><\/td>\n<\/tr>\n
Automated Crop Measurements with UAVs: Evaluation of an AI-Driven Platform for Counting and Biometric Analysis<\/a><\/td>25-10<\/td>multiple<\/td>counting, height, canopy cover<\/td><\/td>\n<\/tr>\n
Enhancing the estimation of equivalent water thickness in neglected and underutilized taro crops using UAV acquired multispectral thermal image data and index-based image segmentation<\/a><\/td>25-10<\/td>taro<\/td>multispectral, thermal<\/td><\/td>\n<\/tr>\n
Evaluating the Performance of\u00a0Winter Wheat\u00a0Under Late Sowing Using UAV Multispectral Data<\/a><\/td>25-10<\/td>Wheat<\/td>multispectral<\/td><\/td>\n<\/tr>\n
Evaluation of\u00a0winter wheat\u00a0varieties\u2019 responses to nitrogen supply supported by 10-band multispectral aerial imaging: reproducibility over crop seasons and sites<\/a><\/td>25-10<\/td>Wheat<\/td>nitrogen, multispectral<\/td><\/td>\n<\/tr>\n
Feasibility of using Sentinel-2 images to detect decline in kiwifruit orchards<\/a><\/td>25-10<\/td>Kiwi<\/td>disease, multispectral, satellite, soil<\/td><\/td>\n<\/tr>\n
Identification of Cotton Leaf Mite Damage Stages Using UAV Multispectral Images and a Stacked Ensemble Method<\/a><\/td>25-10<\/td>Cotton<\/td>insect, multispectral<\/td><\/td>\n<\/tr>\n
Integrating UAS, Computer Vision and AI for Targeted Management of Invasive Insect Pests in Vineyards<\/a><\/td>25-10<\/td>Grape<\/td>insect, multispectral<\/td><\/td>\n<\/tr>\n
Kiwifruit Health Assessment Model Based on Spectral Analysis and UAV Imagery<\/a><\/td>25-10<\/td>Kiwi<\/td>disease, multispectral, thermal<\/td><\/td>\n<\/tr>\n
Kiwifruit Health Assessment Model Based on Spectral Analysis and UAV Imagery <\/a><\/td>25-10<\/td>Kiwi<\/td>disease<\/td><\/td>\n<\/tr>\n
Late growth stage decision on maize varieties’ drought resilience<\/a><\/td>25-10<\/td>Maize<\/td>water stress, multispectral<\/td><\/td>\n<\/tr>\n
Real-time UAV-based wheat lodging detection via edge-accelerated improved Mask-RT-DETR<\/a><\/td>25-10<\/td>Wheat<\/td>lodging<\/td><\/td>\n<\/tr>\n
Sorghum\u00a0yield prediction using UAV multispectral imaging and stacking ensemble learning in arid regions<\/a><\/td>25-10<\/td>Sorghum<\/td>yield, multispectral<\/td><\/td>\n<\/tr>\n
Technology for\u00a0Blackcurrant\u00a0Plantations Control Using Drones<\/a><\/td>25-10<\/td>Blackcurrant<\/td><\/td><\/td>\n<\/tr>\n
Thermal Infrared Imaging System Usage for Crop Health Assessments<\/a><\/td>25-10<\/td>N\/A<\/td>disease, thermal, water stress<\/td><\/td>\n<\/tr>\n
Comparative Assessment of Remote and Proximal NDVI Sensing for Predicting\u00a0Wheat\u00a0Agronomic Traits<\/a><\/td>25-09<\/td>Wheat<\/td>NDVI<\/td><\/td>\n<\/tr>\n
Improving estimation of days to maturity in field pea using RGB aerial imagery and machine learning<\/a><\/td>25-09<\/td>Pea<\/td>maturity<\/td><\/td>\n<\/tr>\n
Influence of Soil Background Noise on Accuracy of Soil Moisture Content Inversion in Alfalfa Fields Based on UAV Multispectral Data<\/a><\/td>25-09<\/td>Alfalfa<\/td>soil moisture<\/td><\/td>\n<\/tr>\n
Methodological Study on Maize Water Stress Diagnosis Based on UAV Multispectral Data and Multi-Model Comparison<\/a><\/td>25-09<\/td>Maize<\/td>water stress, multispectral<\/td><\/td>\n<\/tr>\n
Modeling and Estimating Soybean Leaf Area Index and Biomass Using Machine Learning Based on Unmanned Aerial Vehicle-Captured Multispectral Images<\/a><\/td>25-09<\/td>Soybean<\/td>biomass, LAI, multispectral<\/td><\/td>\n<\/tr>\n
Prediction of\u00a0Lettuce\u00a0Harvest Date and Evaluation of Data for Yield Estimation Using Artificial Intelligence Analysis of Aerial Drone Images\u00a0(Sept 2025)<\/a><\/td>25-09<\/td>Lettuce<\/td>yield<\/td><\/td>\n<\/tr>\n
Quantifying the Effects of UAV Flight Altitude on the Multispectral Monitoring Accuracy of Soil Moisture and Maize Phenotypic Parameters<\/a><\/td>25-09<\/td>Maize<\/td>image acquisition, multispectral, soil moisture<\/td><\/td>\n<\/tr>\n
Reliable NDVI estimation in\u00a0wheat\u00a0using low-Cost UAV-derived RGB vegetation indices<\/a><\/td>25-09<\/td>Wheat<\/td>NDVI<\/td><\/td>\n<\/tr>\n
WeedSpecies Identification Using Drones with Multispectral Cameras and Machine-Learning<\/a><\/td>25-09<\/td>multiple<\/td>weed, multispectral<\/td><\/td>\n<\/tr>\n
A Remote Sensing Approach for\u00a0Biomass\u00a0Assessment in\u00a0Winter Wheat\u00a0Using the NDVI Second Derivative in Terms of NIR<\/a><\/td>25-08<\/td>Wheat<\/td>biomass, NDVI<\/td><\/td>\n<\/tr>\n
Advances in UAV-based deep learning for cassava disease monitoring and detection: A comprehensive review of models, imaging techniques, and agricultural applications<\/a><\/td>25-08<\/td>Cassava<\/td>disease<\/td><\/td>\n<\/tr>\n
Agronomic Information Extraction from UAV-Based Thermal Photogrammetry Using MATLAB<\/a><\/td>25-08<\/td>N\/A<\/td>thermal, water stress<\/td><\/td>\n<\/tr>\n
Automatic classification of healthy \/\u00a0diseased\u00a0plants using multispectral images<\/a><\/td>25-08<\/td>N\/A<\/td>disease, multispectral<\/td><\/td>\n<\/tr>\n
Development of\u00a0Maize\u00a0Canopy Architecture Indicators Through UAV Multi-Source Data<\/a><\/td>25-08<\/td>Maize<\/td>architecture<\/td><\/td>\n<\/tr>\n
Estimating soil\u00a0salinity\u00a0in\u00a0cotton\u00a0fields using UAV multispectral remote sensing and SSA\u2013SVM optimised machine learning model<\/a><\/td>25-08<\/td>Cotton<\/td>soil salinity, multispectral<\/td><\/td>\n<\/tr>\n
Fraction cover estimation using drone-based multispectral images in six\u00a0olive\u00a0cultivars and different planting systems: a case study in Sicily<\/a><\/td>25-08<\/td>Olive<\/td>architecture, multispectral<\/td><\/td>\n<\/tr>\n
Mapping Soil Moisture Using Drones: Challenges and Opportunities<\/a><\/td>25-08<\/td>N\/A<\/td>multispectral, soil moisture, thermal<\/td><\/td>\n<\/tr>\n
Monitoring Chlorophyll Content of\u00a0Brassica napus\u00a0L. Based on UAV Multispectral and RGB Feature Fusion<\/a><\/td>25-08<\/td>Oilseed rape<\/td>chlorophyll, multispectral<\/td><\/td>\n<\/tr>\n
Monitoring Fertilizer Effects in Hardy Kiwi Using UAV-Based Multispectral Chlorophyll Estimation<\/a><\/td>25-08<\/td>Kiwi<\/td>fertilization, multispectral<\/td><\/td>\n<\/tr>\n
