Review: Cost-Effective Unmanned Aerial Vehicle (UAV) Platform for Field Plant Breeding Application
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Dong-Wook Kim | Chang Woo Lee | Jaeyoung Kim | Hak-Jin Kim | Kyung-Hwan Kim | GyuJin Jang | Ju-Kyung Yu | Yoonha Kim | Yong Suk Chung | Jaeyoung Kim | Hak-Jin Kim | Dong-Wook Kim | Ju-Kyung Yu | Hak-Jin Kim | Yoonha Kim | Kyung-Hwan Kim | Chang Woo Lee | Jaeyoung Kim | Dong-Wook Kim | Kyung-Hwan Kim | G. Jang | Ju-Kyung Yu | Yoonha Kim | Chang Woo Lee
[1] A. Huete,et al. A review of vegetation indices , 1995 .
[2] Raul Morais,et al. Hyperspectral Imaging: A Review on UAV-Based Sensors, Data Processing and Applications for Agriculture and Forestry , 2017, Remote. Sens..
[3] Francisco Herrera,et al. Detection of Fir Trees (Abies sibirica) Damaged by the Bark Beetle in Unmanned Aerial Vehicle Images with Deep Learning , 2019, Remote. Sens..
[4] Chunhua Zhang,et al. The application of small unmanned aerial systems for precision agriculture: a review , 2012, Precision Agriculture.
[5] A. Gitelson,et al. Quantitative estimation of chlorophyll-a using reflectance spectra : experiments with autumn chestnut and maple leaves , 1994 .
[6] Francisco Javier Mesas-Carrascosa,et al. Positional Quality Assessment of Orthophotos Obtained from Sensors Onboard Multi-Rotor UAV Platforms , 2014, Sensors.
[7] T. Winkel,et al. The Photochemical Reflectance Index (PRI) as a water-stress index , 2002 .
[8] F. Baret,et al. Estimates of plant density of wheat crops at emergence from very low altitude UAV imagery. , 2017 .
[9] Juan José Fuldain González,et al. NDVI Identification and Survey of a Roman Road in the Northern Spanish Province of Álava , 2019, Remote. Sens..
[10] Byun-Woo Lee,et al. Estimation of rice growth and nitrogen nutrition status using color digital camera image analysis , 2013 .
[11] J. A. Schell,et al. Monitoring the Vernal Advancement and Retrogradation (Green Wave Effect) of Natural Vegetation. [Great Plains Corridor] , 1973 .
[12] Soe W. Myint,et al. A Simplified Empirical Line Method of Radiometric Calibration for Small Unmanned Aircraft Systems-Based Remote Sensing , 2015, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.
[13] Lei Tian,et al. Development of a low-cost agricultural remote sensing system based on an autonomous unmanned aerial vehicle (UAV) , 2011 .
[14] F. Baret,et al. High-Throughput Phenotyping of Plant Height: Comparing Unmanned Aerial Vehicles and Ground LiDAR Estimates , 2017, Front. Plant Sci..
[15] P. Zarco-Tejada,et al. REMOTE SENSING OF VEGETATION FROM UAV PLATFORMS USING LIGHTWEIGHT MULTISPECTRAL AND THERMAL IMAGING SENSORS , 2009 .
[16] Arko Lucieer,et al. Sensor Correction of a 6-Band Multispectral Imaging Sensor for UAV Remote Sensing , 2012, Remote. Sens..
[17] L. G. Santesteban,et al. High-resolution UAV-based thermal imaging to estimate the instantaneous and seasonal variability of plant water status within a vineyard , 2017 .
[18] Roland Siegwart,et al. AgriColMap: Aerial-Ground Collaborative 3D Mapping for Precision Farming , 2018, IEEE Robotics and Automation Letters.
[19] C. Tucker. Red and photographic infrared linear combinations for monitoring vegetation , 1979 .
[20] M. Meron,et al. Applying high-resolution visible-channel aerial imaging of crop canopy to precision irrigation management , 2018, Agricultural Water Management.
[21] Didier Tanré,et al. Atmospherically resistant vegetation index (ARVI) for EOS-MODIS , 1992, IEEE Trans. Geosci. Remote. Sens..
