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References
|
Barrero O, Perdomo SA (2018). RGB and multispectral UAV image fusion for Gramineae weed detection in rice fields. Precision Agriculture pp. 1-14. |
|
|
Bayala J, Zougmoré R, Dayamba SD, Olivier A (2017). Editorial for the Thematic Series in Agriculture and Food Security: Climate-Smart Agriculture Technologies in West Africa: learning from the ground AR4D experiences. Agriculture and Food Security 6(1):40. |
|
|
Berni J, Zarco-Tejada PJ, Sepulcre-Cantó G, Fereres E, Villalobos F (2009a). Mapping canopy conductance and CWSI in olive orchards using high resolution thermal remote sensing imagery. Remote Sensing of Environment 113(11):2380-2388. |
|
|
Berni J, Zarco-Tejada PJ, Suarez L, Fereres E (2009b). Thermal and Narrowband Multispectral Remote Sensing for Vegetation Monitoring From an Unmanned Aerial Vehicle. IEEE Transactions on Geoscience and Remote Sensing 47(3):722-738. |
|
|
Burkart A, Hecht VL, Kraska T, Rascher U (2018). Phenological analysis of unmanned aerial vehicle based time series of barley imagery with high temporal resolution. Precision Agriculture 19(1):134-146. |
|
|
Domingues FM, Bartholomeus H, Van Apeldoorn D, Suomalainen J, Kooistra L (2017). Intercomparison of Unmanned Aerial Vehicle and Ground-Based Narrow Band Spectrometers Applied to Crop Trait Monitoring in Organic Potato Production. Sensors 17(6):1428. |
|
|
Frankelius P, Norman C, Johansen K (2017). Agricultural Innovation and the Role of Institutions: Lessons from the Game of Drones. Journal of Agricultural and Environmental Ethics pp. 1-27. |
|
|
Handique BK, Khan AQ, Goswami C, Prashnani M, Gupta C, Raju PLN (2017). Crop Discrimination Using Multispectral Sensor Onboard Unmanned Aerial Vehicle. Proceedings of the National Academy of Sciences, India Section A: Physical Sciences 87(4):713-719. |
|
|
Hirsch JE (2005). An index to quantify an individual's scientific research output. Proceedings of the National Academy of Sciences of the United States of America 102(46):16569-16572. |
|
|
Honkavaara E, Saari H, Kaivosoja J, Pölönen I, Hakala T, Litkey P, Pesonen L (2013). Processing and Assessment of Spectrometric, Stereoscopic Imagery Collected Using a Lightweight UAV Spectral Camera for Precision Agriculture. Remote Sensing 5(10):5006-5039. |
|
|
Hovhannisyan T, Efendyan P, Vardanyan M (2018). Creation of a digital model of fields with application of DJI phantom 3 drone and the opportunities of its utilization in agriculture. Annals of Agrarian Science 16(2):177-180. |
|
|
Huang Y, Hoffmann WC, Lan Y, Wu W, Fritz BK (2009). Development of a Spray System for an Unmanned Aerial Vehicle Platform. Applied Engineering in Agriculture 25(6):803-809. |
|
|
Hunt ER, Hively WD, Fujikawa S, Linden D, Daughtry CS, McCarty G, McCarty GW (2010). Acquisition of NIR-Green-Blue Digital Photographs from Unmanned Aircraft for Crop Monitoring. Remote Sensing 2(1):290-305. |
|
|
Hunt ER, Horneck DA, Spinelli CB , Turner RW, Bruce AE, Gadler DJ, Brungardt JJ, Hamm PB (2018). Monitoring nitrogen status of potatoes using small unmanned aerial vehicles. Precision Agriculture 19(2):314-333. |
|
|
Huuskonen J, Oksanen T (2018). Soil sampling with drones and augmented reality in precision agriculture. Computers and Electronics in Agriculture 154:25-35. |
|
|
Kamilaris A, Prenafeta-Boldú FX (2018). Deep learning in agriculture: A survey. Computers and Electronics in Agriculture 147:70-90. |
|
|
Khan Z, Rahimi-Eichi V, Haefele S, Garnett T, Miklavcic SJ (2018). Estimation of vegetation indices for high-throughput phenotyping of wheat using aerial imaging. Plant Methods 14(1):20. |
|
|
Krienke B, Ferguson RB, Schlemmer M, Holland K, Marx D, Eskridge K (2017). Using an unmanned aerial vehicle to evaluate nitrogen variability and height effect with an active crop canopy sensor. Precision Agriculture 18(6):900-915. |
|
|
Laliberte AS, Goforth MA, Steele CM, Rango A, Goforth MA, Rango A (2011). Multispectral Remote Sensing from Unmanned Aircraft: Image Processing Workflows and Applications for Rangeland Environments. Remote Sensing 3(11):2529-2551. |
|
|
Lammoglia SK, Brun F, Quemar T, Moeys J, Barriuso E, Gabrielle B, Mamy L (2018). Modelling pesticides leaching in cropping systems: Effect of uncertainties in climate, agricultural practices, soil and pesticide properties. Environmental Modelling and Software. |
|
|