Optimisation of the correlation between normalised difference\u00a0vegetation index\u00a0and\u00a0sugar beet\u00a0yield using multispectral remote sensing data<\/a><\/td>25-08<\/td>Sugar beet<\/td>yield, multispectral<\/td><\/td>\n<\/tr>\n
Precision Agriculture Applications in Tart\u00a0Cherries: Yield Mapping Technologies and Remote Sensing for Water Stress Estimation<\/a><\/td>25-08<\/td>Cherry<\/td>yield, water stress<\/td><\/td>\n<\/tr>\n
Soybean maturity prediction using two-dimensional contour plots from drone-based time series imagery<\/a><\/td>25-08<\/td>Soybean<\/td>maturity, AI<\/td><\/td>\n<\/tr>\n
SOYBEAN\u00a0IRRIGATION MANAGEMENT AND YIELD ESTIMATION BASED ON UAV IN THE MID-SOUTH BASED ON UAV IN THE MID-SOUTH\u00a0(Thesis)<\/a><\/td>25-08<\/td>Soybean<\/td>irrigation, yield<\/td><\/td>\n<\/tr>\n
UAV-based aerial phenotyping to assess key morphophysiological traits and yield in\u00a0soybean<\/a><\/td>25-08<\/td>Soybean<\/td>architecture<\/td><\/td>\n<\/tr>\n
Advances in Precision Farming: a contribute for estimating crop health and water stress by comparing UAV Multispectral and Thermal Imagery<\/a><\/td>25-07<\/td>citrus, orange<\/td>multispectral, thermal, water stress<\/td><\/td>\n<\/tr>\n
Comparison of PhenoCams and drones for lean phenotyping of phenology and senescence of\u00a0wheat\u00a0genotypes in variety testing<\/a><\/td>25-07<\/td>Wheat<\/td>senescence<\/td><\/td>\n<\/tr>\n
Remote-Sensing for\u00a0Herbicide-Free Agriculture : A Bio-Economic and Policy Appraisal<\/a><\/td>25-07<\/td>N\/A<\/td>herbicide<\/td><\/td>\n<\/tr>\n
Phenotyping the hidden half: combining UAV phenotyping and machine learning to predict barley root traits in the field<\/a><\/td>25-06<\/td>Barley<\/td>multispectral, root, thermal<\/td><\/td>\n<\/tr>\n
Cover Crops for Sustainable\u00a0Silage-Maize\u00a0Production: Enhancing Resource Use Efficiency through Modelling and Remote Sensing\u00a0(Thesis)<\/a><\/td>25-05<\/td>Maize<\/td>silage, cover crops<\/td><\/td>\n<\/tr>\n
Analysis of variance and its sources in UAV-based multi-view thermal imaging of wheat plots<\/a><\/td>25-04<\/td>wheat<\/td>thermal<\/td><\/td>\n<\/tr>\n
Assessing Soil Organic Carbon in Semi-Arid Agricultural Soils Using UAVs and Machine Learning: A Pathway to Sustainable Water and Soil Resource Management<\/a><\/td>25-04<\/td>N\/A<\/td>soil, carbon, AI, multispectral<\/td><\/td>\n<\/tr>\n
Drone multispectral imaging captures the effects of soil mineral nitrogen on canopy structure and nitrogen use efficiency in wheat<\/a><\/td>25-04<\/td>Wheat<\/td>nitrogen, soil, multispectral<\/td><\/td>\n<\/tr>\n
Field\u00a0Rice\u00a0Growth Monitoring and Fertilization Management Based on UAV Spectral and Deep Image Feature Fusion<\/a><\/td>25-04<\/td>Rice<\/td>fertilization<\/td><\/td>\n<\/tr>\n
Inversion Model for Total Nitrogen in Rhizosphere Soil of Silage Corn Based on UAV Multispectral Imagery<\/a><\/td>25-04<\/td>Maize<\/td>silage, soil, nitrogen, multispectral<\/td><\/td>\n<\/tr>\n
Monitoring of Soybean Bacterial Blight Disease Using Drone-Mounted Multispectral Imaging: A Case Study in Northeast China<\/a><\/td>25-04<\/td>Soybean<\/td>disease, multispectral<\/td><\/td>\n<\/tr>\n
Monitoring the Progression of Downy Mildew on Vineyards Using Multi-Temporal Unmanned Aerial Vehicle Multispectral Data<\/a><\/td>25-04<\/td>Grape<\/td>disease, multispectral<\/td><\/td>\n<\/tr>\n
UAV Photogrammetry-Based Leaf Area Index for Above-Ground Biomass Estimation in Wetlands<\/a><\/td>25-04<\/td>N\/A<\/td>biomass, LAI, wetlands<\/td><\/td>\n<\/tr>\n
Using Drones to Predict Degradation of Surface Drainage on Agricultural Fields: A Case Study of the Atlantic Dykelands<\/a><\/td>25-04<\/td>Maize<\/td>soil, height, NDVI, multispectral<\/td><\/td>\n<\/tr>\n
A practical guide to UAV-based\u00a0weed\u00a0identification in soybean: Comparing RGB and multispectral sensor performance<\/a><\/td>25-03<\/td>Soybean<\/td>weed, multispectral<\/td><\/td>\n<\/tr>\n
Assessment of the\u00a0Maize\u00a0Crop Water Stress Index (CWSI) Using Drone-Acquired Data Across Different Phenological Stages<\/a><\/td>25-03<\/td>Maize<\/td>water stress<\/td><\/td>\n<\/tr>\n
Characterization of N variations in different organs of\u00a0winter wheat\u00a0and mapping NUE using low altitude UAV-based remote sensing<\/a><\/td>25-03<\/td>Wheat<\/td>nitrogen<\/td><\/td>\n<\/tr>\n
Detecting Changes in\u00a0Soil Fertility\u00a0Properties Using Multispectral UAV Images and Machine Learning in Central Peru<\/a><\/td>25-03<\/td>N\/A<\/td>soil, multispectral<\/td><\/td>\n<\/tr>\n
Drone-based dataset of annotated\u00a0sunflower\u00a0images from Bangladesh<\/a><\/td>25-03<\/td>Sunflower<\/td><\/td><\/td>\n<\/tr>\n
Estimating Stratified Biomass in\u00a0Cotton\u00a0Fields Using UAV Multispectral Remote Sensing and Machine Learning<\/a><\/td>25-03<\/td>Cotton<\/td>biomass, multispectral<\/td><\/td>\n<\/tr>\n
Estimation of\u00a0Silage Maize\u00a0Plant Moisture Content Based on UAV Multispectral Data and Ensemble Learning Methods<\/a><\/td>25-03<\/td>Maize<\/td>silage, moisture, multispectral<\/td><\/td>\n<\/tr>\n
Monitoring\u00a0Maize\u00a0Growth Using a Model for Objective Weight Assignment Based on Multispectral Data From UAV<\/a><\/td>25-03<\/td>Maize<\/td>growth, multispectral<\/td><\/td>\n<\/tr>\n
Multi-scale remote sensing for sustainable\u00a0citrus\u00a0farming: Predicting canopy\u00a0nitrogen\u00a0content using UAV-satellite data fusion<\/a><\/td>25-03<\/td>Citrus<\/td>nitrogen<\/td><\/td>\n<\/tr>\n
Optical leaf area assessment supports\u00a0chlorophyll\u00a0estimation from UAV images<\/a><\/td>25-03<\/td>N\/A<\/td>chlorophyll<\/td><\/td>\n<\/tr>\n
Prediction of\u00a0sugar beet\u00a0yield and quality parameters using Stacked-LSTM model with pre-harvest UAV time series data and meteorological factors<\/a><\/td>25-03<\/td>Sugar beet<\/td>yield, quality<\/td><\/td>\n<\/tr>\n
Vision-based automatic\u00a0fruit\u00a0counting with UAV<\/a><\/td>25-03<\/td>fruit<\/td>counting<\/td><\/td>\n<\/tr>\n
A Novel Approach for\u00a0Maize\u00a0Straw Type Recognition Based on UAV Imagery Integrating Height, Shape, and Spectral Information<\/a><\/td>25-02<\/td>Maize<\/td><\/td><\/td>\n<\/tr>\n
