Unmanned aerial vehicle (UAV) imaging and machine learning applications for plant phenotyping
Tài liệu tham khảo
Adelabu, 2014, Evaluating the impact of red-edge band from Rapideye image for classifying insect defoliation levels, ISPRS J. Photogramm. Remote Sens., 95, 34, 10.1016/j.isprsjprs.2014.05.013
Ahamed, 2011, A review of remote sensing methods for biomass feedstock production, Biomass Bioenergy, 35, 2455, 10.1016/j.biombioe.2011.02.028
Alamoodi, 2021, Machine learning-based imputation soft computing approach for large missing scale and non-reference data imputation, Chaos Solitons Fractals, 151, 10.1016/j.chaos.2021.111236
Ali, 2019, Detection of critical safety events on freeways in clear and rainy weather using SHRP2 naturalistic driving data: Parametric and non-parametric techniques, Saf. Sci., 119, 141, 10.1016/j.ssci.2019.01.007
Alonzo, L.M.B., Chioson, F.B., Co, H.S., Bugtai, N.T., Baldovino, R.G., 2018. A Machine Learning Approach for Coconut Sugar Quality Assessment and Prediction, in: 2018 IEEE 10th International Conference on Humanoid, Nanotechnology, Information Technology,Communication and Control, Environment and Management (HNICEM). Presented at the 2018 IEEE 10th International Conference on Humanoid, Nanotechnology, Information Technology,Communication and Control, Environment and Management (HNICEM), pp. 1–4. https://doi.org/10.1109/HNICEM.2018.8666315.
Ampatzidis, 2019, UAV-Based High Throughput Phenotyping in Citrus Utilizing Multispectral Imaging and Artificial Intelligence, Remote Sens. (Basel), 11, 410, 10.3390/rs11040410
Ampatzidis, 2020, Agroview: Cloud-based application to process, analyze and visualize UAV-collected data for precision agriculture applications utilizing artificial intelligence, Comput. Electron. Agric., 174, 10.1016/j.compag.2020.105457
Anthony, 2014, On crop height estimation with UAVs, 4805
Atefi, 2021, Robotic technologies for high-throughput plant phenotyping: Contemporary reviews and future perspectives, Front. Plant Sci., 12, 10.3389/fpls.2021.611940
Barrientos, 2011, Aerial remote sensing in agriculture: A practical approach to area coverage and path planning for fleets of mini aerial robots, J. Field Rob., 28, 667, 10.1002/rob.20403
Battude, 2016, Estimating maize biomass and yield over large areas using high spatial and temporal resolution Sentinel-2 like remote sensing data, Remote Sens. Environ., 184, 668, 10.1016/j.rse.2016.07.030
Becker-Reshef, 2020, Strengthening agricultural decisions in countries at risk of food insecurity: The GEOGLAM Crop Monitor for Early Warning, Remote Sens. Environ., 237, 10.1016/j.rse.2019.111553
Bendig, 2014, Estimating biomass of barley using Crop Surface Models (CSMs) derived from UAV-based RGB imaging, Remote Sens. (Basel), 6, 10395, 10.3390/rs61110395
Berrar, D., 2018. Cross-Validation. https://doi.org/10.1016/B978-0-12-809633-8.20349-X.