Lary DJ, Zewdie GK, Liu X, Wu D, Levetin E, Allee RJ, Aurin D (2018). Machine Learning Applications for Earth Observation. In Earth Observation Open Science and Innovation (pp. 165-218). Cham: Springer International Publishing. |
|
|
Lelong C, Burger P, Jubelin G, Roux B, Labbé S, Baret F, Baret F (2008). Assessment of Unmanned Aerial Vehicles Imagery for Quantitative Monitoring of Wheat Crop in Small Plots. Sensors 8(5):3557-3585. |
|
|
Maru A, Berne D, Beer JD, Ballantyne P, Pesce V, Kalyesubula S, Chaves J (2018). Digital and Data-Driven Agriculture: Harnessing the Power of Data for Smallholders. F1000Research 7(525). |
|
|
MartiÌnez J, Egea G, Agüera J, PeÌrez-Ruiz M (2017). A cost- effective canopy temperature measurement system for precision agriculture: a case study on sugar beet. Precision Agriculture 18(1):95-110. |
|
|
Mazur M, Wisniewski A, McMillan J (2016). Clarity from above. PwC Global Report on the Commercial Applications of Drone Technology. PwC Poland P 4. |
|
|
Mulla DJ (2013). Twenty five years of remote sensing in precision agriculture: Key advances and remaining knowledge gaps. Biosystems Engineering 114(4):358-371. |
|
|
Oldeland J, Große SA, Naftal L, Strohbach BJ (2017). The Potential of UAV Derived Image Features for Discriminating Savannah Tree Species. In The Roles of Remote Sensing in Nature Conservation. Cham: Springer International Publishing. |
|
|
Pallottino F, Biocca M, Nardi P, Figorilli S, Menesatti P, Costa C (2018). Science mapping approach to analyze the research evolution on precision agriculture: world, EU and Italian situation. Precision Agriculture pp. 1-16. |
|
|
Perianes-Rodriguez A, Waltman L, Van Eck NJ (2016). Constructing bibliometric networks: A comparison between full and fractional counting. Journal of Informetrics 10(4):1178-1195. |
|
|
Rupnik R, Kukar M, VraÄar P, Košir D, Pevec D, Bosnić Z (2018). AgroDSS: A decision support system for agriculture and farming. Computers and Electronics in Agriculture. |
|
|
Schut AGT, Traore PCS, Blaes X, De By RA (2018). Assessing yield and fertilizer response in heterogeneous smallholder fields with UAVs and satellites. Field Crops Research 221:98-107. |
|
|
Singh R, Singh GS (2017). Traditional agriculture: a climate-smart approach for sustainable food production. Energy, Ecology and Environment 2(5):296-316. |
|
|
Thomas S, Kuska MT, Bohnenkamp D, Brugger A, Alisaac E, Wahabzada M (2018). Benefits of hyperspectral imaging for plant disease detection and plant protection: a technical perspective. Journal of Plant Diseases and Protection 125(1):5-20. |
|
|
Thomson R (2018). Web of Science. Thomson Reuters. |
|
|
Turner D, Lucieer A, Watson C, Turner D, Lucieer A, Watson C (2012). An Automated Technique for Generating Georectified Mosaics from Ultra-High Resolution Unmanned Aerial Vehicle (UAV) Imagery, Based on Structure from Motion (SfM) Point Clouds. Remote Sensing 4(5):1392-1410. |
|
|
Van Eck NJ, Waltman L (2013). VOSviewer manual. Leiden, Netherlands: Univeristeit Leiden |
|
|
West JS, Canning GGM, Perryman SA, King K (2017). Novel Technologies for the detection of Fusarium head blight disea- se and airborne inoculum. Tropical Plant Pathology 42(3):203-209. |
|
|
Zarco-Tejada PJ, González-Dugo V, Berni JAJ (2012). Fluorescence, temperature and narrow-band indices acquired from a UAV platform for water stress detection using a micro-hyperspectral imager and a thermal camera. Remote Sensing of Environment 117:322-337. |
|
|
Zarco-Tejada PJ, Guillén-Climent ML, Hernández-Clemente R, Catalina A, González MR, Martín P (2013a). Estimating leaf carotenoid content in vineyards using high resolution hyperspectral imagery acquired from an unmanned aerial vehicle (UAV). Agricultural and Forest Meteorology 171:281-294. |
|
|
Zarco-Tejada PJ, Morales A, Testi L, Villalobos FJ (2013b). Spatio-temporal patterns of chlorophyll fluorescence and physiological and structural indices acquired from hyperspectral imagery as compared with carbon fluxes measured with eddy covariance. Remote Sensing of Environment 133:102-115. |
|
|
Zhang C, Kovacs JM (2012). The application of small unmanned aerial systems for precision agriculture: a review. Precision Agriculture 13(6):693-712. |
|
|
Zhu H, Lan Y, Wu W, Hoffmann WC, Huang Y, Xue X, Fritz B (2010). Development of a PWM Precision Spraying Controller for Unmanned Aerial Vehicles. Journal of Bionic Engineering 7(3):276-283. |
|
|
Zilberman D, Goetz R, Garrido A (2018). Climate Smart Agriculture Building Resilience to Climate Change (Springer). Roma, Italia. |
|
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