Algorithms\u00a0for Plant Monitoring Applications: A Comprehensive Review<\/a><\/td>25-02<\/td>N\/A<\/td>algorithm<\/td><\/td>\n<\/tr>\n
Assessing canopy temperature responses to\u00a0nitrogen\u00a0fertilisation in South Indian crops using UAV-based\u00a0thermal\u00a0sensing<\/a><\/td>25-02<\/td>N\/A<\/td>nitrogen<\/td><\/td>\n<\/tr>\n
Combining machine learning with UAV derived multispectral aerial images for\u00a0wheat\u00a0yield prediction, in southern Brazil<\/a><\/td>25-02<\/td>Wheat<\/td>yield, multispectral<\/td><\/td>\n<\/tr>\n
Digital technologies for\u00a0water\u00a0use and management in agriculture: Recent applications and future outlook<\/a><\/td>25-02<\/td>N\/A<\/td>water<\/td><\/td>\n<\/tr>\n
Drone and handheld sensors for\u00a0hemp: Evaluating NDVI and NDRE in relation to nitrogen application and crop yield<\/a><\/td>25-02<\/td>Hemp<\/td>NDVI, NDRE, nitrogen<\/td><\/td>\n<\/tr>\n
Early Detection of\u00a0Sugar Beet\u00a0Cercospora Leaf Spot\u00a0Disease\u00a0Using Machine Learning-Assisted Thermal Image Processing Method<\/a><\/td>25-02<\/td>Sugar beet<\/td>disease<\/td><\/td>\n<\/tr>\n
Estimating Canopy Chlorophyll Content of\u00a0Potato\u00a0Using Machine Learning and Remote Sensing<\/a><\/td>25-02<\/td>Potato<\/td>chlorophyll<\/td><\/td>\n<\/tr>\n
Estimation Model of\u00a0Corn\u00a0Leaf Area Index Based on Improved CNN<\/a><\/td>25-02<\/td>Maize<\/td>LAI<\/td><\/td>\n<\/tr>\n
Framework for Smartphone-based Grape Detection and\u00a0Vineyard\u00a0Management using UAV-Trained AI<\/a><\/td>25-02<\/td>Grape<\/td><\/td><\/td>\n<\/tr>\n
Geostatistical Approach and Drone Image Analysis of the Spatial Distribution of\u00a0Bacterial Leaf Blight\u00a0in\u00a0Rice\u00a0Plants<\/a><\/td>25-02<\/td>Rice<\/td>disease<\/td><\/td>\n<\/tr>\n
Identification of optimal phenological periods for summer\u00a0maize\u00a0yield prediction using UAV-based multispectral data<\/a><\/td>25-02<\/td>Maize<\/td>yield, multispectral<\/td><\/td>\n<\/tr>\n
Integration of UAV Multispectral Remote Sensing and Random Forest for Full-Growth Stage Monitoring of\u00a0Wheat\u00a0Dynamics<\/a><\/td>25-02<\/td>Wheat<\/td>growth, multispectral<\/td><\/td>\n<\/tr>\n
Inversion of Leaf Chlorophyll Content in Different Growth Periods of\u00a0Maize\u00a0Based on Multi-Source Data from \u201cSky\u2013Space\u2013Ground\u201d<\/a><\/td>25-02<\/td>Maize<\/td>chlorophyll<\/td><\/td>\n<\/tr>\n
Inversion of Soil Moisture Content in\u00a0Silage Corn\u00a0Root Zones Based on UAV Remote Sensing<\/a><\/td>25-02<\/td>Maize<\/td>soil moisture, silage<\/td><\/td>\n<\/tr>\n
Latent detection of anthracnose on\u00a0strawberry\u00a0crop using multi-spectral imaging<\/a><\/td>25-02<\/td>Strawberry<\/td>disease<\/td><\/td>\n<\/tr>\n
Mapping of insect\u00a0pest\u00a0infestation for precision agriculture: A UAV-based multispectral imaging and deep learning techniques<\/a><\/td>25-02<\/td>N\/A<\/td>insect, multispectral<\/td><\/td>\n<\/tr>\n
Nitrogen\u00a0Fertilizer Effects on\u00a0Hemp\u00a0Biomass Production Detected by Drone-based Spectral Imaging<\/a><\/td>25-02<\/td>Hemp<\/td>nitrogen, biomass<\/td><\/td>\n<\/tr>\n
Precise\u00a0Drought\u00a0Threshold Monitoring in\u00a0Winter Wheat\u00a0Different Growth Periods Using a Multispectral Unmanned Aerial Vehicle<\/a><\/td>25-02<\/td>Wheat<\/td>water stress, multispectral<\/td><\/td>\n<\/tr>\n
Precision Agriculture: Temporal and Spatial Modeling of\u00a0Wheat\u00a0Canopy Spectral Characteristics<\/a><\/td>25-02<\/td>Wheat<\/td>growth<\/td><\/td>\n<\/tr>\n
Remote sensing-based maize growth process parameters revel the\u00a0maize\u00a0yield: a comparison of field-and regional-scale<\/a><\/td>25-02<\/td>Maize<\/td>yield<\/td><\/td>\n<\/tr>\n
UAV-Based Remote Sensing Monitoring of\u00a0Maize\u00a0Growth Using Comprehensive Indices<\/a><\/td>25-02<\/td>Maize<\/td>growth<\/td><\/td>\n<\/tr>\n
UAV-based\u00a0rice\u00a0aboveground\u00a0biomass\u00a0estimation using a random forest model with multi-organ feature selection<\/a><\/td>25-02<\/td>Rice<\/td>biomass<\/td><\/td>\n<\/tr>\n
Using UAV-based multispectral images and CGS-YOLO algorithm to distinguish\u00a0maize\u00a0seeding from weed<\/a><\/td>25-02<\/td>Maize<\/td>weed, multispectral<\/td><\/td>\n<\/tr>\n
A study on optimal input images for\u00a0rice\u00a0yield prediction models using CNN with UAV imagery and its reasoning using explainable AI<\/a><\/td>25-01<\/td>Rice<\/td>yield<\/td><\/td>\n<\/tr>\n
Aerial remote sensing system to control pathogens and\u00a0diseases\u00a0in\u00a0broccoli\u00a0crops with the use of artificial vision<\/a><\/td>25-01<\/td>Broccoli<\/td>disease<\/td><\/td>\n<\/tr>\n
Characterization of\u00a0Hazelnut\u00a0Trees in Open Field Through High-Resolution UAV-Based Imagery and Vegetation Indices<\/a><\/td>25-01<\/td>Hazelnut<\/td><\/td><\/td>\n<\/tr>\n
Enhancing model performance through date fusion in multispectral and RGB image-based field phenotyping of\u00a0wheat\u00a0grain yield<\/a><\/td>25-01<\/td>Wheat<\/td>yield, multispectral<\/td><\/td>\n<\/tr>\n
Estimating Leaf\u00a0Nitrogen\u00a0Accumulation Considering Vertical Heterogeneity Using Multiangular Unmanned Aerial Vehicle Remote Sensing in\u00a0Wheat<\/a><\/td>25-01<\/td>Wheat<\/td>nitrogen<\/td><\/td>\n<\/tr>\n
Improvement of\u00a0Citrus\u00a0Yield Prediction Using UAV Multispectral Images and the CPSO Algorithm<\/a><\/td>25-01<\/td>Citrus<\/td>yield, multispectral<\/td><\/td>\n<\/tr>\n
Mapping Spatial Variability of\u00a0Sugarcane\u00a0Foliar Nitrogen, Phosphorus, Potassium and Chlorophyll Concentrations Using Remote Sensing<\/a><\/td>25-01<\/td>Sugarcane<\/td>nitrogen, chlorophyll<\/td><\/td>\n<\/tr>\n
Monitoring the\u00a0Maize\u00a0Canopy Chlorophyll Content Using Discrete Wavelet Transform Combined with RGB Feature Fusion<\/a><\/td>25-01<\/td>Maize<\/td>chlorophyll<\/td><\/td>\n<\/tr>\n
Monitoring Yield and Quality of\u00a0Forages\u00a0and\u00a0Grassland\u00a0in the View of Precision Agriculture Applications\u2014A Review<\/a><\/td>25-01<\/td>Grassland<\/td>yield, quality<\/td><\/td>\n<\/tr>\n
Plant Height\u00a0Estimation in\u00a0Corn\u00a0Fields Based on Column Space Segmentation Algorithm<\/a><\/td>25-01<\/td>Maize<\/td>height<\/td><\/td>\n<\/tr>\n
