{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,29]],"date-time":"2026-03-29T15:23:01Z","timestamp":1774797781852,"version":"3.50.1"},"reference-count":118,"publisher":"MDPI AG","issue":"16","license":[{"start":{"date-parts":[[2021,8,12]],"date-time":"2021-08-12T00:00:00Z","timestamp":1628726400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"European Regional Development Fund within the Estonian National Programme for Addressing Socio-Economic Challenges through R&amp;D (RITA)","award":["L180283PKKK"],"award-info":[{"award-number":["L180283PKKK"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>The recent trend of automated machine learning (AutoML) has been driving further significant technological innovation in the application of artificial intelligence from its automated algorithm selection and hyperparameter optimization of the deployable pipeline model for unraveling substance problems. However, a current knowledge gap lies in the integration of AutoML technology and unmanned aircraft systems (UAS) within image-based data classification tasks. Therefore, we employed a state-of-the-art (SOTA) and completely open-source AutoML framework, Auto-sklearn, which was constructed based on one of the most widely used ML systems: Scikit-learn. It was combined with two novel AutoML visualization tools to focus particularly on the recognition and adoption of UAS-derived multispectral vegetation indices (VI) data across a diverse range of agricultural management practices (AMP). These include soil tillage methods (STM), cultivation methods (CM), and manure application (MA), and are under the four-crop combination fields (i.e., red clover-grass mixture, spring wheat, pea-oat mixture, and spring barley). Furthermore, they have currently not been efficiently examined and accessible parameters in UAS applications are absent for them. We conducted the comparison of AutoML performance using three other common machine learning classifiers, namely Random Forest (RF), support vector machine (SVM), and artificial neural network (ANN). The results showed AutoML achieved the highest overall classification accuracy numbers after 1200 s of calculation. RF yielded the second-best classification accuracy, and SVM and ANN were revealed to be less capable among some of the given datasets. Regarding the classification of AMPs, the best recognized period for data capture occurred in the crop vegetative growth stage (in May). The results demonstrated that CM yielded the best performance in terms of classification, followed by MA and STM. Our framework presents new insights into plant\u2013environment interactions with capable classification capabilities. It further illustrated the automatic system would become an important tool in furthering the understanding for future sustainable smart farming and field-based crop phenotyping research across a diverse range of agricultural environmental assessment and management applications.<\/jats:p>","DOI":"10.3390\/rs13163190","type":"journal-article","created":{"date-parts":[[2021,8,12]],"date-time":"2021-08-12T10:54:41Z","timestamp":1628765681000},"page":"3190","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":24,"title":["An Automated Machine Learning Framework in Unmanned Aircraft Systems: New Insights into Agricultural Management Practices Recognition Approaches"],"prefix":"10.3390","volume":"13","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-0077-3770","authenticated-orcid":false,"given":"Kai-Yun","family":"Li","sequence":"first","affiliation":[{"name":"Institute of Agriculture and Environmental Sciences, Estonian University of Life Sciences, Kreutzwaldi 5, 51006 Tartu, Estonia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0416-1608","authenticated-orcid":false,"given":"Niall G.","family":"Burnside","sequence":"additional","affiliation":[{"name":"School of Environment & Technology, University of Brighton, Lewes Road, Brighton BN2 4JG, UK"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0758-0656","authenticated-orcid":false,"given":"Raul Sampaio","family":"de Lima","sequence":"additional","affiliation":[{"name":"Institute of Agriculture and Environmental Sciences, Estonian University of Life Sciences, Kreutzwaldi 5, 51006 Tartu, Estonia"}]},{"given":"Miguel Villoslada","family":"Peci\u00f1a","sequence":"additional","affiliation":[{"name":"Institute of Agriculture and Environmental Sciences, Estonian University of Life Sciences, Kreutzwaldi 5, 51006 Tartu, Estonia"}]},{"given":"Karli","family":"Sepp","sequence":"additional","affiliation":[{"name":"Agricultural Research Center, 4\/6 Teaduse St., 75501 Saku, Estonia"}]},{"given":"Victor Henrique","family":"Cabral Pinheiro","sequence":"additional","affiliation":[{"name":"Institute of Computer Science, Faculty of Science and Technology, University of Tartu, 50090 Tartu, Estonia"}]},{"given":"Bruno