UAV applications in Agriculture 4.0

Lucas Rios do Amaral, Cristiano Zerbato, Rodrigo Greggio de Freitas, Marcelo Rodrigues Barbosa Júnior, Isabela Ordine Pires da Silva Simões

Resumo


Unmanned Aerial Vehicles (UAVs) have potentially significant application in agriculture and, with the emergence of the digital farming era and Agriculture 4.0, this platform has become increasingly important. UAV imagery may improve or even replace routine data surveys, as well as optimize phytosanitary product application. High-spatial resolution imagery makes UAVs attractive for several applications where traditional satellite sensing is still unsuitable. With the significant recent development of data science techniques, UAVs have a prominent position in assisting farmers for more efficient decision-making and automating agricultural processes. Thus, this work addresses the main agricultural applications of UAVs into five major topics: topographic survey, physiological assessments, biophysical assessments, monitoring of biological targets, and spraying of phytosanitary products and application of bio inputs.

Palavras-chave


Drones; RPA; Digital Agriculture; Precision Agriculture; Remote Sensing

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Referências


ABDULRIDHA, J. et al. Detection of target spot and bacterial spot diseases in tomato using UAV-based and benchtop-based hyperspectral imaging techniques. Precision Agriculture, v. 21, p. 955-978, 2020. https://doi.org/10.1007/s11119-019-09703-4.

ABDULRIDHA, J.; BATUMAN, O.; AMPATZIDIS, Y. UAV-based remote sensing technique to detect citrus canker disease utilizing hyperspectral imaging and machine learning. Remote Sensing, v. 11, 2019. https://doi.org/10.3390/rs11111373.

ALVES, T.M. et al. Optimizing band selection for spectral detection of Aphis glycines Matsumura in soybean. Pest Management Science, v. 75, p. 942-949, 2019. https://doi.org/10.1002/ps.5198.

ARGENTO, F. et al. Site-specific nitrogen management in winter wheat supported by low-altitude remote sensing and soil data. Precision Agriculture, 2020. https://doi.org/10.1007/s11119-020-09733-3.

BAH, M.D; HAFIANE, A.; CANALS, R. Deep learning with unsupervised data labeling for weed detection in line crops in UAV images. Remote Sensing, v. 10, 2018. https://doi.org/10.3390/rs10111690.

BENDIG, J.; BOLTEN, A.; BARETH. G. UAV-based Imaging for Multi-Temporal, very high Resolution Crop Surface Models to monitor Crop. Photogrammetrie - Fernerkundung - Geoinformation, p. 551-562, 2013. https://doi.org/10.1127/1432-8364/2013/0200.

BENDIG, J. et al. Estimating biomass of barley using crop surface models (CSMs) derived from UAV-based RGB imaging. Remote Sensing, v. 6, p. 10395-10412, 2014. https://doi.org/10.3390/rs61110395.

BENDIG, J. et al. Combining UAV-based plant height from crop surface models, visible, and near infrared vegetation indices for biomass monitoring in barley. International Journal of Applied Earth Observation and Geoinformation, v. 39, p. 79-87, 2015. https://doi.org/10.1016/j.jag.2015.02.012.

BERRY, P.M. et al. A calibrated model of wheat lodging compared with field measurements. Agricultural and Forest Meteorology, v. 119, p. 167-180, 2003. https://doi.org/10.1016/S0168-1923(03)00139-4.

BLONQUIST, J.M.; NORMAN, J.M.; BUGBEE, B. Agricultural and Forest Meteorology Automated measurement of canopy stomatal conductance based on infrared temperature. Agricultural and Forest Meteorology, v. 149, p. 1931-1945, 2009. https://doi.org/10.1016/j.agrformet.2009.06.021.

BROCKS, S. Estimating Barley Biomass with Crop Surface Models from Oblique RGB Imagery. Remote Sensing, v. 10, 2018. https://doi.org/10.3390/rs10020268.

BUCHAILLOT, M.L. et al. Evaluating maize genotype performance under low nitrogen conditions using RGB UAV phenotyping techniques. Sensors, v. 19, 2019. https://doi.org/10.3390/s19081815.