Remote Sensing-Assisted Estimation of Water Use in\u00a0Apple\u00a0Orchards with Permanent Living Mulch<\/a><\/td>25-01<\/td>Apple<\/td>irrigation<\/td><\/td>\n<\/tr>\n
UAV Remote Sensing Technology for\u00a0Wheat\u00a0Growth Monitoring in Precision Agriculture: Comparison of Data Quality and Growth Parameter Inversion<\/a><\/td>25-01<\/td>Wheat<\/td>growth, quality<\/td><\/td>\n<\/tr>\n
Unmanned-Aerial-Vehicle-Based Multispectral Monitoring of Nitrogen Content in Canopy Leaves of Processed\u00a0Tomatoes<\/a><\/td>25-01<\/td>Tomato<\/td>nitrogen, multispectral<\/td><\/td>\n<\/tr>\n
Autonomous Yield Estimation System for Small Commercial\u00a0Orchards\u00a0Using UAV and AI<\/a><\/td>24-12<\/td>orchard<\/td>yield<\/td><\/td>\n<\/tr>\n
Coconut\u00a0Palm Tree Counting on Drone Images with Deep Object Detection and Synthetic Training Data<\/a><\/td>24-12<\/td>Coconut<\/td>counting<\/td><\/td>\n<\/tr>\n
Data Preprocessing\u00a0Pipeline for UAS Imagery in Agriculture\u00a0(Thesis)<\/a><\/td>24-12<\/td>N\/A<\/td>image processing<\/td><\/td>\n<\/tr>\n
Dataset of aerial photographs acquired with UAV using a multispectral (green, red and near-infrared) camera for cherry\u00a0tomato\u00a0(Solanum lycopersicum var. cerasiforme) monitoring<\/a><\/td>24-12<\/td>Tomato<\/td>dataset, multispecral<\/td><\/td>\n<\/tr>\n
Drone imagery dataset for early-season\u00a0weed\u00a0classification in maize and tomato crops<\/a><\/td>24-12<\/td>Maize, Tomato<\/td>weed<\/td><\/td>\n<\/tr>\n
Drone-Based Multi-spectral Pipeline for Detecting Abnormal\u00a0Potato\u00a0Strains in the Field<\/a><\/td>24-12<\/td>Potato<\/td>disease<\/td><\/td>\n<\/tr>\n
DSCONV-GAN: a UAV-BASED model for\u00a0Verticillium\u00a0Wilt disease detection in\u00a0Chinese cabbage\u00a0in complex growing environments<\/a><\/td>24-12<\/td>Cabbage<\/td>disease<\/td><\/td>\n<\/tr>\n
EXAMINATION OF RED CLOVER OPTIMUM HARVESTING STATUS IN SEED PRODUCTION WITH UNMANNED AERIAL SYSTEMS (UAS)<\/a><\/td>24-12<\/td>Red clover<\/td><\/td><\/td>\n<\/tr>\n
Field-scale UAV-based multispectral phenomics: Leveraging machine learning, explainable AI, and hybrid feature engineering for enhancements in\u00a0potato\u00a0phenotyping<\/a><\/td>24-12<\/td>Potato<\/td>multispectral<\/td><\/td>\n<\/tr>\n
From images to loci: Applying 3D deep learning to enable multivariate and multi-temporal digital phenotyping and mapping genetics underlying nitrogen use efficiency in\u00a0wheat<\/a><\/td>24-12<\/td>Wheat<\/td>nitrogen<\/td><\/td>\n<\/tr>\n
High throughput phenotyping in\u00a0soybean\u00a0breeding using RGB image vegetation indices based on drone<\/a><\/td>24-12<\/td>Soybean<\/td>breeding<\/td><\/td>\n<\/tr>\n
NDVI\u00a0Estimation Throughout the Whole Growth Period of Multi-Crops Using RGB Images and Deep Learning<\/a><\/td>24-12<\/td>multiple<\/td>NDVI, growth<\/td><\/td>\n<\/tr>\n
Nitrogen Fertilizer Placement and Rate Impacts\u00a0Sunflower\u00a0Seed Yield, Oil and Protein Content\u00a0(Thesis)<\/a><\/td>24-12<\/td>Sunflower<\/td>yield, quality<\/td><\/td>\n<\/tr>\n
Optimizing machine learning models for\u00a0wheat\u00a0yield estimation using a comprehensive UAV dataset<\/a><\/td>24-12<\/td>Wheat<\/td>yield<\/td><\/td>\n<\/tr>\n
Precision assessment of\u00a0rice\u00a0grain\u00a0moisture\u00a0content using UAV multispectral imagery and machine learning<\/a><\/td>24-12<\/td>Rice<\/td>grain moisture, multispectral<\/td><\/td>\n<\/tr>\n
Rice\u00a0Yield Estimation Based on Cumulative Time Series Vegetation Indices of UAV MS and RGB Images<\/a><\/td>24-12<\/td>Rice<\/td>yield<\/td><\/td>\n<\/tr>\n
Suitable vegetation indices for predicting\u00a0sugarcane\u00a0Brix content in the field<\/a><\/td>24-12<\/td>Sugarcane<\/td><\/td><\/td>\n<\/tr>\n
UAV-Multispectral Based\u00a0Maize Lodging\u00a0Stress Assessment with Machine and Deep Learning Methods<\/a><\/td>24-12<\/td>Maize<\/td>lodging, multispectral<\/td><\/td>\n<\/tr>\n
Unmanned aerial vehicle phenotyping of agronomic and physiological traits in\u00a0mungbean<\/a><\/td>24-12<\/td>Mungbean<\/td><\/td><\/td>\n<\/tr>\n
Using Data-Driven Computer Vision Techniques to Improve\u00a0Wheat\u00a0Yield Prediction<\/a><\/td>24-12<\/td>Wheat<\/td>yield<\/td><\/td>\n<\/tr>\n
Will drones in plant protection reduce the use of chemical\u00a0pesticides?<\/a><\/td>24-12<\/td>N\/A<\/td>plant protection<\/td><\/td>\n<\/tr>\n
Accurate Prediction of 327\u00a0Rice\u00a0Variety Growth Period Based on Unmanned Aerial Vehicle Multispectral Remote Sensing<\/a><\/td>24-11<\/td>Rice<\/td>growth, multispectral<\/td><\/td>\n<\/tr>\n
Analysis of Growth Variation in\u00a0Maize\u00a0Leaf Area Index Based on Time-Series Multispectral Images and Random Forest Models<\/a><\/td>24-11<\/td>Maize<\/td>growth, LAI, multispectral<\/td><\/td>\n<\/tr>\n
Artificial Intelligence\u00a0Applied to Support Agronomic Decisions for the Automatic Aerial Analysis Images Captured by UAV: A Systematic Review<\/a><\/td>24-11<\/td>N\/A<\/td>AI<\/td><\/td>\n<\/tr>\n
Assessing Weed Canopy Cover in\u00a0No-Till\u00a0and\u00a0Conventional Tillage\u00a0Plots in Winter Wheat Production Using Drone Data<\/a><\/td>24-11<\/td>Wheat<\/td>weed<\/td><\/td>\n<\/tr>\n
Assessment of UAV-Based Deep Learning for\u00a0Corn\u00a0Crop Analysis in Midwest Brazil<\/a><\/td>24-11<\/td>Maize<\/td><\/td><\/td>\n<\/tr>\n
Automating\u00a0grapevine\u00a0LAI features estimation with UAV imagery and machine learning<\/a><\/td>24-11<\/td>Grape<\/td>LAI<\/td><\/td>\n<\/tr>\n
Combining UAV Multispectral and Thermal Infrared Data for\u00a0Maize\u00a0Growth Parameter Estimation<\/a><\/td>24-11<\/td>Maize<\/td>growth, multispectral, thermal<\/td><\/td>\n<\/tr>\n
Detecting\u00a0Environmental Stress\u00a0In Agriculture Using Satellite Imagery And Spectral Indices<\/a><\/td>24-11<\/td>N\/A<\/td>satellite<\/td><\/td>\n<\/tr>\n
High-throughput phenotypic traits estimation of\u00a0faba bean\u00a0based on machine learning and drone-based multimodal data<\/a><\/td>24-11<\/td>Faba bean<\/td><\/td><\/td>\n<\/tr>\n
Plant-level prediction of\u00a0potato\u00a0yield using machine learning and unmanned aerial vehicle (UAV) multispectral imagery<\/a><\/td>24-11<\/td>Potato<\/td>yield, multispectral<\/td><\/td>\n<\/tr>\n