Boulesteix, 2012, Overview of random forest methodology and practical guidance with emphasis on computational biology and bioinformatics, WIREs Data Min. Knowl. Discovery, 2, 493, 10.1002/widm.1072
Brocks, 2018, Estimating barley biomass with crop surface models from oblique RGB imagery, Remote Sens. (Basel), 10, 268, 10.3390/rs10020268
Cai, 2018, Feature selection in machine learning: A new perspective, Neurocomputing, 300, 70, 10.1016/j.neucom.2017.11.077
Candiago, 2015, Evaluating multispectral images and vegetation indices for precision farming applications from UAV images, Remote Sens. (Basel), 7, 4026, 10.3390/rs70404026
Chang, 2017, Crop height monitoring with digital imagery from Unmanned Aerial System (UAS), Comput. Electron. Agric., 141, 232, 10.1016/j.compag.2017.07.008
Cortes, 1995, Support-vector networks, Mach. Learn., 20, 273, 10.1007/BF00994018
Cosenza, 2022, Effects of numbers of observations and predictors for various model types on the performance of forest inventory with airborne laser scanning, Can. J. For. Res., 52, 385, 10.1139/cjfr-2021-0192
Crampton, 2016, Assemblage of the vertical: commercial drones and algorithmic life, Geographica Helvetica, 71, 137, 10.5194/gh-71-137-2016
Ćwiąkała, 2018, Assessment of the possibility of using Unmanned Aerial Vehicles (UAVs) for the documentation of hiking trails in alpine areas, Sensors, 18, 81, 10.3390/s18010081
de Vlaming, 2015, The current and future use of ridge regression for prediction in quantitative genetics, Biomed Res. Int., 2015, e143712, 10.1155/2015/143712
Deery, D.M., Jones, H.G., 2021. Field Phenomics: Will It Enable Crop Improvement? Plant Phenomics 2021, 2021/9871989. https://doi.org/10.34133/2021/9871989.Deery.
Delavarpour, 2021, A technical study on UAV characteristics for precision agriculture applications and associated practical challenges, Remote Sens. (Basel), 13, 1204, 10.3390/rs13061204
Delen, D., Oztekin, A., Kong, Z. (James), 2010. A machine learning-based approach to prognostic analysis of thoracic transplantations. Artificial Intelligence in Medicine 49, 33–42. https://doi.org/10.1016/j.artmed.2010.01.002.
Dicke, 2012, Quantifying herbicide injuries in maize by use of remote sensing, Julius-Kühn-Archiv, 1, 199
Dittmar, D.P., Bryant, T., Crawford, H., 2021. Handbook Design and Composition 579.
Doughty, 2019, Mapping coastal wetland biomass from high resolution Unmanned Aerial Vehicle (UAV) imagery, Remote Sens. (Basel), 11, 540, 10.3390/rs11050540
Easterday, 2019, Remotely sensed water limitation in vegetation: Insights from an experiment with Unmanned Aerial Vehicles (UAVs), Remote Sens. (Basel), 11, 1853, 10.3390/rs11161853
Esquerdo, 2011, Use of NDVI/AVHRR time-series profiles for soybean crop monitoring in Brazil, Int. J. Remote Sens., 32, 3711, 10.1080/01431161003764112
Feng, 2019, Cotton yield estimation from UAV-based plant height, Trans. ASABE, 62, 393, 10.13031/trans.13067
Friedman, 2010, Regularization paths for generalized linear models via coordinate descent, J. Stat. Softw., 33, 1, 10.18637/jss.v033.i01
Gitelson, 2002, Novel algorithms for remote estimation of vegetation fraction, Remote Sens. Environ., 80, 76, 10.1016/S0034-4257(01)00289-9
Gitelson, 1998, Remote sensing of chlorophyll concentration in higher plant leaves, Adv. Space Res. - ADV. SPACE RES., 22, 689, 10.1016/S0273-1177(97)01133-2
Goel, 2003, Potential of airborne hyperspectral remote sensing to detect nitrogen deficiency and weed infestation in corn, Comput. Electron. Agric., 38, 99, 10.1016/S0168-1699(02)00138-2
Guo, Y., Jia, X., Paull, D., Zhang, J., Farooq, A., Chen, X., Islam, Md.N., 2019. A Drone-Based Sensing System to Support Satellite Image Analysis for Rice Farm Mapping, in: IGARSS 2019 - 2019 IEEE International Geoscience and Remote Sensing Symposium. Presented at the IGARSS 2019 - 2019 IEEE International Geoscience and Remote Sensing Symposium, pp. 9376–9379. https://doi.org/10.1109/IGARSS.2019.8898638.