[22] Adel Hafiane,et al. Deep leaning approach with colorimetric spaces and vegetation indices for vine diseases detection in UAV images , 2018, Comput. Electron. Agric..
[23] L. D. Miller,et al. Remote mapping of standing crop biomass for estimation of the productivity of the shortgrass prairie, Pawnee National Grasslands, Colorado , 1972 .
[24] P. J. Zarco-Tejada,et al. Detection of downy mildew of opium poppy using high-resolution multi-spectral and thermal imagery acquired with an unmanned aerial vehicle , 2014, Precision Agriculture.
[25] David Reiser,et al. 3-D Imaging Systems for Agricultural Applications—A Review , 2016, Sensors.
[26] Jun Ni,et al. Development of an Unmanned Aerial Vehicle-Borne Crop-Growth Monitoring System , 2017, Sensors.
[27] H. Jones. Application of Thermal Imaging and Infrared Sensing in Plant Physiology and Ecophysiology , 2004 .
[28] Pablo J. Zarco-Tejada,et al. Thermal and Narrowband Multispectral Remote Sensing for Vegetation Monitoring From an Unmanned Aerial Vehicle , 2009, IEEE Transactions on Geoscience and Remote Sensing.
[29] Piero Toscano,et al. Intercomparison of UAV, Aircraft and Satellite Remote Sensing Platforms for Precision Viticulture , 2015, Remote. Sens..
[30] F. López-Granados,et al. Multi-temporal mapping of the vegetation fraction in early-season wheat fields using images from UAV , 2014 .
[31] A. Gitelson,et al. Novel algorithms for remote estimation of vegetation fraction , 2002 .
[32] Ryan R. Jensen,et al. Small-Scale Unmanned Aerial Vehicles in Environmental Remote Sensing: Challenges and Opportunities , 2011 .
[33] A. Gitelson,et al. Use of a green channel in remote sensing of global vegetation from EOS- MODIS , 1996 .
[34] Qin Zhang,et al. A Review of Imaging Techniques for Plant Phenotyping , 2014, Sensors.
[35] Yu Qin,et al. Improving estimates of fractional vegetation cover based on UAV in alpine grassland on the Qinghai–Tibetan Plateau , 2016 .
[36] Edward Jones,et al. A survey of image processing techniques for plant extraction and segmentation in the field , 2016, Comput. Electron. Agric..
[37] Takashi Watanabe,et al. Development of Geospatial Model for Preparing Distribution of Rare Plant Resources Using Uav/Drone , 2018, Indian Journal of Pharmaceutical Education and Research.
[38] G. Rondeaux,et al. Optimization of soil-adjusted vegetation indices , 1996 .
[39] Simon Bennertz,et al. Combining UAV-based plant height from crop surface models, visible, and near infrared vegetation indices for biomass monitoring in barley , 2015, Int. J. Appl. Earth Obs. Geoinformation.
[40] Georgi Hristov,et al. A Review of the Characteristics of Modern Unmanned Aerial Vehicles , 2016 .
[41] A. Huete,et al. A Modified Soil Adjusted Vegetation Index , 1994 .
[42] A. Huete. A soil-adjusted vegetation index (SAVI) , 1988 .
[43] Yong Suk Chung,et al. New Parameters for Seedling Vigor Developed via Phenomics , 2019, Applied Sciences.
[44] Michael Pflanz,et al. Monitoring Agronomic Parameters of Winter Wheat Crops with Low-Cost UAV Imagery , 2016, Remote. Sens..
[45] P. Zarco-Tejada,et al. Unmanned aerial platform-based multi-spectral imaging for field phenotyping of maize , 2015, Plant Methods.
[46] Michael J. Starek,et al. Unmanned aircraft system-derived crop height and normalized difference vegetation index metrics for sorghum yield and aphid stress assessment , 2017 .
[47] Yuri A. Gritz,et al. Relationships between leaf chlorophyll content and spectral reflectance and algorithms for non-destructive chlorophyll assessment in higher plant leaves. , 2003, Journal of plant physiology.
[48] Hao Yang,et al. Unmanned Aerial Vehicle Remote Sensing for Field-Based Crop Phenotyping: Current Status and Perspectives , 2017, Front. Plant Sci..