Prediction of\u00a0Turfgrass\u00a0Quality Using Multispectral UAV Imagery and Ordinal Forests: Validation Using a Fuzzy Approach<\/a><\/td>24-11<\/td>Turfgrass<\/td>quality, multispectral<\/td><\/td>\n<\/tr>\n
Research on\u00a0Soybean\u00a0Seedling Stage Recognition Based on Swin Transformer<\/a><\/td>24-11<\/td>Soybean<\/td><\/td><\/td>\n<\/tr>\n
UAV multispectral remote sensing for agriculture: A comparative study of\u00a0radiometric correction\u00a0methods under varying illumination conditions<\/a><\/td>24-11<\/td>N\/A<\/td>multispectral<\/td><\/td>\n<\/tr>\n
Using Multi-Sensor Data Fusion Techniques and Machine Learning Algorithms for Improving UAV-Based Yield Prediction of\u00a0Oilseed Rape<\/a><\/td>24-11<\/td>Oilseed rape<\/td>yield, multispectral<\/td><\/td>\n<\/tr>\n
A Comprehensive Survey of Drones for\u00a0Turfgrass\u00a0Monitoring<\/a><\/td>24-10<\/td>Turfgrass<\/td><\/td><\/td>\n<\/tr>\n
Advanced Plant Phenotyping: Unmanned Aerial Vehicle Remote Sensing and CimageA\u00a0Software\u00a0Technology for Precision Crop Growth Monitoring<\/a><\/td>24-10<\/td>multiple<\/td>image processing<\/td><\/td>\n<\/tr>\n
An Integrated Approach: Watershed Segmentation with Local Maxima and Minima Algorithms for\u00a0Tree\u00a0Crown Delineation of Mango (Mangifera indica) using UAV Multispectral Imagery<\/a><\/td>24-10<\/td>Mango<\/td>multispectral<\/td><\/td>\n<\/tr>\n
Effects of Variety and Growth Stage on UAV Multispectral Estimation of Plant Nitrogen Content of\u00a0Winter Wheat<\/a><\/td>24-10<\/td>Wheat<\/td>nitrogen, multispectral<\/td><\/td>\n<\/tr>\n
Estimation of forage biomass in\u00a0oat\u00a0(Avena sativa) using agronomic variables through UAV multispectral imaging<\/a><\/td>24-10<\/td>Oat<\/td>biomass, multispectral<\/td><\/td>\n<\/tr>\n
Identification of High-Photosynthetic-Efficiency\u00a0Wheat\u00a0Varieties Based on Multi-Source Remote Sensing from UAVs<\/a><\/td>24-10<\/td>Wheat<\/td>breeding, LAI, multispectral<\/td><\/td>\n<\/tr>\n
Influence of Spatial Scale Effect on UAV Remote Sensing Accuracy in Identifying Chinese\u00a0Cabbage\u00a0(Brassica rapa subsp. Pekinensis) Plants<\/a><\/td>24-10<\/td>Cabbage<\/td>image processing<\/td><\/td>\n<\/tr>\n
Integrating multi-source remote sensing and machine learning for root-zone soil moisture and yield prediction of\u00a0winter oilseed rape\u00a0(Brassica napus L.): A new perspective from the temperature-vegetation index feature space<\/a><\/td>24-10<\/td>Oilseed rape<\/td>yield, soil moisture, multispectral<\/td><\/td>\n<\/tr>\n
Intelligent agriculture: deep learning in UAV-based remote sensing imagery for crop\u00a0diseases\u00a0and\u00a0pests\u00a0detection<\/a><\/td>24-10<\/td>N\/A<\/td>disease, pest<\/td><\/td>\n<\/tr>\n
Manure\u2010biochar\u00a0blends effectively reduce nutrient leaching and increase water retention in a sandy, agricultural soil: Insights from a field experiment<\/a><\/td>24-10<\/td>N\/A<\/td>soil, amendment<\/td><\/td>\n<\/tr>\n
SOIL MOISTURE\u00a0OF CORN CROPS IN A CONSERVATION AGRICULTURE SYSTEMS CAN BE ESTIMATED WITH RGB AND INFRARED IMAGES<\/a><\/td>24-10<\/td>Maize<\/td>soil moisture, multispectral<\/td><\/td>\n<\/tr>\n
Spatial analysis with unoccupied aircraft systems data in\u00a0wheat\u00a0breeding yield trials<\/a><\/td>24-10<\/td>Wheat<\/td>breeding<\/td><\/td>\n<\/tr>\n
UAV-Based Multispectral\u00a0Winter Wheat\u00a0Growth Monitoring with Adaptive Weight Allocation<\/a><\/td>24-10<\/td>Wheat<\/td>growth, multispectral<\/td><\/td>\n<\/tr>\n
Under-Canopy Drone 3D Surveys for\u00a0Wild Fruit\u00a0Hotspot Mapping<\/a><\/td>24-10<\/td>multiple<\/td>AI, image processing<\/td><\/td>\n<\/tr>\n
Use of Unmanned Aerial Vehicles for Monitoring\u00a0Pastures\u00a0and\u00a0Forages\u00a0in Agricultural Sciences: A Systematic Review<\/a><\/td>24-10<\/td>Grassland<\/td><\/td><\/td>\n<\/tr>\n
Using UAV Images and Phenotypic Traits to Predict\u00a0Potato\u00a0Morphology and Yield in Peru<\/a><\/td>24-10<\/td>Potato<\/td>yield<\/td><\/td>\n<\/tr>\n
Water Stress Detection in\u00a0Potato\u00a0Crops Using Multispectral Imaging and Advanced Object Detection Models\u00a0(Thesis)<\/a><\/td>24-10<\/td>Potato<\/td>water stress, multispectral<\/td><\/td>\n<\/tr>\n
An enhanced chlorophyll estimation model with a canopy structural trait in\u00a0maize\u00a0crops: Use of multi-spectral UAV images and machine learning algorithm<\/a><\/td>24-09<\/td>Maize<\/td>chlorophyll, multispectral<\/td><\/td>\n<\/tr>\n
Assessing Plant Pigmentation Impacts: A Novel Approach Integrating UAV and Multispectral Data to Analyze\u00a0Atrazine\u00a0Metabolite Effects from Soil Contamination<\/a><\/td>24-09<\/td>N\/A<\/td>herbicide, multispectral<\/td><\/td>\n<\/tr>\n
Assessing the Impact of UAV Flight Altitudes on the Accuracy of\u00a0Multispectral\u00a0Indices<\/a><\/td>24-09<\/td>Wheat<\/td>image acquisition, multispectral<\/td><\/td>\n<\/tr>\n
Biomass\u00a0prediction of\u00a0Typha latifolia\u00a0on a paludiculture site by combining structural and spectral features from UAS data<\/a><\/td>24-09<\/td>Typha<\/td>biomass, multispectral<\/td><\/td>\n<\/tr>\n
Combining UAV Multi-Source Remote Sensing Data with CPO-SVR to Estimate Seedling Emergence in Breeding\u00a0Sunflowers<\/a><\/td>24-09<\/td>Sunflower<\/td>breeding<\/td><\/td>\n<\/tr>\n
Crop\u00a0water stress\u00a0detection based on UAV remote sensing systems<\/a><\/td>24-09<\/td>N\/A<\/td>water stress, multispectral<\/td><\/td>\n<\/tr>\n
Efficient Physics-informed Transfer Learning to Quantify Biochemical Traits of\u00a0Winter Wheat\u00a0from UAV Multispectral Imagery<\/a><\/td>24-09<\/td>Wheat<\/td>quality, multispectral<\/td><\/td>\n<\/tr>\n
Estimation of\u00a0corn\u00a0nitrogen demand under different irrigation conditions based on UAV multispectral technology<\/a><\/td>24-09<\/td>Maize<\/td>nitrogen, irrigation, multispectral<\/td><\/td>\n<\/tr>\n
Estimation of\u00a0Cotton\u00a0SPAD Based on Multi-Source Feature Fusion and Voting Regression Ensemble Learning in Intercropping Pattern of Cotton and Soybean<\/a><\/td>24-09<\/td>Cotton<\/td>growth, multispectral<\/td><\/td>\n<\/tr>\n