Guo, 2020, Modified red blue vegetation index for chlorophyll estimation and yield prediction of maize from visible images captured by UAV, Sensors, 20, 5055, 10.3390/s20185055
Gupta, 2021, Advances of UAVs toward future transportation: The state-of-the-art, challenges, and opportunities, Future Transportation, 1, 326, 10.3390/futuretransp1020019
Han, 2018, Measurement and calibration of plant-height from fixed-wing UAV images, Sensors, 18, 4092, 10.3390/s18124092
Han, 2019, Modeling maize above-ground biomass based on machine learning approaches using UAV remote-sensing data, Plant Methods, 15, 10, 10.1186/s13007-019-0394-z
Hebbali, 2022, olsrr. Rsquared Academy
Holman, 2016, High throughput field phenotyping of wheat plant height and growth rate in field plot trials using UAV based remote sensing, Remote Sens. (Basel), 8, 1031, 10.3390/rs8121031
Huang, 2013, Development and prospect of unmanned aerial vehicle technologies for agricultural production management, Int. J. Agric. Biol. Eng., 6, 1
Huang, 2018, Agricultural remote sensing big data: Management and applications, J. Integr. Agric., 17, 1915, 10.1016/S2095-3119(17)61859-8
Huang, 2018, UAV low-altitude remote sensing for precision weed management, Weed Technol., 32, 2, 10.1017/wet.2017.89
Huete, 1988, A soil-adjusted vegetation index (SAVI), Remote Sens. Environ., 25, 295, 10.1016/0034-4257(88)90106-X
Ismail, 2010, A comparison of regression tree ensembles: Predicting Sirex noctilio induced water stress in Pinus patula forests of KwaZulu-Natal, South Africa, Int. J. Appl. Earth Obs. Geoinf., 12, S45
Ji, 2022, Estimation of plant height and yield based on UAV imagery in faba bean (Vicia faba L.), Plant Methods, 18, 26, 10.1186/s13007-022-00861-7
Jiang, 2007, 2-Band enhanced vegetation index without a blue band and its application to AVHRR data, Proc. SPIE-Int. Soc. Opt. Eng., 6679
Jiménez, 2019, Identifying cognitive deficits in cocaine dependence using standard tests and machine learning, Prog. Neuropsychopharmacol. Biol. Psychiatry, 95, 10.1016/j.pnpbp.2019.109709
Johnson, D.M., 2013. Forecasting corn and soybean yields in the United States utilizing pre- and within-season remotely sensed variables 2013, B54C-02.
Jung, 2021, The potential of remote sensing and artificial intelligence as tools to improve the resilience of agriculture production systems, Curr. Opin. Biotechnol. Food Biotechnol. Plant Biotechnol., 70, 15, 10.1016/j.copbio.2020.09.003
Kalogiannidis, 2022, Role of crop-protection technologies in sustainable agricultural productivity and management, Land, 11, 1680, 10.3390/land11101680
Khanal, 2018, Integration of high resolution remotely sensed data and machine learning techniques for spatial prediction of soil properties and corn yield, Comput. Electron. Agric., 153, 213, 10.1016/j.compag.2018.07.016
Kim, 2018, Modeling and testing of growth status for chinese cabbage and white radish with UAV-based RGB imagery, Remote Sens. (Basel), 10, 563, 10.3390/rs10040563
Klemas, 2015, Coastal and environmental remote sensing from unmanned aerial vehicles: An overview, J. Coast. Res., 31, 1260, 10.2112/JCOASTRES-D-15-00005.1
Kopitar, 2020, Early detection of type 2 diabetes mellitus using machine learning-based prediction models, Sci. Rep., 10, 11981, 10.1038/s41598-020-68771-z
Kuhn, 2008, Building predictive models in R using the caret package, J. Stat. Softw., 28, 1, 10.18637/jss.v028.i05
Lakshminarayan, 1999, Imputation of missing data in industrial databases, Appl. Intell., 11, 259, 10.1023/A:1008334909089
Lattanzi, 2017, Review of robotic infrastructure inspection systems, J. Infrastruct. Syst., 23, 04017004, 10.1061/(ASCE)IS.1943-555X.0000353
Li, 2019, Mapping of river terraces with low-cost UAS based structure-from-motion photogrammetry in a complex terrain setting, Remote Sens. (Basel), 11, 464, 10.3390/rs11040464
Li, 2019, Applications of multirotor drone technologies in construction management, Int. J. Constr. Manag., 19, 401
Li, 2016, Remote estimation of canopy height and aboveground biomass of maize using high-resolution stereo images from a low-cost unmanned aerial vehicle system, Ecol. Ind., 67, 637, 10.1016/j.ecolind.2016.03.036
Linaza, M.T., Posada, J., Bund, J., Eisert, P., Quartulli, M., Döllner, J., Pagani, A., G. Olaizola, I., Barriguinha, A., Moysiadis, T., Lucat, L., 2021. Data-Driven Artificial Intelligence Applications for Sustainable Precision Agriculture. Agronomy 11, 1227. https://doi.org/10.3390/agronomy11061227.