[49] Zhiguo Ding,et al. Joint Transmission Scheduling and Power Allocation in Non-Orthogonal Multiple Access , 2019, IEEE Transactions on Communications.
[50] Hamlyn G. Jones,et al. Use of infrared thermometry for estimation of stomatal conductance as a possible aid to irrigation scheduling , 1999 .
[51] Luis A. Ruiz,et al. CONFIGURATION AND SPECIFICATIONS OF AN UNMANNED AERIAL VEHICLE FOR PRECISION AGRICULTURE , 2016 .
[52] Eija Honkavaara,et al. Quantitative Remote Sensing at Ultra-High Resolution with UAV Spectroscopy: A Review of Sensor Technology, Measurement Procedures, and Data Correction Workflows , 2018, Remote. Sens..
[53] J. F. Ortega,et al. Estimation of leaf area index in onion (Allium cepa L.) using an unmanned aerial vehicle , 2013 .
[54] Hong Sun,et al. Sensors for measuring plant phenotyping: A review , 2018 .
[55] Elizabeth Pattey,et al. Retrieval of leaf area index from top-of-canopy digital photography over agricultural crops , 2010 .
[56] José Emilio Meroño de Larriva,et al. Drift Correction of Lightweight Microbolometer Thermal Sensors On-Board Unmanned Aerial Vehicles , 2018, Remote. Sens..
[57] Yafit Cohen,et al. Evaluating water stress in irrigated olives: correlation of soil water status, tree water status, and thermal imagery , 2009, Irrigation Science.
[58] Di Wu,et al. Potential of hyperspectral imaging combined with chemometric analysis for assessing and visualising tenderness distribution in raw farmed salmon fillets , 2014 .
[59] Min Jiang,et al. Estimates of rice lodging using indices derived from UAV visible and thermal infrared images , 2018 .
[60] J. Alex Thomasson,et al. Measurement and Calibration of Plant-Height from Fixed-Wing UAV Images , 2018, Sensors.
[61] Matthew Bardeen,et al. Detection and Segmentation of Vine Canopy in Ultra-High Spatial Resolution RGB Imagery Obtained from Unmanned Aerial Vehicle (UAV): A Case Study in a Commercial Vineyard , 2017, Remote. Sens..
[62] Lei Tian,et al. Method for automatic georeferencing aerial remote sensing (RS) images from an unmanned aerial vehicle (UAV) platform , 2011 .
[63] J. Flexas,et al. UAVs challenge to assess water stress for sustainable agriculture , 2015 .
[64] M. Vincini,et al. A broad-band leaf chlorophyll vegetation index at the canopy scale , 2008, Precision Agriculture.
[65] José Manuel Peñá-Barragán,et al. Assessing Optimal Flight Parameters for Generating Accurate Multispectral Orthomosaicks by UAV to Support Site-Specific Crop Management , 2015, Remote. Sens..
[66] Nicholas E. Kolarik,et al. Describing seasonal differences in tree crown delineation using multispectral UAS data and structure from motion , 2019, Remote Sensing Letters.
[67] Simon Bennertz,et al. Estimating Biomass of Barley Using Crop Surface Models (CSMs) Derived from UAV-Based RGB Imaging , 2014, Remote. Sens..
[68] J. F. Ortega,et al. Onion biomass monitoring using UAV-based RGB imaging , 2018, Precision Agriculture.
[69] Piero Toscano,et al. UAV-based high-throughput phenotyping to discriminate barley vigour with visible and near-infrared vegetation indices , 2018 .
[70] Naser El-Sheimy,et al. A New Vegetation Segmentation Approach for Cropped Fields Based on Threshold Detection from Hue Histograms , 2018, Sensors.
[71] Nithya Rajan,et al. Unmanned Aerial Vehicles for High-Throughput Phenotyping and Agronomic Research , 2016, PloS one.
[72] S. Idso,et al. Normalizing the stress-degree-day parameter for environmental variability☆ , 1981 .
[73] William R. Raun,et al. By‐Plant Prediction of Corn Forage Biomass and Nitrogen Uptake at Various Growth Stages Using Remote Sensing and Plant Height , 2007 .