Rucy Carneiro Alves","family":"de Lima","sequence":"additional","affiliation":[{"name":"Institute of Computer Science, Faculty of Science and Technology, University of Tartu, 50090 Tartu, Estonia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2904-5838","authenticated-orcid":false,"given":"Ming-Der","family":"Yang","sequence":"additional","affiliation":[{"name":"Department of Civil Engineering, Innovation and Development Center of Sustainable Agriculture, National Chung Hsing University, Taichung 402, Taiwan"}]},{"given":"Ants","family":"Vain","sequence":"additional","affiliation":[{"name":"Institute of Agriculture and Environmental Sciences, Estonian University of Life Sciences, Kreutzwaldi 5, 51006 Tartu, Estonia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8076-7943","authenticated-orcid":false,"given":"Kalev","family":"Sepp","sequence":"additional","affiliation":[{"name":"Institute of Agriculture and Environmental Sciences, Estonian University of Life Sciences, Kreutzwaldi 5, 51006 Tartu, Estonia"}]}],"member":"1968","published-online":{"date-parts":[[2021,8,12]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"358","DOI":"10.1016\/j.biosystemseng.2012.08.009","article-title":"Twenty five years of remote sensing in precision agriculture: Key advances and remaining knowledge gaps","volume":"114","author":"Mulla","year":"2013","journal-title":"Biosyst. Eng."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Tripicchio, P., Satler, M., Dabisias, G., Ruffaldi, E., and Avizzano, C.A. (2015, January 15\u201317). Towards Smart Farming and Sustainable Agriculture with Drones. Proceedings of the 2015 International Conference on Intelligent Environments, IEEE, Rabat, Morocco.","DOI":"10.1109\/IE.2015.29"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"49","DOI":"10.1016\/j.compag.2004.02.006","article-title":"Imaging from an unmanned aerial vehicle: Agricultural surveillance and decision support","volume":"44","author":"Herwitz","year":"2004","journal-title":"Comput. Electron. Agric."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"693","DOI":"10.1007\/s11119-012-9274-5","article-title":"The application of small unmanned aerial systems for precision agriculture: A review","volume":"13","author":"Zhang","year":"2012","journal-title":"Precis. Agric."},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Tsouros, D.C., Bibi, S., and Sarigiannidis, P.G. (2019). A Review on UAV-Based Applications for Precision Agriculture. Information, 10.","DOI":"10.3390\/info10110349"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"174","DOI":"10.1016\/j.biosystemseng.2010.11.010","article-title":"Development of a low-cost agricultural remote sensing system based on an autonomous unmanned aerial vehicle (UAV)","volume":"108","author":"Xiang","year":"2011","journal-title":"Biosyst. Eng."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"192","DOI":"10.1080\/22797254.2018.1527661","article-title":"Monitoring of crop fields using multispectral and thermal imagery from UAV","volume":"52","author":"Raeva","year":"2018","journal-title":"Eur. J. Remote Sens."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"372","DOI":"10.1016\/j.compag.2015.09.001","article-title":"Field-based crop phenotyping: Multispectral aerial imaging for evaluation of winter wheat emergence and spring stand","volume":"118","author":"Sankaran","year":"2015","journal-title":"Comput. Electron. Agric."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"1111","DOI":"10.3389\/fpls.2017.01111","article-title":"Unmanned Aerial Vehicle Remote Sensing for Field-Based Crop Phenotyping: Current Status and Perspectives","volume":"8","author":"Yang","year":"2017","journal-title":"Front. Plant Sci."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"52","DOI":"10.1016\/j.tplants.2013.09.008","article-title":"Field high-throughput phenotyping: The new crop breeding frontier","volume":"19","author":"Araus","year":"2014","journal-title":"Trends Plant Sci."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"98","DOI":"10.1016\/j.fcr.2018.10.014","article-title":"Assessing the influence of row spacing on soybean yield using experimental and producer survey data","volume":"230","author":"Andrade","year":"2018","journal-title":"Field Crop. Res."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"2599","DOI":"10.1007\/s00122-014-2402-z","article-title":"Genetic and non-genetic long-term trends of 12 different crops in German official variety performance trials and on-farm yield trends","volume":"127","author":"Laidig","year":"2014","journal-title":"Theor. Appl. Genet."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"1617","DOI":"10.1002\/csc2.20139","article-title":"Soft winter wheat outyields hard winter wheat in a subhumid environment: Weather drivers, yield plasticity, and rates of yield gain","volume":"60","author":"Lollato","year":"2020","journal-title":"Crop. Sci."