CÂNDIDO, B.M. et al. High-resolution monitoring of diffuse (sheet or interrill) erosion using structure-from-motion. Geoderma, v. 375, p. 114477, 2020. https://doi.org/10.1016/j.geoderma.2020.114477.

CAO, F. et al. Fast detection of sclerotinia sclerotiorum on oilseed rape leaves using low-altitude remote sensing technology. Sensors, v. 18, 2018. https://doi.org/10.3390/s18124464.

CASTALDI, F. et al. Assessing the potential of images from unmanned aerial vehicles (UAV) to support herbicide patch spraying in maize. Precision Agriculture, v. 18, p. 76-94, 2017. https://doi.org/10.1007/s11119-016-9468-3.

CHANG, A. et al. Crop height monitoring with digital imagery from Unmanned Aerial System (UAS). Computers and Electronics in Agriculture, v. 141, p. 232-237, 2017. https://doi.org/10.1016/j.compag.2017.07.008.

CHEN, P. et al. Droplet deposition and control of planthoppers of different nozzles in two-stage rice with a quadrotor unmanned aerial vehicle. Agronomy, v. 10, 2020. https://doi.org/10.3390/agronomy10020303.

CHEN, S. et al. Effect of droplet size parameters on droplet deposition and drift of aerial spraying by using plant protection UAV. Agronomy, v. 10, 2020. https://doi.org/10.3390/agronomy10020195.

CHEN, S.W. et al. Counting Apples and Oranges with Deep Learning: A Data-Driven Approach. IEEE Robotics and Automation Letter, v. 2, p. 781-788, 2017. https://doi.org/10.1109/LRA.2017.2651944.

CHÉNÉ, Y. et al. On the use of depth camera for 3D phenotyping of entire plants. Computers and Electronics in Agriculture, v. 82, p. 122-127, 2012. https://doi.org/10.1016/j.compag.2011.12.007.

COSTA, L.; NUNES, L.; AMPATZIDIS, Y. A new visible band index (vNDVI) for estimating NDVI values on RGB images utilizing genetic algorithms. Computers and Electronics in Agriculture, v. 172, p. 105334, 2020. https://doi.org/10.1016/j.compag.2020.105334.

CRUSIOL, L.G.T. et al. UAV-based thermal imaging in the assessment of water status of soybean plants. International Journal of Remote Sensing, v. 41, p. 3243-3265, 2020. https://doi.org/10.1080/01431161.2019.1673914.

DÍAZ-VARELA, R.A. et al. High-Resolution Airborne UAV Imagery to Assess Olive Tree Crown Parameters Using 3D Photo Reconstruction: Application in Breeding Trials. Remote Sensing, v. 7, 2015. https://doi.org/10.3390/rs70404213.

DILLEN, M. et al. Biomass and Bioenergy Productivity , stand dynamics and the selection effect in a mixed willow clone short rotation coppice plantation. Biomass and Bioenergy, v. 87, p. 46-54, 2016. https://doi.org/10.1016/j.biombioe.2016.02.013.

DU, M., NOGUCHI, N. Monitoring of Wheat Growth Status and Mapping of Wheat Yield ’ s within-Field Spatial Variations Using Color Images Acquired from UAV-camera System. Remote Sensing, v. 9, 2017. https://doi.org/10.3390/rs9030289.

FAIÇAL, B.S. et al. An adaptive approach for UAV-based pesticide spraying in dynamic environments. Computers and Electronics in Agriculture, v. 138, p. 210-223, 2017. https://doi.org/10.1016/j.compag.2017.04.011.

FAO. Global report on food crises, 2018.

FORLANI, G. et al. Quality assessment of DSMs produced from UAV flights geo-referenced with on-board RTK positioning. Remote Sensing, v. 10, 2018. https://doi.org/10.3390/rs10020311.

FU, Y. et al. Winter wheat biomass estimation based on spectral indices, band depth analysis and partial least squares regression using hyperspectral measurements. Computers and Electronics in Agriculture, v. 100, p. 51-59, 2014. https://doi.org/10.1016/j.compag.2013.10.010.

GAO, J. et al. Fusion of pixel and object-based features for weed mapping using unmanned aerial vehicle imagery. International Journal of Applied Earth Observation and Geoinformation, v. 67, p. 43-53, 2018. https://doi.org/10.1016/j.jag.2017.12.012.