[74] Andreas Burkart,et al. Deploying four optical UAV-based sensors over grassland: challenges and limitations , 2015 .
[75] Marco Dubbini,et al. Evaluating Multispectral Images and Vegetation Indices for Precision Farming Applications from UAV Images , 2015, Remote. Sens..
[76] Yi Lin,et al. LiDAR: An important tool for next-generation phenotyping technology of high potential for plant phenomics? , 2015, Comput. Electron. Agric..
[77] Juliane Bendig,et al. UAV-based Imaging for Multi-Temporal, very high Resolution Crop Surface Models to monitor Crop Growth Variability , 2013 .
[78] M. Meron,et al. Evaluation of different approaches for estimating and mapping crop water status in cotton with thermal imaging , 2010, Precision Agriculture.
[79] G. Meyer,et al. Verification of color vegetation indices for automated crop imaging applications , 2008 .
[80] Rajeev K Varshney,et al. Crop Breeding Chips and Genotyping Platforms: Progress, Challenges, and Perspectives. , 2017, Molecular plant.
[81] J. Roujean,et al. Estimating PAR absorbed by vegetation from bidirectional reflectance measurements , 1995 .
[82] N. Broge,et al. Deriving green crop area index and canopy chlorophyll density of winter wheat from spectral reflectance data , 2002 .
[83] Oluibukun Gbenga Ajayi,et al. Generation of accurate digital elevation models from UAV acquired low percentage overlapping images , 2017 .
[84] Timothy L. Hawthorne,et al. Using Object-Oriented Classification for Coastal Management in the East Central Coast of Florida: A Quantitative Comparison between UAV, Satellite, and Aerial Data , 2019, Drones.
[85] Weixing Cao,et al. Predicting grain yield in rice using multi-temporal vegetation indices from UAV-based multispectral and digital imagery , 2017 .
[86] Wei Guo,et al. High-Throughput Phenotyping of Sorghum Plant Height Using an Unmanned Aerial Vehicle and Its Application to Genomic Prediction Modeling , 2017, Front. Plant Sci..
[87] Hiroshi Nakano,et al. Assessing Correlation of High-Resolution NDVI with Fertilizer Application Level and Yield of Rice and Wheat Crops Using Small UAVs , 2019, Remote. Sens..
[88] Jaime Lloret,et al. Urban Lawn Monitoring in Smart City Environments , 2018, J. Sensors.
[89] Scott C. Chapman,et al. Estimation of plant height using a high throughput phenotyping platform based on unmanned aerial vehicle and self-calibration: Example for sorghum breeding , 2018 .
[90] C. Field,et al. A narrow-waveband spectral index that tracks diurnal changes in photosynthetic efficiency , 1992 .
[91] Jon Nielsen,et al. Are vegetation indices derived from consumer-grade cameras mounted on UAVs sufficiently reliable for assessing experimental plots? , 2016 .
[92] J. Baluja,et al. Assessment of vineyard water status variability by thermal and multispectral imagery using an unmanned aerial vehicle (UAV) , 2012, Irrigation Science.
[93] S. F. D. Gennaro,et al. Unmanned Aerial Vehicle (UAV)-based remote sensing to monitor grapevine leaf stripe disease within a vineyard affected by esca complex , 2016 .
[94] C. Daughtry,et al. Remote Sensing Leaf Chlorophyll Content Using a Visible Band Index , 2011 .
[95] F. Nex,et al. UAV for 3D mapping applications: a review , 2014 .
[96] Dong-Wook Kim,et al. Modeling and Testing of Growth Status for Chinese Cabbage and White Radish with UAV-Based RGB Imagery , 2018, Remote. Sens..
[97] Andrew D. Richardson,et al. An evaluation of noninvasive methods to estimate foliar chlorophyll content , 2002 .
[98] Arko Lucieer,et al. Assessing the Accuracy of Georeferenced Point Clouds Produced via Multi-View Stereopsis from Unmanned Aerial Vehicle (UAV) Imagery , 2012, Remote. Sens..
[99] I Leinonen,et al. Estimating stomatal conductance with thermal imagery. , 2006, Plant, cell & environment.