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Zhu-Barker, X., and Steenwerth, K.L. (2018). Nitrous Oxide Production from Soils in the Future: Processes, Controls, and Responses to Climate Change, Chapter Six. Climate Change Impacts on Soil Processes and Ecosystem Properties, Elsevier.","DOI":"10.1016\/B978-0-444-63865-6.00006-5"},{"key":"ref_15","unstructured":"De Longe, M.S., Owen, J.J., and Silver, W.L. (2014). Greenhouse Gas Mitigation Opportunities in California Agriculture: Review of California Rangeland Emissions and Mitigation Potential, Duke University. Nicholas Institute for Environ, Policy Solutions Report."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"131","DOI":"10.1016\/B978-0-444-63865-6.00006-5","article-title":"Nitrous Oxide Production from Soils in the Future","volume":"Volume 35","author":"Steenwerth","year":"2018","journal-title":"Developments in Soil Science"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"279","DOI":"10.1016\/j.agee.2003.09.018","article-title":"Legume versus fertilizer sources of nitrogen: Ecological tradeoffs and human needs","volume":"102","author":"Crews","year":"2004","journal-title":"Agric. Ecosyst. Environ."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"107848","DOI":"10.1016\/j.fcr.2020.107848","article-title":"Exploring long-term variety performance trials to improve environment-specific genotype \u00d7 management recommendations: A case-study for winter wheat","volume":"255","author":"Munaro","year":"2020","journal-title":"Field Crop. Res."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"2167","DOI":"10.1890\/08-1122.1","article-title":"The fate of nitrogen in grain cropping systems: A meta-analysis of 15N field experiments","volume":"19","author":"Gardner","year":"2009","journal-title":"Ecol. Appl."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"163","DOI":"10.1016\/S0065-2113(04)92003-2","article-title":"Nutrients in Agroecosystems: Rethinking the Management Paradigm","volume":"92","author":"Drinkwater","year":"2007","journal-title":"Adv. Agron."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"4","DOI":"10.1016\/j.fcr.2012.09.009","article-title":"Yield gap analysis with local to global relevance\u2014A review","volume":"143","author":"Cassman","year":"2013","journal-title":"Field Crop. Res."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"175","DOI":"10.1016\/bs.agron.2017.01.003","article-title":"Delineation of Soil Management Zones for Variable-Rate Fertilization","volume":"143","author":"Nawar","year":"2017","journal-title":"Adv. Agron."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"61","DOI":"10.1016\/j.compag.2018.05.012","article-title":"Machine learning approaches for crop yield prediction and nitrogen status estimation in precision agriculture: A review","volume":"151","author":"Chlingaryan","year":"2018","journal-title":"Comput. Electron. Agric."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"76","DOI":"10.1109\/MWC.2019.1800350","article-title":"Toward Intelligent Network Optimization in Wireless Networking: An Auto-Learning Framework","volume":"26","author":"Zhang","year":"2019","journal-title":"IEEE Wirel. Commun."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"106622","DOI":"10.1016\/j.knosys.2020.106622","article-title":"AutoML: A survey of the state-of-the-art","volume":"212","author":"He","year":"2020","journal-title":"Knowl. Based Syst."},{"key":"ref_26","unstructured":"Mendoza, H., Klein, A., Feurer, M., Springenberg, J.T., and Hutter, F. (2016, January 24). Towards Automatically-Tuned Neural Networks. Proceedings of the Workshop on Automatic Machine Learning, New York, NY, USA."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"409","DOI":"10.1613\/jair.1.11854","article-title":"Benchmark and Survey of Automated Machine Learning Frameworks","volume":"70","author":"Huber","year":"2021","journal-title":"J. Artif. Intell. Res."},{"key":"ref_28","unstructured":"Yao, Q., Wang, M., Chen, Y., Dai, W., Hu, Y.Q., Li, Y.F., Tu, W.W., Yang, Q., and Yu, Y. (2018). Taking the Human out of Learning Applications: A Survey on Automated Machine Learning. arXiv."},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Thornton, C., Hutter, F., Hoos, H.H., and Leyton-Brown, K. (2013, January 11\u201314). Auto-WEKA: Combined Selection and Hyperparame-ter Optimization of Classification Algorithms. Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Chicago, IL, USA.","DOI":"10.1145\/2487575.2487629"},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Feurer, M., Klein, A., Eggensperger, K., Springenberg, J.T., Blum, M., and Hutter, F. (2019, January 8\u201314). Auto-sklearn: Efficient and Robust Automated Machine Learning. Proceedings of the Advances in Neural Information Processing Systems, Vancouver, BC, Canada.","DOI":"10.1007\/978-3-030-05318-5_6"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"103375","DOI":"10.1016\/j.compbiomed.2019.103375","article-title":"A review of feature selection methods in medical applications","volume":"112","author":"Remeseiro","year":"2019","journal-title":"Comput. Biol. Med."