GAO, P. et al. Article development of a recognition system for spraying areas from unmanned aerial vehicles using a machine learning approach. Sensors, v. 19, 2019. https://doi.org/10.3390/s19020313.

GARCÍA-MARTÍNEZ, H. et al. Corn Grain Yield Estimation from Vegetation Indices, Canopy Cover, Plant Density, and a Neural Network Using Multispectral and RGB Images Acquired with Unmanned Aerial Vehicles. Agriculture, v. 10, p. 277, 2020. https://doi.org/10.3390/agriculture10070277.

GEIPEL, J.,; LINK, J.; CLAUPEIN, W. Combined spectral and spatial modeling of corn yield based on aerial images and crop surface models acquired with an unmanned aircraft system. Remote Sensing, v. 6, p. 10335-10355, 2014. https://doi.org/10.3390/rs61110335.

GIANNETTI, F. et al. Assessment of UAV photogrammetric DTM-independent variables for modelling and mapping forest structural indices in mixed temperate forests. Ecological Indicators, v. 117, p. 106513, 2020. https://doi.org/10.1016/j.ecolind.2020.106513.

GILES, D.K.; BILLING, R.C. Deployment and performance of a uav for crop spraying. Chemical Engineering Transactions, v. 44, p. 307-312, 2015. https://doi.org/10.3303/CET1544052.

GRÜNER, E.; ASTOR, T.; WACHENDORF, M. Biomass Prediction of Heterogeneous Temperate Grasslands Using an SfM Approach Based on UAV Imaging. Agronomy, v. 9, 2019. https://doi.org/10.3390/agronomy9020054.

GRÜNER, E.; WACHENDORF, M.; ASTOR, T. The potential of UAV-borne spectral and textural information for predicting aboveground biomass and N fixation in legume-grass mixtures. PLoS One, v. 15, p. 1-21, 2020. https://doi.org/10.1371/journal.pone.0234703.

GUO, S. et al. Distribution characteristics on droplet deposition of wind field vortex formed by multi-rotor UAV. PLoS One, v. 14, 2019. https://doi.org/10.1371/journal.pone.0220024.

HAMUDA, E.; GLAVIN, M.; JONES, E. A survey of image processing techniques for plant extraction and segmentation in the field. Computers and Electronics in Agriculture, v. 125, p. 184-199, 2016. https://doi.org/10.1016/j.compag.2016.04.024.

HASSAN, M.A. et al. Accuracy assessment of plant height using an unmanned aerial vehicle for quantitative genomic analysis in bread wheat. Plant Methods, v. 15, p. 1-12, 2019a. https://doi.org/10.1186/s13007-019-0419-7.

HASSAN, M.A. et al. A rapid monitoring of NDVI across the wheat growth cycle for grain yield prediction using a multi-spectral UAV platform. Plant Science, v. 282, p. 95-103, 2019b. https://doi.org/10.1016/j.plantsci.2018.10.022.

HASSLER, S.C.; BAYSAL-GUREL, F. Unmanned aircraft system (UAS) technology and applications in agriculture. Agronomy, v. 9, 2019. https://doi.org/10.3390/agronomy9100618.

HE, X.K.; BONDS, J.; HERBST, A., Langenakens, J. Recent development of unmanned aerial vehicle for plant protection in East Asia. International Journal of Agricultural and Biological Engineering, v. 10, p. 18-30, 2017. https://doi.org/10.3965/j.ijabe.20171003.3248.

HEINEMANN, S. et al. Land surface temperature retrieval for agricultural areas using a novel UAV platform equipped with a thermal infrared and multi-spectral sensor. Remote Sensing, v. 12, 2020. https://doi.org/10.3390/rs12071075.

HOLMAN, F.H. et al. High throughput field phenotyping of wheat plant height and growth rate in field plot trials using UAV based remote sensing. Remote Sensing, v. 8, 2016. https://doi.org/10.3390/rs8121031.

HOU, C. et al. Optimization of control parameters of droplet density in citrus trees using UAVs and the Taguchi method. International Journal of Agricultural and Biological Engineering, v.12, p. 1-9, 2019. https://doi.org/10.25165/j.ijabe.20191204.4139.