Liu, 2022, Estimation of potato above-ground biomass using UAV-based hyperspectral images and machine-learning regression, Remote Sens. (Basel), 14, 5449, 10.3390/rs14215449
Lobell, 2003, Remote sensing of regional crop production in the Yaqui Valley, Mexico: estimates and uncertainties, Agr. Ecosyst. Environ., 94, 205, 10.1016/S0167-8809(02)00021-X
Löw, 2018, Regional-scale monitoring of cropland intensity and productivity with multi-source satellite image time series, GISci. Remote Sens., 55, 539, 10.1080/15481603.2017.1414010
Lussem, 2019, Estimating biomass in temperate grassland with high resolution canopy surface models from UAV-based RGB images and vegetation indices, JARS, 13
Lussem, 2019, Estimating biomass in temperate grassland with high resolution canopy surface models from UAV-based RGB images and vegetation indices, JARS, 13
Mahesh, 2019, Machine Learning Algorithms -A Review, IJSR, 9, 381
Maimaitijiang, 2020, Crop monitoring using satellite/UAV data fusion and machine learning, Remote Sens. (Basel), 12, 1357, 10.3390/rs12091357
Marston, 2020, Detection of stress induced by Soybean Aphid (Hemiptera: Aphididae) using multispectral imagery from unmanned aerial vehicles, J. Econ. Entomol., 113, 779, 10.1093/jee/toz306
Maulud, 2020, A review on linear regression comprehensive in machine learning, JASTT, 1, 140, 10.38094/jastt1457
Mazur, M., 2016. Six Ways Drones Are Revolutionizing Agriculture [WWW Document]. MIT Technology Review. URL https://www.technologyreview.com/2016/07/20/158748/six-ways-drones-are-revolutionizing-agriculture/ (accessed 2.2.23).
MicaSense, A.S., 2021. Atlas Flight by MicaSense, Inc. [WWW Document]. AppAdvice. URL /app/atlas-flight/1103867349 (accessed 5.12.21).
Mochida, 2019, Computer vision-based phenotyping for improvement of plant productivity: a machine learning perspective, GigaScience, 8, giy153, 10.1093/gigascience/giy153
Mohammed, 2019, Failure prediction using machine learning in a virtualised HPC system and application, Cluster Comput., 22, 471, 10.1007/s10586-019-02917-1
Moran, 1997, Opportunities and limitations for image-based remote sensing in precision crop management, Remote Sens. Environ., 61, 319, 10.1016/S0034-4257(97)00045-X
Nadkarni, P., 2016. Chapter 10 - Core Technologies: Data Mining and “Big Data,” in: Nadkarni, P. (Ed.), Clinical Research Computing. Academic Press, pp. 187–204. https://doi.org/10.1016/B978-0-12-803130-8.00010-5.