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"112434","DOI":"10.1016\/j.rse.2021.112434","article-title":"Estimation of root zone soil moisture from ground and remotely sensed soil information with multisensor data fusion and automated machine learning","volume":"260","author":"Babaeian","year":"2021","journal-title":"Remote Sens. Environ."},{"key":"ref_33","unstructured":"Ledell, E., and Poirier, S. (2020, January 17). H2O AutoML: Scalable Automatic Machine Learning. Proceedings of the 7th ICML Workshop on Automated Machine Learning, Vienna, Austria. Available online: https:\/\/www.automl.org\/wp-content\/uploads\/2020\/07\/AutoML_2020_paper_61.pdf?fbclid=IwAR2QaAJWDbgi1jIfnhK83x2g3hV6APfvTZoeUblcf4q44wxqT1z5oRTiEVo."},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Koh, J.C.O., Spangenberg, G., and Kant, S. (2021). Automated Machine Learning for High-Throughput Image-Based Plant Phenotyping. Remote Sens., 13.","DOI":"10.3390\/rs13050858"},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Jin, H., Song, Q., and Hu, X. (2019, January 4\u20138). Auto-Keras: An Efficient Neural Architecture Search System. Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Anchorage, AL, USA.","DOI":"10.1145\/3292500.3330648"},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Komer, B., Bergstra, J., and Eliasmith, C. (2014, January 6\u201312). Hyperopt-Sklearn: Automatic Hyperparameter Configuration for Scikit-Learn. Proceedings of the 13th Python in Science Conference, Austin, TX, USA.","DOI":"10.25080\/Majora-14bd3278-006"},{"key":"ref_37","unstructured":"FAO (2006). World Reference Base for Soil Resources, FAO. World Soil Resources Report 103."},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Poncet, A.M., Knappenberger, T., Brodbeck, C., Fogle, J.M., Shaw, J.N., and Ortiz, B.V. (2019). Multispectral UAS Data Accuracy for Different Radiometric Calibration Methods. Remote Sens., 11.","DOI":"10.3390\/rs11161917"},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"1462","DOI":"10.3390\/rs4051462","article-title":"Sensor Correction of a 6-Band Multispectral Imaging Sensor for UAV Remote Sensing","volume":"4","author":"Kelcey","year":"2012","journal-title":"Remote Sens."},{"key":"ref_40","unstructured":"Feurer, M., Eggensperger, K., Falkner, S., Lindauer, M., and Hutter, F. (2018, January 10\u201315). Practical Automated Machine Learning for the AutoML Challenge 2018. Proceedings of the International Workshop on Automatic Machine Learning at ICML, Stockholm, Sweden."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"55","DOI":"10.3846\/gac.2018.2023","article-title":"Modernization of the Estonian National GNSS Reference Station Network","volume":"44","author":"Metsar","year":"2018","journal-title":"Geod. Cartogr."},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"de Lima, R., Lang, M., Burnside, N., Peci\u00f1a, M., Arum\u00e4e, T., Laarmann, D., Ward, R., Vain, A., and Sepp, K. (2021). An Evaluation of the Effects of UAS Flight Parameters on Digital Aerial Photogrammetry Processing and Dense-Cloud Production Quality in a Scots Pine Forest. Remote Sens., 13.","DOI":"10.3390\/rs13061121"},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Toma\u0161t\u00edk, J., Mokro\u0161, M., Surov\u00fd, P., Grzn\u00e1rov\u00e1, A., and Mergani\u010d, J. (2019). UAV RTK\/PPK Method\u2014An Optimal Solution for Mapping Inaccessible Forested Areas?. Remote Sens., 11.","DOI":"10.3390\/rs11060721"},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Zhang, J., Huang, W., and Zhou, Q. (2014). Reflectance Variation within the In-Chlorophyll Centre Waveband for Robust Retrieval of Leaf Chlorophyll Content. PLoS ONE, 9.","DOI":"10.1371\/journal.pone.0110812"},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"4049","DOI":"10.1109\/JSTARS.2015.2400134","article-title":"Evaluation of Chlorophyll-Related Vegetation Indices Using Simulated Sentinel-2 Data for Estimation of Crop Fraction of Absorbed Photosynthetically Active Radiation","volume":"8","author":"Dong","year":"2015","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"286","DOI":"10.1016\/S0176-1617(11)81633-0","article-title":"Spectral Reflectance Changes Associated with Autumn Senescence of Aesculus hippocastanum L. and Acer platanoides L. Leaves. Spectral Features and Relation to Chlorophyll Estimation","volume":"143","author":"Gitelson","year":"1994","journal-title":"J. Plant Physiol."