HU, P. et al. Estimation of plant height using a high throughput phenotyping platform based on unmanned aerial vehicle and self-calibration: Example for sorghum breeding. European Journal of Agronomy, v. 95, p. 24-32, 2018. https://doi.org/10.1016/j.eja.2018.02.004.

HUANG, H. et al. Detection of helminthosporium leaf blotch disease based on UAV imagery. Applied Sciences, v. 9, p. 1-14, 2019. https://doi.org/10.3390/app9030558.

HUNT, E.R. et al. A visible band index for remote sensing leaf chlorophyll content at the Canopy scale. International Journal of Applied Earth Observation and Geoinformation, v. 21, p. 103-112, 2012. https://doi.org/10.1016/j.jag.2012.07.020.

HUNTER, J.E. et al. Coverage and drift potential associated with nozzle and speed selection for herbicide applications using an unmanned aerial sprayer. Weed Technology, v. 34, p. 235-240, 2020. https://doi.org/10.1017/wet.2019.101.

IHUOMA, S.O.; MADRAMOOTOO, C.A. Crop reflectance indices for mapping water stress in greenhouse grown bell pepper. Agricultural Water Management, v. 219, p. 49-58, 2019. https://doi.org/10.1016/j.agwat.2019.04.001.

IVIĆ, S.; ANDREJČUK, A.; DRUŽETA, S. Autonomous control for multi-agent non-uniform spraying. Applied Soft Computing, v. 80, p. 742-760, 2019. https://doi.org/10.1016/j.asoc.2019.05.001.

JAMES, M.R.; ROBSON, S. Mitigating systematic error in topographic models derived from UAV and ground-based image networks. Earth Surface Processes and Landforms, v. 39, p. 1413-1420, 2014. https://doi.org/10.1002/esp.3609.

JIMENEZ-BERNI, J.A. High Throughput Determination of Plant Height, Ground Cover, and Aboveground Biomass in Wheat with LiDAR. Frontiers in Plant Science, v. 9, p. 1-18, 2018. https://doi.org/10.3389/fpls.2018.00237.

KAMILARIS, A.; PRENAFETA-BOLDÚ, F.X. Deep learning in agriculture: A survey. Computers and Electronics in Agriculture, v. 147, p. 70-90, 2018. https://doi.org/10.1016/j.compag.2018.02.016.

KANDYLAKIS, Z. et al. Water Stress Estimation in Vineyards from Aerial SWIR and multi-spectral UAV data. Remote Sensing, v. 12, 2020. https://doi.org/10.3390/RS12152499.

KING, B.A. et al. Thermal Crop Water Stress Index Base Line Temperatures for Sugarbeet in Arid Western U.S. Agricultural of Water Management, v. 243, p. 106459, 2021. https://doi.org/10.1016/j.agwat.2020.106459.

LAN, Y.; CHEN, S. Current status and trends of plant protection UAV and its spraying technology in China. International Journal of Precision Agricultural Aviation, v. 1, p. 1-9, 2018. https://doi.org/10.33440/j.ijpaa.20180101.0002.

LAZCANO, C.; DOMÍNGUEZ, J. The use of vermicompost in sustainable agriculture: impact on plant growth. Soil Nutrients, p. 1-23, 2011.

LI, J. et al. Vertical distribution and vortex structure of rotor wind field under the influence of rice canopy. Computers and Electronics in Agriculture, v. 159, p. 140-146, 2019. https://doi.org/10.1016/j.compag.2019.02.027.

LI, J.Y. et al. Design and test of operation parameters for rice air broadcasting by unmanned aerial vehicle. International Journal of Agricultural and Biological Engineering, v. 9, p. 24-32, 2016. https://doi.org/10.3965/j.ijabe.20160905.2248.

LIAKOS, K.G. et al. Machine learning in agriculture: A review. Sensors, v. 18, p. 1-30, 2018. https://doi.org/10.3390/s18082674.

LIAN, Q. et al. Design of precision variable-rate spray system for unmanned aerial vehicle using automatic control method. International Journal of Agricultural and Biological Engineering, v. 12, p. 29-35, 2019. https://doi.org/10.25165/j.ijabe.20191202.4701.