Nex, 2014, UAV for 3D mapping applications: a review, Appl. Geomat., 6, 1, 10.1007/s12518-013-0120-x
Nickmilder, 2021, Development of machine learning models to predict compressed sward height in walloon pastures based on sentinel-1, sentinel-2 and meteorological data using multiple data transformations, Remote Sens. (Basel), 13, 408, 10.3390/rs13030408
Niu, 2019, Estimating above-ground biomass of maize using features derived from UAV-based RGB imagery, Remote Sens. (Basel), 11, 1261, 10.3390/rs11111261
Osborne, 2019, Four assumptions of multiple regression that researchers should always test, Pract. Assess. Res. Eval., 8
Osco, L.P., Junior, J.M., Ramos, A.P.M., Furuya, D.E.G., Santana, D.C., Teodoro, L.P.R., Gonçalves, W.N., Baio, F.H.R., Pistori, H., Junior, C.A. da S., Teodoro, P.E., 2020. Leaf Nitrogen Concentration and Plant Height Prediction for Maize Using UAV-Based Multispectral Imagery and Machine Learning Techniques. Remote Sensing 12, 3237. https://doi.org/10.3390/rs12193237.
Oumar, 2014, Integrating environmental variables and WorldView-2 image data to improve the prediction and mapping of Thaumastocoris peregrinus (bronze bug) damage in plantation forests, ISPRS J. Photogramm. Remote Sens., 87, 39, 10.1016/j.isprsjprs.2013.10.010
Panday, 2020, Correlating the plant height of wheat with above-ground biomass and crop yield using drone imagery and crop surface model. A case study from Nepal, Drones, 4, 28, 10.3390/drones4030028
Partel, 2019, Automated vision-based system for monitoring Asian citrus psyllid in orchards utilizing artificial intelligence, Comput. Electron. Agric., 162, 328, 10.1016/j.compag.2019.04.022
Paudel, 2021, Machine learning for large-scale crop yield forecasting, Agr. Syst., 187, 10.1016/j.agsy.2020.103016
Payero, 2004, Comparison of eleven vegetation indices for estimating plant height of alfalfa and grass, Appl. Eng. Agric., 20, 385, 10.13031/2013.16057
Pérez-Ortiz, 2015, A semi-supervised system for weed mapping in sunflower crops using unmanned aerial vehicles and a crop row detection method, Appl. Soft Comput., 37, 533, 10.1016/j.asoc.2015.08.027
Paul J. Pinter, Jr., Jerry L. Hatfield, James S. Schepers, Edward M. Barnes, M. Susan Moran, Craig S.T. Daughtry, Dan R. Upchurch, 2003. Remote Sensing for Crop Management. Photogrammetric engineering and remote sensing 69, 647–664. https://doi.org/10.14358/PERS.69.6.647.
Poudyal, 2023, Prediction of morpho-physiological traits in sugarcane using aerial imagery and machine learning, Smart Agric. Technol., 3
Pranga, 2021, Improving accuracy of herbage yield predictions in perennial ryegrass with UAV-based structural and spectral data fusion and machine learning, Remote Sens. (Basel), 13, 3459, 10.3390/rs13173459
Prasad, 2006, Crop yield estimation model for Iowa using remote sensing and surface parameters, Int. J. Appl. Earth Obs. Geoinf., 8, 26
Probst, 2019, Hyperparameters and tuning strategies for random forest, WIREs Data Min. Knowl. Discovery, 9, e1301, 10.1002/widm.1301
Rajaveni, 2017, Geological and geomorphological controls on groundwater occurrence in a hard rock region, Appl. Water Sci., 7, 1377, 10.1007/s13201-015-0327-6
Rashidi, H.H., Tran, N.K., Betts, E.V., Howell, L.P., Green, R., 2019. Artificial Intelligence and Machine Learning in Pathology: The Present Landscape of Supervised Methods. Academic Pathology 6, 2374289519873088. https://doi.org/10.1177/2374289519873088.