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"337","DOI":"10.1016\/j.rse.2003.12.013","article-title":"Hyperspectral vegetation indices and novel algorithms for predicting green LAI of crop canopies: Modeling and validation in the context of precision agriculture","volume":"90","author":"Haboudane","year":"2004","journal-title":"Remote Sens. Environ."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"229","DOI":"10.1080\/07038992.1996.10855178","article-title":"Evaluation of Vegetation Indices and a Modified Simple Ratio for Boreal Applications","volume":"22","author":"Chen","year":"1996","journal-title":"Can. J. Remote Sens."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"1230","DOI":"10.1016\/j.agrformet.2008.03.005","article-title":"Estimating chlorophyll content from hyperspectral vegetation indices: Modeling and validation","volume":"148","author":"Wu","year":"2008","journal-title":"Agric. For. Meteorol."},{"key":"ref_50","unstructured":"Merton, R., and Huntington, J. (1999, January 7\u201314). Early Simulation Results of the Aries-1 Satellite Sensor for Multi-Temporal Vege-tation Research Derived from Aviris. Proceedings of the Eighth Annual JPL, Orlando, FL, USA. Available online: http:\/\/www.eoc.csiro.au\/hswww\/jpl_99.htm."},{"key":"ref_51","unstructured":"Henebry, G., Vi\u00f1a, A., and Gitelson, A. (2020, October 22). The Wide Dynamic Range Vegetation Index and Its Potential Utility for Gap Analysis. Available online: https:\/\/digitalcommons.unl.edu\/natrespapers\/262."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"1211","DOI":"10.3390\/rs6021211","article-title":"The Generalized Difference Vegetation Index (GDVI) for Dryland Characterization","volume":"6","author":"Wu","year":"2014","journal-title":"Remote Sens."},{"key":"ref_53","doi-asserted-by":"crossref","unstructured":"Strong, C.J., Burnside, N.G., and Llewellyn, D. (2017). The potential of small-Unmanned Aircraft Systems for the rapid detection of threatened unimproved grassland communities using an Enhanced Normalized Difference Vegetation Index. PLoS ONE, 12.","DOI":"10.1371\/journal.pone.0186193"},{"key":"ref_54","first-page":"235","article-title":"Assessment of RapidEye vegetation indices for estimation of leaf area index and biomass in corn and soybean crops","volume":"34","author":"Kross","year":"2015","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_55","first-page":"512","article-title":"New Index for Crop Canopy Fresh Biomass Estimation","volume":"30","author":"Chen","year":"2010","journal-title":"Spectrosc. Spectr. Anal."},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"3999","DOI":"10.1080\/01431160310001654923","article-title":"Narrow band vegetation indices overcome the saturation problem in biomass estimation","volume":"25","author":"Mutanga","year":"2004","journal-title":"Int. J. Remote Sens."},{"key":"ref_57","doi-asserted-by":"crossref","unstructured":"Vasudevan, A., Kumar, D.A., and Bhuvaneswari, N.S. (2016, January 15\u201316). Precision farming using unmanned aerial and ground vehicles. Proceedings of the 2016 IEEE International Conference on Technological Innovations in ICT for Agriculture and Rural Development, TIAR, Chennai, India.","DOI":"10.1109\/TIAR.2016.7801229"},{"key":"ref_58","doi-asserted-by":"crossref","unstructured":"Ballester, C., Brinkhoff, J., Quayle, W.C., and Hornbuckle, J. (2019). Monitoring the Effects of Water Stress in Cotton using the Green Red Vegetation Index and Red Edge Ratio. Remote Sens., 11.","DOI":"10.3390\/rs11070873"},{"key":"ref_59","doi-asserted-by":"crossref","unstructured":"Feng, H., Tao, H., Zhao, C., Li, Z., and Yang, G. (2021). Comparison of UAV RGB Imagery and Hyperspectral Remote-Sensing Data for Monitoring Winter-Wheat Growth. Res. Sq.","DOI":"10.21203\/rs.3.rs-170131\/v1"},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"2934","DOI":"10.1109\/JSTARS.2019.2918487","article-title":"Determining Effective Meter-Scale Image Data and Spectral Vegetation Indices for Tropical Forest Tree Species Differentiation","volume":"12","author":"Cross","year":"2019","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"111","DOI":"10.1016\/S0034-4257(98)00046-7","article-title":"Remote Sensing of Chlorophyll a, Chlorophyll b, Chlorophyll a+b, and Total Carotenoid Content in Eucalyptus Leaves","volume":"66","author":"Datt","year":"1998","journal-title":"Remote Sens. Environ."},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"71","DOI":"10.1016\/0034-4257(90)90085-Z","article-title":"Calculating the vegetation index faster","volume":"34","author":"Crippen","year":"1990","journal-title":"Remote Sens. Environ."},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"289","DOI":"10.1016\/S0034-4257(96)00072-7","article-title":"Use of a green channel in remote sensing of global vegetation from EOS-MODIS","volume":"58","author":"Gitelson","year":"1996","journal-title":"Remote Sens. Environ."