LIAO, J. et al. Optimization of variables for maximizing efficacy and efficiency in aerial spray application to cotton using UASs. International Journal of Agricultural and Biological Engineering, v. 12, p. 10-17, 2019. https://doi.org/10.25165/j.ijabe.20191202.4288.

LIU, H.; ZHU, H.; WANG, P. Quantitative modelling for leaf nitrogen content of winter wheat using UAV-based hyperspectral data. International Journal of Remote Sensing, v. 38, p. 2117-2134, 2017. https://doi.org/10.1080/01431161.2016.1253899.

LÓPEZ-GRANADOS, F. et al. Object-based early monitoring of a grass weed in a grass crop using high resolution UAV imagery. Agronomy for Sustainable Development, v. 36, 2016. https://doi.org/10.1007/s13593-016-0405-7.

LOU, Z. et al. Effect of unmanned aerial vehicle flight height on droplet distribution, drift and control of cotton aphids and spider mites. Agronomy, v. 8, 2018. https://doi.org/10.3390/agronomy8090187.

MADEC, S. et al. High-throughput phenotyping of plant height: Comparing unmanned aerial vehicles and ground lidar estimates. Frontiers in Plant Science, v. 8, p. 1-14, 2017. https://doi.org/10.3389/fpls.2017.02002.

MAIMAITIJIANG, M. et al. Soybean yield prediction from UAV using multimodal data fusion and deep learning. Remote Sensing of Environment, v. 237, p. 111599, 2020. https://doi.org/10.1016/j.rse.2019.111599.

MARINO, S.; ALVINO, A. Detection of Spatial and Temporal Variability of Vegetation Indices. Agronomy, v. 9, 2019. https://doi.org/10.3390/agronomy9050226.

MARTINEZ-GUANTER, J. et al. Spray and economics assessment of a UAV-based ultra-low-volume application in olive and citrus orchards. Precision Agriculture, v. 21, p. 226-243, 2020. https://doi.org/10.1007/s11119-019-09665-7.

MEINEN, B.U.; ROBINSON, D.T. Where did the soil go? Quantifying one year of soil erosion on a steep tile-drained agricultural field. Science of The Total Environment, v. 729, p. 138320, 2020a. https://doi.org/10.1016/j.scitotenv.2020.138320.

MEINEN, B.U.; ROBINSON, D.T. Mapping erosion and deposition in an agricultural landscape: Optimization of UAV image acquisition schemes for SfM-MVS. Remote Sensing of Environment, v. 239, p. 111666. 2020b. https://doi.org/10.1016/j.rse.2020.111666.

MENG, Y. et al. Experimental evaluation of UAV spraying for peach trees of different shapes: Effects of operational parameters on droplet distribution. Computers and Electronics in Agriculture, v. 170, p. 1-12. 2020. https://doi.org/10.1016/j.compag.2020.105282.

MILLAN, V.E.G.; RANKINE, C.; SANCHEZ-AZOFEIFA, G.A. Crop loss evaluation using digital surface models from unmanned aerial vehicles data. Remote Sensing, v. 12. 2020. https://doi.org/10.3390/rs12060981.

MODICA, G. et al. Monitoring the vegetation vigor in heterogeneous citrus and olive orchards. A multiscale object-based approach to extract trees’ crowns from UAV multi-spectral imagery. Computers and Electronics in Agriculture, v. 175, p. 105500. 2020. https://doi.org/10.1016/j.compag.2020.105500.

NÄSI, R. et al. Estimating biomass and nitrogen amount of barley and grass using UAV and aircraft based spectral and photogrammetric 3D features. Remote Sensing, v. 10, p. 1-32. 2018. https://doi.org/10.3390/rs10071082.

NIJLAND, W. et al. Monitoring plant condition and phenology using infrared sensitive consumer grade digital cameras. Agricultural and Forest Meteorology, v. 184, p. 98-106. 2014. https://doi.org/10.1016/j.agrformet.2013.09.007.

OLSON, D. et al. Relationship of drone-based vegetation indices with corn and sugarbeet yields. Agronomy Journal, v. 111, p. 2545-2557. 2019. https://doi.org/10.2134/agronj2019.04.0260.