EVALUATING THE APPLICABILITY OF PASSIVE REMOTE SENSING TECHNOLOGY WITH A MULTIROTOR DRONE IN PRECISION\u00a0VITICULTURE<\/a><\/td>24-09<\/td>Grape<\/td>NDVI, multispectral<\/td><\/td>\n<\/tr>\n
Evaluation of\u00a0Sugarcane\u00a0Crop Growth Monitoring Using Vegetation Indices Derived from RGB-Based UAV Images and Machine Learning Models<\/a><\/td>24-09<\/td>Sugarcane<\/td>growth<\/td><\/td>\n<\/tr>\n
From Fields to Pixels: UAV Multispectral and Field-Captured RGB Imaging for High-Throughput\u00a0Wheat\u00a0Spike and Kernel Counting<\/a><\/td>24-09<\/td>Wheat<\/td>counting, multispectral<\/td><\/td>\n<\/tr>\n
Hybrid-AI and Model Ensembling to Exploit UAV-Based RGB Imagery: An Evaluation of\u00a0Sorghum\u00a0Crop\u2019s Nitrogen Content<\/a><\/td>24-09<\/td>Sorghum<\/td>nitrogen, AI<\/td><\/td>\n<\/tr>\n
Identification of\u00a0infesting plants\u00a0in monoculture fields through CNNs from UAV imagery\u00a0(Thesis)<\/a><\/td>24-09<\/td>multiple<\/td>disease, image processing<\/td><\/td>\n<\/tr>\n
Image Analysis Artificial Intelligence Technologies for Plant\u00a0Phenotyping: Current State of the Art<\/a><\/td>24-09<\/td>N\/A<\/td>image processing, AI<\/td><\/td>\n<\/tr>\n
Improving\u00a0Pearl Millet\u00a0Yield Estimation From UAV Imagery in the Semiarid Agroforestry System of Senegal Through Textural Indices and Reflectance Normalization<\/a><\/td>24-09<\/td>Pearl Millet<\/td>yield, multispectral<\/td><\/td>\n<\/tr>\n
In-situ and remote sensing characterization of\u00a0salinity-affected agricultural areas in the Po River Delta\u00a0(Thesis)<\/a><\/td>24-09<\/td>N\/A<\/td>soil salinity, multispectral<\/td><\/td>\n<\/tr>\n
Optimizing UAV-based uncooled thermal cameras in field conditions for precision agriculture<\/a><\/td>24-09<\/td>N\/A<\/td>thermal<\/td><\/td>\n<\/tr>\n
Rice\u00a0yield prediction through drone-derived vegetation indices: A case study in Tamil Nadu, India<\/a><\/td>24-09<\/td>Rice<\/td>yield<\/td><\/td>\n<\/tr>\n
Spatio-temporal mapping of leaf area index in\u00a0rice: spectral indices and multi-scale texture comparison derived from different sensors<\/a><\/td>24-09<\/td>Rice<\/td>LAI, multispectral<\/td><\/td>\n<\/tr>\n
The hawk eye scan:\u00a0Halyomorpha halys\u00a0detection relying on aerial tele photos and neural networks<\/a><\/td>24-09<\/td>orchard<\/td>insect<\/td><\/td>\n<\/tr>\n
Unoccupied-Aerial-Systems-Based Biophysical Analysis of Montmorency\u00a0Cherry\u00a0Orchards: A Comparative Study<\/a><\/td>24-09<\/td>Cherry<\/td>height, LAI<\/td><\/td>\n<\/tr>\n
Use of UAVs\u2019 multispectral images for\u00a0sugar beet\u00a0cultivars discrimination and yield estimation<\/a><\/td>24-09<\/td>Sugar beet<\/td>yield, multispectral<\/td><\/td>\n<\/tr>\n
Accurate Characterization of\u00a0Soil Moisture\u00a0in Wheat Fields with an Improved Drought Index from Unmanned Aerial Vehicle Observations<\/a><\/td>24-08<\/td>Wheat<\/td>soil moisture, multispectral<\/td><\/td>\n<\/tr>\n
Assessing\u00a0grapevine\u00a0water status in a variably irrigated vineyard with NIR\/SWIR hyperspectral imaging from UAV<\/a><\/td>24-08<\/td>Grape<\/td>water stress, multispectral<\/td><\/td>\n<\/tr>\n
Efficient detection of\u00a0cotton\u00a0verticillium wilt by combining satellite time series data and multi-view UAV images<\/a><\/td>24-08<\/td>Cotton<\/td>disease<\/td><\/td>\n<\/tr>\n
Evaluating the utility of combining high resolution thermal, multispectral and 3D imagery from unmanned aerial vehicles to monitor water stress in\u00a0vineyards<\/a><\/td>24-08<\/td>Grape<\/td>water stress, multispectral<\/td><\/td>\n<\/tr>\n
Harnessing Image Processing for Precision Disease Diagnosis in\u00a0Sugar beet\u00a0Agriculture<\/a><\/td>24-08<\/td>Sugar beet<\/td>disease, image processing<\/td><\/td>\n<\/tr>\n
How can aerial imagery and\u00a0vegetation indices\u00a0algorithms monitor the geotagged crop?<\/a><\/td>24-08<\/td>N\/A<\/td><\/td><\/td>\n<\/tr>\n
Monitoring\u00a0Maize\u00a0Canopy Chlorophyll Content throughout the Growth Stages Based on UAV MS and RGB Feature Fusion<\/a><\/td>24-08<\/td>Maize<\/td>chlorophyll, multispectral<\/td><\/td>\n<\/tr>\n
Phenotyping for Effects of Drought Levels in\u00a0Quinoa\u00a0Using Remote Sensing Tools<\/a><\/td>24-08<\/td>Quinoa<\/td>water stress, multispectral<\/td><\/td>\n<\/tr>\n
Research on the Identification of\u00a0Wheat\u00a0Fusarium Head Blight Based on Multispectral Remote Sensing from UAVs<\/a><\/td>24-08<\/td>Wheat<\/td>disease, multispectral<\/td><\/td>\n<\/tr>\n
Semantic Segmentation for\u00a0Plant Leaf Disease\u00a0Classification and Damage Detection: A Deep Learning Approach<\/a><\/td>24-08<\/td>multiple<\/td>disease, AI<\/td><\/td>\n<\/tr>\n
Spinel: a framework for counting\u00a0wheat\u00a0spikes and kernels using UAV and ground-based imaging in breeding fields\u00a0(Thesis)<\/a><\/td>24-08<\/td>Wheat<\/td>counting, image processing<\/td><\/td>\n<\/tr>\n
Utilizing Unmanned Aerial Systems (UAS) and Computer Software to Assess Effects of\u00a0Cover Crops\u00a0on Cash Crop Volumetric Mass and Plant Height<\/a><\/td>24-08<\/td>Cover crops<\/td>biomass, height<\/td><\/td>\n<\/tr>\n
Visible Spectrum Image Analysis For Estimation of Phenological Stages in Irrigated\u00a0Bean\u00a0Cropping<\/a><\/td>24-08<\/td>Bean<\/td><\/td><\/td>\n<\/tr>\n
Estimating Leaf Chlorophyll Content of\u00a0Winter Wheat\u00a0from UAV Multispectral Images Using Machine Learning Algorithms under Different Species, Growth Stages, and Nitrogen Stress Conditions<\/a><\/td>24-07<\/td>Wheat<\/td>chlorophyll, multispectral, nitrogen<\/td><\/td>\n<\/tr>\n
From pixels to plant health: accurate detection of\u00a0banana\u00a0Xanthomonas wilt in complex African landscapes using high-resolution UAV images and deep learning<\/a><\/td>24-07<\/td>Banana<\/td>disease<\/td><\/td>\n<\/tr>\n
High\u2010throughput phenotyping platforms for\u00a0pulse\u00a0crop biofortification<\/a><\/td>24-07<\/td>Pulse crops<\/td>breeding<\/td><\/td>\n<\/tr>\n
In-season Nitrogen Prediction and Evaluation using Airborne Imagery and AI Techniques in Commercial\u00a0Potato\u00a0Production<\/a><\/td>24-07<\/td>Potato<\/td>nitrogen, AI<\/td><\/td>\n<\/tr>\n