Rasmussen, 2016, Are vegetation indices derived from consumer-grade cameras mounted on UAVs sufficiently reliable for assessing experimental plots?, Eur. J. Agron., 74, 75, 10.1016/j.eja.2015.11.026
Reedha, 2022, Transformer neural network for weed and crop classification of high resolution UAV images, Remote Sens. (Basel), 14, 592, 10.3390/rs14030592
Ren, 2020, Comparison of machine learning and land use regression for fine scale spatiotemporal estimation of ambient air pollution: Modeling ozone concentrations across the contiguous United States, Environ. Int., 142, 10.1016/j.envint.2020.105827
Revenga, 2022, Above-ground biomass prediction for croplands at a sub-meter resolution using UAV–LiDAR and machine learning methods, Remote Sens. (Basel), 14, 3912, 10.3390/rs14163912
Robles, 2010, Potential for remote sensing to detect and predict herbicide injury on Waterhyacinth (Eichhornia crassipes), Invasive Plant Sci. Manage., 3, 440, 10.1614/IPSM-D-09-00040.1
Rosser, 2018, Surgical and medical applications of drones: A comprehensive review, JSLS, 22, 00018
Rouse, J.W., Haas, R.H., Schell, J.A., Deering, D.W., 1974. Monitoring vegetation systems in the Great Plains with ERTS.
Ruengvirayudh, 2016, Comparing stepwise regression models to the best-subsets models, or, the art of stepwise, General Linear Model J.
Samseemoung, 2012, Application of low altitude remote sensing (LARS) platform for monitoring crop growth and weed infestation in a soybean plantation, Precis. Agric., 13, 611, 10.1007/s11119-012-9271-8
Schratz, 2019, Hyperparameter tuning and performance assessment of statistical and machine-learning algorithms using spatial data, Ecol. Model., 406, 109, 10.1016/j.ecolmodel.2019.06.002
Selvaraj, 2020, Machine learning for high-throughput field phenotyping and image processing provides insight into the association of above and below-ground traits in cassava (Manihot esculenta Crantz), Plant Methods, 16, 87, 10.1186/s13007-020-00625-1
Shakoor, N., Lee, S., Mockler, T.C., 2017. High throughput phenotyping to accelerate crop breeding and monitoring of diseases in the field. Current Opinion in Plant Biology, 38 Biotic interactions 2017 38, 184–192. https://doi.org/10.1016/j.pbi.2017.05.006.
Sims, 2002, Relationships between leaf pigment content and spectral reflectance across a wide range of species, leaf structures and developmental stages, Remote Sens. Environ., 81, 337, 10.1016/S0034-4257(02)00010-X
Singh, 2018, A meta-analysis and review of unmanned aircraft system (UAS) imagery for terrestrial applications, Int. J. Remote Sens., 39, 5078, 10.1080/01431161.2017.1420941
Soltis, 2020, Plants meet machines: Prospects in machine learning for plant biology, Appl. Plant Sci., 8, e11371, 10.1002/aps3.11371
Song, 2019, Winter wheat canopy height extraction from UAV-based point cloud data with a moving cuboid filter, Remote Sens. (Basel), 11, 1239, 10.3390/rs11101239
Strobl, 2009, An introduction to recursive partitioning: Rationale, application, and characteristics of classification and regression trees, bagging, and random forests, Psychol. Methods, 14, 323, 10.1037/a0016973
Sun, 2020, Machine learning and its applications in plant molecular studies, Brief. Funct. Genomics, 19, 40, 10.1093/bfgp/elz036
Svetnik, 2003, Random forest: A classification and regression tool for compound classification and QSAR modeling, J. Chem. Inf. Comput. Sci., 43, 1947, 10.1021/ci034160g
Teodoro, 2021, Predicting days to maturity, plant height, and grain yield in soybean: A machine and deep learning approach using multispectral data, Remote Sens. (Basel), 13, 4632, 10.3390/rs13224632
Thelen, 2004, Use of optical remote sensing for detecting herbicide injury in soybean, Weed Technol., 18, 292, 10.1614/WT-03-049R2