OTA, T. et al. Aboveground Biomass Estimation Using Structure from Motion Approach with Aerial Photographs in a Seasonal Tropical Forest. Forests, v. 6, p. 3882-3898. 2015. https://doi.org/10.3390/f6113882.

PAN, Z. et al. Droplet distribution and control against citrus Leafminer with Uav spraying. International Journal of Robotics and Automation, v. 32, p. 299-307. 2017. https://doi.org/10.2316/Journal.206.2017.3.206-4980.

PIJL, A. et al. TERRA: Terrain Extraction from elevation Rasters through Repetitive Anisotropic filtering. International Journal of Applied Earth Observation and Geoinformation, v. 84, p. 101977. 2020. https://doi.org/10.1016/j.jag.2019.101977.

RAHNEMOONFAR, M.; SHEPPARD, C. Deep Count: Fruit Counting Based on Deep Simulated Learning. Sensors, v. 17, p. 1-12. 2017. https://doi.org/10.3390/s17040905.

REN, H. et al. An improved ground control point configuration for digital surface model construction in a coal waste dump using an unmanned aerial vehicle system. Remote Sensing, v. 12. 2020. https://doi.org/10.3390/rs12101623.

ROOSJEN, P.P.J. et al. Deep learning for automated detection of Drosophila suzukii: potential for UAV-based monitoring. Pest Management Science, v. 76, p. 2994-3002. 2020. https://doi.org/10.1002/ps.5845.

SARBOLANDI, H.; LEFLOCH, D.; KOLB, A. Kinect range sensing: Structured-light versus Time-of-Flight Kinect. Computer Vision and Image Understanding, v. 139, p. 1-20, 2015. https://doi.org/10.1016/j.cviu.2015.05.006.

SCHIRRMANN, M. et al. Monitoring Agronomic Parameters of Winter Wheat Crops with Low-Cost UAV Imagery. Remote Sensing, v. 8, 2016. https://doi.org/10.3390/rs8090706.

SHANI, G.; VIT, A. Comparing RGB-D Sensors for Close Range Outdoor. Sensors, v. 18, 2018. https://doi.org/10.3390/s18124413.

SHE, Y. et al. Applications of small UAV systems for tree and nursery inventory management. In Proceeding of the 12th International Conference on Precision Agriculture (ICPA), Sacramento, CA, USA, 20-23 July 2014.

SHE, Y. et al. Applications of High-Resolution Imaging for Open Field Container Nursery Counting. Remote Sensing, v. 10, 2018. https://doi.org/10.3390/rs10122018.

SHENDRYK, Y. et al. Fine-scale prediction of biomass and leaf nitrogen content in sugarcane using UAV LiDAR and multi-spectral imaging. International Journal of Applied Earth Observation and Geoinformation, v. 92, p. 102177. 2020. https://doi.org/10.1016/j.jag.2020.102177.

SIEBRING, J. et al. Object-based image analysis applied to low altitude aerial imagery for potato plant trait retrieval and pathogen detection. Sensors, v. 19, 2019. https://doi.org/10.3390/s19245477.

SULIK, J.J.; LONG, D.S. Spectral considerations for modeling yield of canola. Remote Sensing of Environment, v. 184, p. 161-174. 2016. https://doi.org/10.1016/j.rse.2016.06.016.

TANG, Y. et al. Effects of operation height and tree shape on droplet deposition in citrus trees using an unmanned aerial vehicle. Computers and Electronics in Agriculture, v. 148, p. 1-7, 2018. https://doi.org/10.1016/j.compag.2018.02.026.

TESKE, A.L. et al. Optimised dispensing of predatory mites by multirotor UAVs in wind: A distribution pattern modelling approach for precision pest management. Biosystems Engineering.,v. 187, p. 226-238, 2019. https://doi.org/10.1016/j.biosystemseng.2019.09.009.

THOMPSON, L.J.; PUNTEL, L.A. Transforming unmanned aerial vehicle (UAV) and multi-spectral sensor into a practical decision support system for precision nitrogen management in corn. Remote Sensing, v. 12, 2020. https://doi.org/10.3390/rs12101597.

TILLY, N.; AASEN, H.; BARETH, G. Fusion of Plant Height and Vegetation Indices for the Estimation of Barley Biomass. Remote Sensing, v. 7, p. 11449-11480, 2015. https://doi.org/10.3390/rs70911449.