Research on Estimating\u00a0Potato\u00a0Fraction Vegetation Coverage (FVC) Based on the Vegetation Index Intersection Method<\/a><\/td>24-07<\/td>Potato<\/td>growth<\/td><\/td>\n<\/tr>\n
Unmanned Aerial Vehicle-Measured Multispectral Vegetation Indices for Predicting LAI, SPAD Chlorophyll, and Yield of\u00a0Maize<\/a><\/td>24-07<\/td>Maize<\/td>yield, LAI, chlorophyll<\/td><\/td>\n<\/tr>\n
A global dataset for assessing\u00a0nitrogen-related plant traits using drone imagery in major field crop species<\/a><\/td>24-06<\/td>multiple<\/td>nitrogen, dataset<\/td><\/td>\n<\/tr>\n
Artificial Intelligence for Airborne\u00a0Phenotyping\u00a0(Thesis)<\/a><\/td>24-06<\/td>Wheat<\/td>disease, AI, multispectral<\/td><\/td>\n<\/tr>\n
Computer vision\u00a0in smart agriculture and precision farming: Techniques and applications<\/a><\/td>24-06<\/td>N\/A<\/td>image processing, AI<\/td><\/td>\n<\/tr>\n
Dataset Generation and Automation to Detect Colony of\u00a0Morning Glory\u00a0at Growing Season Using Alignment of Two Season\u2019s Orthomosaic Images Taken by Drone<\/a><\/td>24-06<\/td>Soybean<\/td>weed<\/td><\/td>\n<\/tr>\n
Efficient Damage Assessment of\u00a0Rice\u00a0Bacterial Leaf Blight Disease in Agricultural Insurance Using Unmanned Aerial Vehicle Image Data<\/a><\/td>24-06<\/td>Rice<\/td>disease<\/td><\/td>\n<\/tr>\n
End-to-End Deep Learning Models for Gap Identification in\u00a0Maize\u00a0Fields<\/a><\/td>24-06<\/td>Maize<\/td>counting, multispectral<\/td><\/td>\n<\/tr>\n
Evapotranspiration\u00a0Measurements in Pasture Classes, Crops, and Native Cerrado Based on Sensors Embodied on Uavs<\/a><\/td>24-06<\/td>Grassland<\/td>water stress, multispectral<\/td><\/td>\n<\/tr>\n
Field Scale Precision: Predicting Grain Yield of Diverse\u00a0Wheat\u00a0Breeding Lines Using High-Throughput UAV Multispectral Imaging<\/a><\/td>24-06<\/td>Wheat<\/td>yield, breeding, multispectral<\/td><\/td>\n<\/tr>\n
High temporal resolution unoccupied aerial systems\u00a0phenotyping\u00a0provides unique information between flight dates<\/a><\/td>24-06<\/td>Maize<\/td>image acquisition<\/td><\/td>\n<\/tr>\n
Optimizing\u00a0winter wheat\u00a0leaf area index estimation across growth stages using UAV data\u00a0(Master thesis)<\/a><\/td>24-06<\/td>Wheat<\/td>LAI<\/td><\/td>\n<\/tr>\n
Prediction of Phenotypic Parameters of\u00a0Sugarcane\u00a0Plants Based on Multispectral Drone Imagery and Machine learning<\/a><\/td>24-06<\/td>Sugarcane<\/td>height, multispectral<\/td><\/td>\n<\/tr>\n
Quantification of species composition in\u00a0grass-clover\u00a0swards using RGB and multispectral UAV imagery and machine learning<\/a><\/td>24-06<\/td>Cover crops<\/td>counting, multispectral<\/td><\/td>\n<\/tr>\n
Wheat\u00a0Yield Prediction Using Machine Learning Method Based on UAV Remote Sensing Data<\/a><\/td>24-06<\/td>Wheat<\/td>yield<\/td><\/td>\n<\/tr>\n
A systematic review on precision agriculture applied to\u00a0sunflowers, the role of hyperspectral imaging\u00a0(May 2024)<\/a><\/td>24-05<\/td>Sunflower<\/td><\/td><\/td>\n<\/tr>\n
EscaYard: Precision viticulture multimodal dataset of\u00a0vineyards\u00a0affected by Esca disease consisting of geotagged smartphone images, phytosanitary status, UAV 3D point clouds and Orthomosaics<\/a><\/td>24-05<\/td>Grape<\/td>disease<\/td><\/td>\n<\/tr>\n
Estimating the Aboveground FreshWeight of\u00a0Sugarcane\u00a0Using Multispectral Images and Light Detection and Ranging (LiDAR)<\/a><\/td>24-05<\/td>Sugarcane<\/td>biomass, multispectral, LIDAR<\/td><\/td>\n<\/tr>\n
Examining the Percent Canopy Cover and Health of\u00a0Winter Wheat\u00a0in No-Till and Conventional Tillage Plots Using a Drone<\/a><\/td>24-05<\/td>Wheat<\/td>disease, canopy cover<\/td><\/td>\n<\/tr>\n
Field-based high-throughput phenotyping enhances phenomic and genomic predictions for grain yield and plant height across years in\u00a0maize<\/a><\/td>24-05<\/td>Maize<\/td>yield, height, breeding<\/td><\/td>\n<\/tr>\n
Method for Monitoring\u00a0Wheat\u00a0Growth Status and Estimating Yield Based on UAV Multispectral Remote Sensing<\/a><\/td>24-05<\/td>Wheat<\/td>growth, yield, multispectral<\/td><\/td>\n<\/tr>\n
Multi-Altitude\u00a0Corn\u00a0Tassel Detection and Counting Based on UAV RGB Imagery and Deep Learning<\/a><\/td>24-05<\/td>Maize<\/td>counting, image acquisition<\/td><\/td>\n<\/tr>\n
Potential of Remote Sensing Technologies for\u00a0Maize\u00a0(Zea mays L.) Breeding\u00a0(Thesis)<\/a><\/td>24-05<\/td>Maize<\/td><\/td><\/td>\n<\/tr>\n
Review of Crop\u00a0Phenotyping\u00a0in Field Plot Experiments Using UAV-Mounted Sensors and Algorithms<\/a><\/td>24-05<\/td>multiple<\/td><\/td><\/td>\n<\/tr>\n
Application of Artificial Intelligence and Sensor Fusion for\u00a0Soil\u00a0Organic Matter Prediction<\/a><\/td>24-04<\/td>N\/A<\/td>soil, NDVI<\/td><\/td>\n<\/tr>\n
Assessing the Severity of VerticilliumWilt in\u00a0Cotton\u00a0Fields and Constructing Pesticide Application Prescription Maps Using Unmanned Aerial Vehicle (UAV) Multispectral Images<\/a><\/td>24-04<\/td>Cotton<\/td>disease, multispectral<\/td><\/td>\n<\/tr>\n
Drone-based imaging sensors, techniques, and applications in plant\u00a0phenotyping\u00a0for crop breeding: A comprehensive review<\/a><\/td>24-04<\/td>multiple<\/td><\/td><\/td>\n<\/tr>\n
Evaluation of Mosaic\u00a0Image Quality\u00a0and Analysis of Influencing Factors Based on UAVs<\/a><\/td>24-04<\/td>N\/A<\/td>image acquisition<\/td><\/td>\n<\/tr>\n
Mapping\u00a0Maize\u00a0Planting Densities Using Unmanned Aerial Vehicles, Multispectral Remote Sensing, and Deep Learning Technology<\/a><\/td>24-04<\/td>Maize<\/td>counting, multispectral<\/td><\/td>\n<\/tr>\n
Sensing and automatic scoring of\u00a0sugar-beet\u00a0fields by using UAV-imagery systems for disease quantification\u00a0(Thesis)<\/a><\/td>24-04<\/td>Sugar beet<\/td>disease<\/td><\/td>\n<\/tr>\n
Adoption of unoccupied aerial systems in agricultural research<\/a><\/td>24-03<\/td>N\/A<\/td><\/td><\/td>\n<\/tr>\n
Advancements in Utilizing Image-Analysis Technology for Crop-Yield\u00a0Estimation<\/a><\/td>24-03<\/td>multiple<\/td>yield, biomass<\/td><\/td>\n<\/tr>\n
Inversion of\u00a0winter wheat\u00a0leaf area index from UAV multispectral images: classical vs. deep learning approaches<\/a><\/td>24-03<\/td>Wheat<\/td>LAI, multispectral<\/td><\/td>\n<\/tr>\n