Thenkabail, 1994, Thematic mapper vegetation indices for determining soybean and corn growth parameters. PE&RS, 60, 437
Tilly, 2015, Fusion of plant height and vegetation indices for the estimation of barley biomass, Remote Sens. (Basel), 7, 11449, 10.3390/rs70911449
van Klompenburg, 2020, Crop yield prediction using machine learning: A systematic literature review, Comput. Electron. Agric., 177, 10.1016/j.compag.2020.105709
Veysi, 2017, A satellite based crop water stress index for irrigation scheduling in sugarcane fields, Agric. Water Manag., 189, 70, 10.1016/j.agwat.2017.04.016
Vijayakumar, 2023, Tree-level citrus yield prediction utilizing ground and aerial machine vision and machine learning, Smart Agric. Technol., 3
Viljanen, 2018, 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, 8, 70, 10.3390/agriculture8050070
Wang, 2022, Deep learning augmented data assimilation: Reconstructing missing information with convolutional autoencoders, Mon. Weather Rev., 150, 1977, 10.1175/MWR-D-21-0288.1
Wang, 2016, Estimation of biomass in wheat using random forest regression algorithm and remote sensing data, Crop J., 4, 212, 10.1016/j.cj.2016.01.008
Watanabe, 2017, High-throughput phenotyping of sorghum plant height using an unmanned aerial vehicle and its application to genomic prediction modeling, Front. Plant Sci., 8, 421, 10.3389/fpls.2017.00421
Whitmire, C.D., Vance, J.M., Rasheed, H.K., Missaoui, A., Rasheed, K.M., Maier, F.W., 2021. Using Machine Learning and Feature Selection for Alfalfa Yield Prediction. AI 2, 71–88. https://doi.org/10.3390/ai2010006.
Wiseman, 2014, RADARSAT-2 polarimetric SAR response to crop biomass for agricultural production monitoring, IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens., 7, 4461, 10.1109/JSTARS.2014.2322311
Xiang, 2019, Mini-unmanned aerial vehicle-based remote sensing: Techniques, applications, and prospects, IEEE Geosci. Remote Sens. Mag., 7, 29, 10.1109/MGRS.2019.2918840
Xu, 2022, Consistency-regularized region-growing network for semantic segmentation of urban scenes with point-level annotations, IEEE Trans. Image Process., 31, 5038, 10.1109/TIP.2022.3189825
Yalcin, H., 2017. Plant phenology recognition using deep learning: Deep-Pheno, in: 2017 6th International Conference on Agro-Geoinformatics. Presented at the 2017 6th International Conference on Agro-Geoinformatics, pp. 1–5. https://doi.org/10.1109/Agro-Geoinformatics.2017.8046996.
Yao, 2019, Unmanned aerial vehicle for remote sensing applications—A review, Remote Sens. (Basel), 11, 1443, 10.3390/rs11121443
Yu, 2010, Application of support vector machine modeling for prediction of common diseases: the case of diabetes and pre-diabetes, BMC Med. Inform. Decis. Mak., 10, 16, 10.1186/1472-6947-10-16
Yue, 2018, A comparison of regression techniques for estimation of above-ground winter wheat biomass using near-surface spectroscopy, Remote Sens. (Basel), 10, 66, 10.3390/rs10010066
Zhang, 2012, The application of small unmanned aerial systems for precision agriculture: a review, Precis. Agric., 13, 693, 10.1007/s11119-012-9274-5
Zhang, 2022, Multi-phenotypic parameters extraction and biomass estimation for lettuce based on point clouds, Measurement, 204, 10.1016/j.measurement.2022.112094
Zhang, 2019, Maize canopy temperature extracted from UAV thermal and RGB imagery and its application in water stress monitoring, Front. Plant Sci., 10, 1270, 10.3389/fpls.2019.01270
Zheng, 2022, Prediction of strawberry dry biomass from UAV multispectral imagery using multiple machine learning methods, Remote Sens. (Basel), 14, 4511, 10.3390/rs14184511
Zou, 2003, Correlation and simple linear regression, Radiology, 227, 617, 10.1148/radiol.2273011499