TOMAŠTÍK, J. et al. UAV RTK/PPK method-An optimal solution for mapping inaccessible forested areas? Remote Sensing, v. 11, 2019. https://doi.org/10.3390/RS11060721.

TSOUROS, D.C. et al. A review on UAV-based applications for precision agriculture. Information, v. 10, 2019. https://doi.org/10.3390/info10110349.

VERGARA-DIAZ, O. et al. Grain yield losses in yellow-rusted durum wheat estimated using digital and conventional parameters under field conditions. The Crop Journal, v. 3, p. 200-210, 2015. https://doi.org/10.1016/j.cj.2015.03.003.

VILJANEN, N.; HONKAVAARA, E. A Novel Machine Learning Method for Estimating Biomass of Grass Swards Using a Photogrammetric Canopy Height Model, Images and Vegetation Indices Captured by a Drone. Agriculture, v. 8, 2018. https://doi.org/10.3390/agriculture8050070.

WALTER, J. et al. Photogrammetry for the estimation of wheat biomass and harvest index. Field Crops Research, v. 216, p. 165-174, 2018. https://doi.org/10.1016/j.fcr.2017.11.024.

WANG, G. et al. Comparison of spray deposition, control efficacy on wheat aphids and working efficiency in the wheat field of the unmanned aerial vehicle with boom sprayer and two conventional knapsack sprayers. Applied Sciences, v. 9, p. 1-17, 2019. https://doi.org/10.3390/app9020218.

WANG, L. et al. Vision-based adaptive variable rate spraying approach for unmanned aerial vehicles. International Journal of Agricultural and Biological Engineering, v. 12, p. 18-26, 2019. https://doi.org/10.25165/j.ijabe.20191203.4358.

WANG, S.L. et al. Performances evaluation of four typical unmanned aerial vehicles used for pesticide application in China. International Journal of Agricultural and Biological Engineering, v. 10, p. 22-31, 2017. https://doi.org/10.25165/j.ijabe.20171004.3219.

WANG, W., LI, C. Size estimation of sweet onions using consumer-grade RGB-depth sensor. Journal of Food Engineering, v. 142, p. 153-162, 2014. https://doi.org/10.1016/j.jfoodeng.2014.06.019.

WATANABE, K. et al. High-throughput phenotyping of sorghum plant height using an unmanned aerial vehicle and its application to genomic prediction modeling. Frontiers in Plant Science, v. 8, p. 1-11, 2017. https://doi.org/10.3389/fpls.2017.00421.

WEISS, M.; BARET, F. Using 3D Point Clouds Derived from UAV RGB Imagery to Describe Vineyard 3D Macro-Structure. Remote Sensing, v. 9, p. 111, 2017. https://doi.org/10.3390/rs9020111.

WEN, S. et al. Design of plant protection uav variable spray system based on neural networks. Sensors, v. 19, 2019. https://doi.org/10.3390/s19051112.

WIESNER-HANKS, T. et al. Millimeter-Level Plant Disease Detection From Aerial Photographs via Deep Learning and Crowdsourced Data. Frontiers in Plant Science, v. 10, p. 1-11, 2019. https://doi.org/10.3389/fpls.2019.01550.

WILLKOMM, M.; BOLTEN, A.; BARETH, G. Non-destructive monitoring of rice by hyperspectral in-field spectrometry and UAV-based remote sensing: Case study of field-grown rice in North Rhine-Westphalia, Germany. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 2016–July, p. 1071-1077, 2016. https://doi.org/10.5194/isprsarchives-XLI-B1-1071-2016.

WOO, H. et al. Evaluating ortho-photo production potentials based on UAV real-time geo-referencing points. Spatial Information Research, v. 26, p. 639-646, 2018. https://doi.org/10.1007/s41324-018-0208-9.

WU, M. et al. Evaluation of orthomosics and digital surface models derived from aerial imagery for crop type mapping. Remote Sensing, v. 9, p. 1-14, 2017. https://doi.org/10.3390/rs9030239.

XIAO, D.Q. et al. Classification and recognition scheme for vegetable pests based on the BOF-SVM model. International Journal of Agricultural and Biological Engineering, v. 11, p. 190-196, 2018. https://doi.org/10.25165/j.ijabe.20181103.3477.