},{"key":"ref_64","doi-asserted-by":"crossref","first-page":"968","DOI":"10.2134\/agronj2005.0200","article-title":"Aerial Color Infrared Photography for Determining Early In-Season Nitrogen Requirements in Corn","volume":"98","author":"Sripada","year":"2006","journal-title":"Agron. J."},{"key":"ref_65","doi-asserted-by":"crossref","first-page":"931","DOI":"10.1080\/01431160500196398","article-title":"Determination of green herbage ratio in grasslands using spectral reflectance. Methods and ground measurements","volume":"28","author":"Gianelle","year":"2007","journal-title":"Int. J. Remote Sens."},{"key":"ref_66","unstructured":"Rouse, J.W., Hass, R.H., Schell, J.A., Deering, D.W., and Harlan, J.C. (1974). Monitoring the Vernal Advancement and Retrogradation (Green Wave Effect) of Natural Vegetation, Texas A&M University. Final Report, RSC 1978-4."},{"key":"ref_67","doi-asserted-by":"crossref","first-page":"375","DOI":"10.1016\/0034-4257(94)00114-3","article-title":"Estimating PAR absorbed by vegetation from bidirectional reflectance measurements","volume":"51","author":"Roujean","year":"1995","journal-title":"Remote Sens. Environ."},{"key":"ref_68","doi-asserted-by":"crossref","first-page":"337","DOI":"10.1016\/S0034-4257(02)00010-X","article-title":"Relationships between leaf pigment content and spectral reflectance across a wide range of species, leaf structures and developmental stages","volume":"81","author":"Sims","year":"2002","journal-title":"Remote Sens. Environ."},{"key":"ref_69","doi-asserted-by":"crossref","first-page":"663","DOI":"10.2307\/1936256","article-title":"Derivation of Leaf-Area Index from Quality of Light on the Forest Floor","volume":"50","author":"Jordan","year":"1969","journal-title":"Ecology"},{"key":"ref_70","doi-asserted-by":"crossref","first-page":"165","DOI":"10.1078\/0176-1617-01176","article-title":"Wide Dynamic Range Vegetation Index for Remote Quantification of Biophysical Characteristics of Vegetation","volume":"161","author":"Gitelson","year":"2004","journal-title":"J. Plant Physiol."},{"key":"ref_71","doi-asserted-by":"crossref","first-page":"1493","DOI":"10.1162\/neco.1997.9.7.1493","article-title":"Dimension Reduction by Local Principal Component Analysis","volume":"9","author":"Kambhatla","year":"1997","journal-title":"Neural Comput."},{"key":"ref_72","unstructured":"R Core Team (2020). R: A Language and Environment for Statistical Computing, R Foundation for Statistical Computing."},{"key":"ref_73","first-page":"1","article-title":"FactoMineR: AnRPackage for Multivariate Analysis","volume":"25","author":"Josse","year":"2008","journal-title":"J. Stat. Softw."},{"key":"ref_74","unstructured":"ESRI (2016). ArcGIS PRO: Essential Workflows, ESRI. Available online: https:\/\/community.esri.com\/t5\/esritraining-documents\/arcgis-pro-essential-workflows-course-resources\/ta-p\/914710."},{"key":"ref_75","unstructured":"Feurer, M., Eggensperger, K., Falkner, S., Lindauer, M., and Hutter, F. (2020). Auto-Sklearn 2.0: The Next Generation. arXiv."},{"key":"ref_76","first-page":"2825","article-title":"Scikit-Learn: Machine Learning in Python","volume":"12","author":"Pedregosa","year":"2011","journal-title":"J. Mach. Learn. Res."},{"key":"ref_77","doi-asserted-by":"crossref","unstructured":"Hutter, F., Hoos, H.H., and Leyton-Brown, K. (2011, January 17\u201321). Sequential Model-Based Optimization for General Algorithm Configuration. Proceedings of the Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Rome, Italy.","DOI":"10.1007\/978-3-642-25566-3_40"},{"key":"ref_78","doi-asserted-by":"crossref","first-page":"293","DOI":"10.1023\/A:1018628609742","article-title":"Least Squares Support Vector Machine Classifiers","volume":"9","author":"Suykens","year":"1999","journal-title":"Neural Process. Lett."},{"key":"ref_79","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1023\/A:1010933404324","article-title":"Random Forests","volume":"45","author":"Breiman","year":"2001","journal-title":"Mach. Learn."},{"key":"ref_80","unstructured":"Olson, R.S., Urbanowicz, R.J., Andrews, P.C., Lavender, N.A., Kidd, L.C., and Moore, J.H. (April, January 30). Automating Biomedical Data Science Through Tree-Based Pipeline Optimization. Proceedings of the Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Porto, Portugal."},{"key":"ref_81","doi-asserted-by":"crossref","unstructured":"Feurer, M., Springenberg, J.T., and Hutter, F. (2015, January 25\u201330). Initializing Bayesian Hyperparameter Optimization via Me-ta-Learning. Proceedings of the Proceedings of the National Conference on Artificial Intelligence, Austin, TX, USA. Available online: https:\/\/ojs.aaai.org\/index.php\/AAAI\/article\/view\/9354.","DOI":"10.1609\/aaai.v29i1.9354"},{"key":"ref_82","unstructured":"Franceschi, L., Frasconi, P., Salzo, S., Grazzi, R., and Pontil, M. (2018). Bilevel Programming for Hyperparameter Opti-mization and Meta-Learning. arXiv."