Yield Prediction Using NDVI Values from GreenSeeker and MicaSense Cameras at Different Stages of\u00a0Winter Wheat\u00a0Phenology<\/a><\/td>24-03<\/td>Wheat<\/td>yield, NDVI<\/td><\/td>\n<\/tr>\n
Application of drone to aid in the evaluation of trials in\u00a0cotton\u00a0cultivation<\/a><\/td>24-02<\/td>Cotton<\/td>disease, pest<\/td><\/td>\n<\/tr>\n
Assessing relationships of\u00a0cover crop\u00a0biomass and nitrogen content to multispectral imagery<\/a><\/td>24-02<\/td>Cover crops<\/td>nitrogen, biomass, multispectral<\/td><\/td>\n<\/tr>\n
Remote Sensing\u00a0for Agriculture in the Era of Industry 5.0\u2014A Survey<\/a><\/td>24-01<\/td>N\/A<\/td><\/td><\/td>\n<\/tr>\n
Automatic Monitoring of\u00a0Maize Seedling\u00a0Growth Using Unmanned Aerial Vehicle-Based RGB Imagery<\/a><\/td>23-07<\/td>Maize<\/td>counting, growth, canopy cover<\/td><\/td>\n<\/tr>\n
Phenomic and genomic prediction of yield on multiple locations in\u00a0winter wheat<\/a><\/td>23-05<\/td>Wheat<\/td>yield<\/td><\/td>\n<\/tr>\n
Application of UAS-Based Remote Sensing in Estimating\u00a0Winter Wheat\u00a0Phenotypic Traits and Yield During the Growing Season<\/a><\/td>23-01<\/td>Wheat<\/td>yield, biomass, height, LAI<\/td><\/td>\n<\/tr>\n
Classification of\u00a0Maize Lodging\u00a0Extents Using Deep Learning Algorithms by UAV-Based RGB and Multispectral Images<\/a><\/td>22-07<\/td>Maize<\/td>lodging, multispectral, AI<\/td><\/td>\n<\/tr>\n
Predicting grain protein concentration in\u00a0winter wheat\u00a0(Triticum aestivum L.) based on unpiloted aerial vehicle multispectral optical remote sensing<\/a><\/td>22-05<\/td>Wheat<\/td>quality<\/td><\/td>\n<\/tr>\n
Crop water stress index computation approaches and their sensitivity to soil water dynamics<\/a><\/td>22-03<\/td>maize<\/td>thermal, water stress<\/td><\/td>\n<\/tr>\n
Global Wheat Head Detection 2021: An Improved Dataset for Benchmarking Wheat Head Detection Methods<\/a><\/td>21-09<\/td>wheat<\/td>counting<\/td><\/td>\n<\/tr>\n
Assessing the Self-Recovery Ability of\u00a0Maize\u00a0after\u00a0Lodging\u00a0Using UAV-LiDAR Data<\/a><\/td>21-06<\/td>Maize<\/td>LIDAR<\/td><\/td>\n<\/tr>\n
Calibration and Image Processing of Aerial Thermal Image for UAV Application in Crop Water Stress Estimation<\/a><\/td>21-05<\/td>N\/A<\/td>thermal, water stress<\/td><\/td>\n<\/tr>\n
Wheat Yield\u00a0Estimation with NDVI Values Using a Proximal Sensing Tool\u00a0(Aug 2020)<\/a><\/td>20-08<\/td>Wheat<\/td>yield, NDVI<\/td><\/td>\n<\/tr>\n
Prediction of the Kiwifruit Decline Syndrome in Diseased Orchards by Remote Sensing<\/a><\/td>20-07<\/td>Kiwi<\/td>disease, multispectral, thermal<\/td><\/td>\n<\/tr>\n
Applications of UAV Thermal Imagery in Precision Agriculture: State of the Art and Future Research Outlook<\/a><\/td>20-05<\/td>multiple<\/td>thermal<\/td><\/td>\n<\/tr>\n
A High-Throughput Model-Assisted Method for Phenotyping Maize Green Leaf Area Index Dynamics Using Unmanned Aerial Vehicle Imagery<\/a><\/td>19-06<\/td>Maize<\/td>LAI, multispectral<\/td><\/td>\n<\/tr>\n
High-Throughput Phenotyping of Plant Height: Comparing Unmanned Aerial Vehicles and Ground LiDAR Estimates<\/a><\/td>17-11<\/td>Wheat<\/td>height, LIDAR<\/td><\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n","protected":false},"excerpt":{"rendered":"

Une s\u00e9lection de publications scientifiques (open access) concernant le ph\u00e9notypage par drone, et des th\u00e8mes associ\u00e9sA selection of scientific publications […]<\/p>\n","protected":false},"author":1,"featured_media":0,"parent":0,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"","meta":{"_monsterinsights_skip_tracking":false,"_monsterinsights_sitenote_active":false,"_monsterinsights_sitenote_note":"","_monsterinsights_sitenote_category":0,"site-sidebar-layout":"default","site-content-layout":"","ast-site-content-layout":"default","site-content-style":"default","site-sidebar-style":"default","ast-global-header-display":"","ast-banner-title-visibility":"","ast-main-header-display":"","ast-hfb-above-header-display":"","ast-hfb-below-header-display":"","ast-hfb-mobile-header-display":"","site-post-title":"","ast-breadcrumbs-content":"","ast-featured-img":"","footer-sml-layout":"","ast-disable-related-posts":"","theme-transparent-header-meta":"","adv-header-id-meta":"","stick-header-meta":"","header-above-stick-meta":"","header-main-stick-meta":"","header-below-stick-meta":"","astra-migrate-meta-layouts":"default","ast-page-background-enabled":"default","ast-page-background-meta":{"desktop":{"background-color":"var(--ast-global-color-4)","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""},"tablet":{"background-color":"","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""},"mobile":{"background-color":"","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""}},"ast-content-background-meta":{"desktop":{"background-color":"var(--ast-global-color-5)","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""},"tablet":{"background-color":"var(--ast-global-color-5)","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""},"mobile":{"background-color":"var(--ast-global-color-5)","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""}},"footnotes":""},"class_list":["post-716","page","type-page","status-publish","hentry"],"_links":{"self":[{"href":"https:\/\/sylphagro.fr\/index.php\/wp-json\/wp\/v2\/pages\/716","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/sylphagro.fr\/index.php\/wp-json\/wp\/v2\/pages"}],"about":[{"href":"https:\/\/sylphagro.fr\/index.php\/wp-json\/wp\/v2\/types\/page"}],"author":[{"embeddable":true,"href":"https:\/\/sylphagro.fr\/index.php\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/sylphagro.fr\/index.php\/wp-json\/wp\/v2\/comments?post=716"}],"version-history":[{"count":5,"href":"https:\/\/sylphagro.fr\/index.php\/wp-json\/wp\/v2\/pages\/716\/revisions"}],"predecessor-version":[{"id":726,"href":"https:\/\/sylphagro.fr\/index.php\/wp-json\/wp\/v2\/pages\/716\/revisions\/726"}],"wp:attachment":[{"href":"https:\/\/sylphagro.fr\/index.php\/wp-json\/wp\/v2\/media?parent=716"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}