XIAO, Q. et al. Comparison of droplet deposition control efficacy on phytophthora capsica and aphids in the processing pepper field of the unmanned aerial vehicle and knapsack sprayer. Agronomy, v. 10, 2020. https://doi.org/10.3390/agronomy10020215.

XUE, J.; SU, B., 2017. Significant remote sensing vegetation indices: A review of developments and applications. Journal of Sensors, 2017. https://doi.org/10.1155/2017/1353691.

YANG, G. et al. Unmanned Aerial Vehicle Remote Sensing for Field-Based Crop Phenotyping: Current Status and Perspectives. Frontiers in Plant Science, v. 8, 2017. https://doi.org/10.3389/fpls.2017.01111.

YANG, M. et al Assessment of Water and Nitrogen Use Efficiencies Through UAV-Based Multispectral Phenotyping in Winter Wheat. Frontiers in Plant Science, v. 11, p. 1-16, 2020. https://doi.org/10.3389/fpls.2020.00927.

YANG, S.; YANG, X.; MO, J. The application of unmanned aircraft systems to plant protection in China. Precis. Agric., v. 19, p. 278-292, 2018 https://doi.org/10.1007/s11119-017-9516-7.

YAO, W. et al. Effect of UAV prewetting application during the flowering period of cotton on pesticide droplet deposition. Frontiers of Agricultural Science and Engineering, v. 5, p. 455-461, 2018. https://doi.org/10.15302/J-FASE-2018232.

YE, H. et al. Identification of banana fusarium wilt using supervised classification algorithms with UAV-based multi-spectral imagery. International Journal of Agricultural and Biological Engineering, v. 13, p. 136-142, 2020. https://doi.org/10.25165/j.ijabe.20201303.5524.

YUE, J. et al. Estimation of Winter Wheat Above-Ground Biomass Using Unmanned Aerial Vehicle-Based Snapshot Hyperspectral Sensor and Crop Height Improved Models. Remote Sensing, v. 9, 2017. https://doi.org/10.3390/rs9070708.

YUE, J. et al. Estimate of winter-wheat aboveground biomass based on UAV ultrahigh-ground-resolution image textures and vegetation indices. ISPRS Journal of Photogrammetry and Remote Sensing, v. 150, p. 226-244, 2019. https://doi.org/10.1016/j.isprsjprs.2019.02.022.

ZHANG, D. et al. Detection of rice sheath blight using an unmanned aerial system with high-resolution color and multi-spectral imaging. PLoS One, v. 13, p. 1-15, 2018. https://doi.org/10.1371/journal.pone.0187470.

ZHANG, K.F. et al. Experimental study of single-rotor UAV on droplet deposition distribution in soybean field. Applied Ecology and Environmental Research, v. 17, p. 13833-13844, 2019. https://doi.org/10.15666/aeer/1706_1383313844.

ZHANG, N. et al. Monitoring daily variation of leaf layer photosynthesis in rice using UAV-based multi-spectral imagery and a light response curve model. Agricultural and Forest Meteorology, v. 291, 2020. https://doi.org/10.1016/j.agrformet.2020.108098.

ZHANG, X.Q. et al. Effects of Spray Parameters of Drone on the Droplet Deposition in Sugarcane Canopy. Sugar Tech, v. 22, p. 583-588, 2020. https://doi.org/10.1007/s12355-019-00792-z.

ZHENG, H. et al. Enhancing the nitrogen signals of rice canopies across critical growth stages through the integration of textural and spectral information from unmanned aerial vehicle (UAV) multi-spectral imagery. Remote Sensing, v. 12, 2020. https://doi.org/10.3390/rs12060957.

ZHENG, Y.J. et al. Modelling operation parameters of UAV on spray effects at different growth stages of corns. International Journal of Agricultural and Biological Engineering, v. 10, p. 57-66, 2017. https://doi.org/10.3965/j.ijabe.20171003.2578.

ZHOU, J. et al. Classification of soybean leaf wilting due to drought stress using UAV-based imagery. Computers and Electronics in Agriculture, v. 175, p. 105576, 2020. https://doi.org/10.1016/j.compag.2020.105576.




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