},{"key":"ref_83","doi-asserted-by":"crossref","first-page":"277","DOI":"10.1016\/j.isprsjprs.2017.06.001","article-title":"A review of supervised object-based land-cover image classification","volume":"130","author":"Ma","year":"2017","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_84","doi-asserted-by":"crossref","first-page":"17","DOI":"10.1145\/2935694.2935698","article-title":"Model Selection Management Systems: The Next Frontier of Advanced Analytics","volume":"44","author":"Kumar","year":"2016","journal-title":"ACM SIGMOD Rec."},{"key":"ref_85","doi-asserted-by":"crossref","first-page":"1075","DOI":"10.21105\/joss.01075","article-title":"Yellowbrick: Visualizing the Scikit-Learn Model Selection Process","volume":"4","author":"Bengfort","year":"2019","journal-title":"J. Open Source Softw."},{"key":"ref_86","doi-asserted-by":"crossref","first-page":"2526","DOI":"10.1109\/TVT.2019.2893615","article-title":"Machine Learning Inspired Sound-Based Amateur Drone Detection for Public Safety Applications","volume":"68","author":"Anwar","year":"2019","journal-title":"IEEE Trans. Veh. Technol."},{"key":"ref_87","doi-asserted-by":"crossref","first-page":"861","DOI":"10.1016\/j.patrec.2005.10.010","article-title":"An introduction to ROC analysis","volume":"27","author":"Fawcett","year":"2005","journal-title":"Pattern Recognit. Lett."},{"key":"ref_88","doi-asserted-by":"crossref","first-page":"157","DOI":"10.1002\/sim.2929","article-title":"Evaluating the added predictive ability of a new marker: From area under the ROC curve to reclassification and beyond","volume":"27","author":"Pencina","year":"2007","journal-title":"Stat. Med."},{"key":"ref_89","first-page":"1","article-title":"An application of machine learning to haematological diagnosis","volume":"8","author":"Kukar","year":"2018","journal-title":"Sci. Rep."},{"key":"ref_90","doi-asserted-by":"crossref","first-page":"427","DOI":"10.1016\/j.ipm.2009.03.002","article-title":"A systematic analysis of performance measures for classification tasks","volume":"45","author":"Sokolova","year":"2009","journal-title":"Inf. Process. Manag."},{"key":"ref_91","doi-asserted-by":"crossref","unstructured":"Boyd, K., Eng, K.H., and Page, C.D. (2013, January 23\u201327). Area under the Precision-Recall Curve: Point Estimates and Confidence In-tervals. Proceedings of the Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Prague, Czech Republic.","DOI":"10.1007\/978-3-642-40994-3_55"},{"key":"ref_92","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/s12864-019-6413-7","article-title":"The advantages of the Matthews correlation coefficient (MCC) over F1 score and accuracy in binary classification evaluation","volume":"21","author":"Chicco","year":"2020","journal-title":"BMC Genom."},{"key":"ref_93","doi-asserted-by":"crossref","unstructured":"Wang, Q., Ming, Y., Jin, Z., Shen, Q., Liu, D., Smith, M.J., Veeramachaneni, K., and Qu, H. (2019). ATMSeer: Increasing Transparency and Controllability in Automated Machine Learning. arXiv.","DOI":"10.1145\/3290605.3300911"},{"key":"ref_94","doi-asserted-by":"crossref","first-page":"390","DOI":"10.1109\/TVCG.2020.3030361","article-title":"PipelineProfiler: A Visual Analytics Tool for the Exploration of AutoML Pipelines","volume":"27","author":"Ono","year":"2021","journal-title":"IEEE Trans. Vis. Comput. Graph."},{"key":"ref_95","doi-asserted-by":"crossref","unstructured":"Serpico, S.B., D\u2019Inca, M., Melgani, F., and Moser, G. (2003, January 13). Comparison of Feature Reduction Techniques for Classification of Hyperspectral Remote Sensing Data. Proceedings of the Image and Signal Processing for Remote Sensing VIII, Crete, Greece.","DOI":"10.1117\/12.463524"},{"key":"ref_96","doi-asserted-by":"crossref","unstructured":"Saito, T., and Rehmsmeier, M. (2015). The Precision-Recall Plot Is More Informative than the ROC Plot When Evaluating Binary Classifiers on Imbalanced Datasets. PLoS ONE, 10.","DOI":"10.1371\/journal.pone.0118432"},{"key":"ref_97","doi-asserted-by":"crossref","first-page":"179","DOI":"10.1111\/j.1469-1809.1936.tb02137.x","article-title":"The use of multiple measurements in taxonomic problems","volume":"7","author":"Fisher","year":"1936","journal-title":"Ann. Eugen."},{"key":"ref_98","doi-asserted-by":"crossref","first-page":"3","DOI":"10.1007\/s10994-006-6226-1","article-title":"Extremely randomized trees","volume":"63","author":"Geurts","year":"2006","journal-title":"Mach. Learn."