Advances in Remote Sensing

Advances in Remote Sensing

ISSN Print: 2169-267X
ISSN Online: 2169-2688
www.scirp.org/journal/ars
E-mail: ars@scirp.org
"Assessment of UAV Based Vegetation Indices for Nitrogen Concentration Estimation in Spring Wheat"
written by Olga S. Walsh, Sanaz Shafian, Juliet M. Marshall, Chad Jackson, Jordan R. McClintick-Chess, Steven M. Blanscet, Kristin Swoboda, Craig Thompson, Kelli M. Belmont, Willow L. Walsh,
published by Advances in Remote Sensing, Vol.7 No.2, 2018
has been cited by the following article(s):
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[1] Wheat Yield and Protein Estimation with Handheld and Unmanned Aerial Vehicle-Mounted Sensors
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[2] Forecasting of Hypoallergenic Wheat Productivity Based on Unmanned Aerial Vehicles Remote Sensing Approach—Case Study
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[3] Effects of the Spatial Resolution of UAV Images on the Prediction and Transferability of Nitrogen Content Model for Winter Wheat
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[4] Drought Damage Assessment for Crop Insurance Based on Vegetation Index by Unmanned Aerial Vehicle (UAV) Multispectral Images of Paddy Fields in Indonesia
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[5] Diagnosis of Nitrogen Content in the Leaves of Apple Tree Using Spectral Imagery
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[6] Unmanned aircraft systems and image analysis in yield estimation and agricultural management
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[7] A kite balloon system for the monitoring of gatherings in open areas
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[8] Drone-Based Vegetation Index Analysis to Estimated Nitrogen Content on The Rice Plantations
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[9] 양파노균병 예찰의 정밀도를 위한 정규식생지수 평가 및 적용
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[10] Estimation of above-ground biomass of winter wheat based on consumer-grade multi-spectral UAV
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[11] Combining canopy reflectance spectrometry and genome-wide prediction to increase response to selection for powdery mildew resistance in cultivated strawberry
Elrahman, L Osorio… - Journal of …, 2022
[12] Crop Monitoring Using Sentinel-2 and UAV Multispectral Imagery: A Comparison Case Study in Northeastern Germany
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[13] The feasibility of using a low-cost near-infrared, sensitive, consumer-grade digital camera mounted on a commercial UAV to assess Bambara groundnut yield
… Journal of Remote …, 2022
[14] Comparative Sensitivity of Vegetation Indices Measured via Proximal and Aerial Sensors for Assessing N Status and Predicting Grain Yield in Rice Cropping Systems
Remote Sensing, 2022
[15] Wheat yield and protein estimation with handheld‐and UAV‐based reflectance measurements
Agrosystems …, 2022
[16] 분광 영상을 이용한 사과나무 잎의 질소 영양 상태 진단
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[17] Review on unmanned aerial vehicles, remote sensors, imagery processing, and their applications in agriculture
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[18] TELEDETECCIÓN DE ESTRÉS EN CULTIVOS DE CAÑA A TRAVÉS DE IMÁGENES MULTIESPECTRALES
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[19] Aerial Robotics in Agriculture: Parafoils, Blimps, Aerostats, and Kites
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[20] Optimizing Top Dressing Nitrogen Fertilization Using VENμS and Sentinel-2 L1 Data
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[21] Potato crop stress identification in aerial images using deep learning‐based object detection
Agronomy …, 2021
[22] Why Science Needs Story
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[23] Non-destructive method of Biomass and Nitrogen (N) level estimation in Stevia rebaudiana using various multispectral indices
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[24] Potato Crop Stress Identification in Aerial Images using Deep Learning-based Object Detection
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[25] Random forest regression results in accurate assessment of potato nitrogen status based on multispectral data from different platforms and the critical concentration …
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[26] The Application of an Unmanned Aerial System and Machine Learning Techniques for Red Clover-Grass Mixture Yield Esti-mation Under Variety Performance Trials
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[27] Development and Evaluation of Unmanned Aerial Vehicles for High Throughput Phenotyping of Field-based Wheat Trials
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[28] Determination of nitrogen and chlorophyll content in two varieties of winter wheat plants means of ground and airborne spectrometry
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[29] Estimation of Leaf Nitrogen Content of Wheat Based on UAV Image at Filling Stage
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[30] Analysis of remote sensing based vegetation indices (VIs) for unmanned aerial system (UAS)
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[31] Estimation of Winter Wheat Nitrogen Content, Biomass and Yield using UAV Images in South Korea
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[32] Intra-Field Canopy Nitrogen Retrieval from Unmanned Aerial Vehicle Imagery for Wheat and Corn Fields
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[33] An Approach for Route Optimization in Applications of Precision Agriculture Using UAVs
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[34] 基于无人机图像的小麦灌浆期叶片氮含量估算
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[35] Analysis of Remote Sensing based Vegetation Indices (VIs) for Unmanned Aerial System (UAS): A Review
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[36] Management and characterization of abiotic stress via PhénoField®, a high-throughput field phenotyping platform
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[37] Management and characterization of abiotic stress via PhénoField®, a high-throughput field phenotyping platform.
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[38] UAVs technology for the development of GUI based application for precision agriculture and environmental research
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[39] Evaluación del aprovechamiento de la fertilización nitrogenada en la caña de azúcar mediante la fotogrametría multiespectral
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[40] Análisis de imágenes multiespectrales de sembrados de caña de azúcar adquiridas con vehículos aéreos no tripulados
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[41] Česká technologická platforma pro zemědělství (ČTPZ)
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