},{"key":"ref_99","doi-asserted-by":"crossref","unstructured":"Samaras, S., Diamantidou, E., Ataloglou, D., Sakellariou, N., Vafeiadis, A., Magoulianitis, V., Lalas, A., Dimou, A., Zarpalas, D., and Votis, K. (2019). Deep Learning on Multi Sensor Data for Counter UAV Applications\u2014A Systematic Review. Sensors, 19.","DOI":"10.3390\/s19224837"},{"key":"ref_100","doi-asserted-by":"crossref","unstructured":"David, L.C., and Ballado, A.J. (2016, January 25\u201327). Vegetation indices and textures in object-based weed detection from UAV imagery. Proceedings of the 6th IEEE International Conference on Control System, Computing and Engineering, ICCSCE, Penang, Malaysia.","DOI":"10.1109\/ICCSCE.2016.7893584"},{"key":"ref_101","doi-asserted-by":"crossref","unstructured":"Torres-S\u00e1nchez, J., Lopez-Granados, F., De Castro, A.I., and Pe\u00f1a-Barragan, J.M. (2013). Configuration and Specifications of an Unmanned Aerial Vehicle (UAV) for Early Site Specific Weed Management. PLoS ONE, 8.","DOI":"10.1371\/journal.pone.0058210"},{"key":"ref_102","doi-asserted-by":"crossref","first-page":"112","DOI":"10.1016\/j.eja.2015.07.004","article-title":"Low-altitude, high-resolution aerial imaging systems for row and field crop phenotyping: A review","volume":"70","author":"Sankaran","year":"2015","journal-title":"Eur. J. Agron."},{"key":"ref_103","doi-asserted-by":"crossref","unstructured":"Chawade, A., Van Ham, J., Blomquist, H., Bagge, O., Alexandersson, E., and Ortiz, R. (2019). High-Throughput Field-Phenotyping Tools for Plant Breeding and Precision Agriculture. Agronomy, 9.","DOI":"10.3390\/agronomy9050258"},{"key":"ref_104","doi-asserted-by":"crossref","unstructured":"Young, S.N. (2019). A Framework for Evaluating Field-Based, High-Throughput Phenotyping Systems: A Meta-Analysis. Sensors, 19.","DOI":"10.3390\/s19163582"},{"key":"ref_105","doi-asserted-by":"crossref","first-page":"1137","DOI":"10.1007\/s10514-018-9790-x","article-title":"UAV route planning for active disease classification","volume":"43","author":"Vivaldini","year":"2018","journal-title":"Auton. Robot."},{"key":"ref_106","doi-asserted-by":"crossref","unstructured":"Zhang, X., Han, L., Dong, Y., Shi, Y., Huang, W., Han, L., Gonz\u00e1lez-Moreno, P., Ma, H., Ye, H., and Sobeih, T. (2019). A Deep Learning-Based Approach for Automated Yellow Rust Disease Detection from High-Resolution Hyperspectral UAV Images. Remote Sens., 11.","DOI":"10.3390\/rs11131554"},{"key":"ref_107","doi-asserted-by":"crossref","first-page":"105979","DOI":"10.1016\/j.ecolind.2019.105979","article-title":"Fine scale plant community assessment in coastal meadows using UAV based multispectral data","volume":"111","author":"Villoslada","year":"2020","journal-title":"Ecol. Indic."},{"key":"ref_108","doi-asserted-by":"crossref","first-page":"645","DOI":"10.1111\/j.1654-1103.2007.tb02578.x","article-title":"Use of Vegetation Classification and Plant Indicators to Assess Grazing Abandonment in Estonian Coastal Wetlands","volume":"18","author":"Burnside","year":"2007","journal-title":"J. Veg. Sci."},{"key":"ref_109","first-page":"1023","article-title":"UAV multispectral survey to map soil and crop for precision farming applications","volume":"XLI-B1","author":"Sona","year":"2016","journal-title":"ISPRS Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci."},{"key":"ref_110","doi-asserted-by":"crossref","unstructured":"Kwak, G.-H., and Park, N.-W. (2019). Impact of Texture Information on Crop Classification with Machine Learning and UAV Images. Appl. Sci., 9.","DOI":"10.3390\/app9040643"},{"key":"ref_111","doi-asserted-by":"crossref","unstructured":"Yang, M.-D., Tseng, H.-H., Hsu, Y.-C., and Tsai, H.P. (2020). Semantic Segmentation Using Deep Learning with Vegetation Indices for Rice Lodging Identification in Multi-date UAV Visible Images. Remote Sens., 12.","DOI":"10.3390\/rs12040633"},{"key":"ref_112","doi-asserted-by":"crossref","first-page":"105817","DOI":"10.1016\/j.compag.2020.105817","article-title":"Adaptive autonomous UAV scouting for rice lodging assessment using edge computing with deep learning EDANet","volume":"179","author":"Yang","year":"2020","journal-title":"Comput. Electron. Agric."},{"key":"ref_113","doi-asserted-by":"crossref","unstructured":"Najafi, P., Feizizadeh, B., and Navid, H. (2021). A Comparative Approach of Fuzzy Object Based Image Analysis and Machine Learning Techniques Which Are Applied to Crop Residue Cover Mapping by Using Sentinel-2 Satellite and UAV Imagery. Remote Sens., 13.","DOI":"10.3390\/rs13050937"},{"key":"ref_114","doi-asserted-by":"crossref","first-page":"259","DOI":"10.1016\/j.rse.2011.11.020","article-title":"A comparison of pixel-based and object-based image analysis with selected machine learning algorithms for the classification of agricultural landscapes using SPOT-5 HRG imagery","volume":"118","author":"Duro","year":"2012","journal-title":"Remote Sens. Environ."},{"key":"ref_115","doi-asserted-by":"crossref","first-page":"26","DOI":"10.11108\/kagis.2012.15.4.026","article-title":"A Study on Object-Based Image Analysis Methods for Land Cover Classification in Agricultural Areas","volume":"15","author":"Kim","year":"2012","journal-title":"J. Korean Assoc. Geogr. Inf. Stud."},{"key":"ref_116","doi-asserted-by":"crossref","first-page":"124","DOI":"10.1016\/j.landusepol.2018.04.053","article-title":"Effects of no-tillage on agricultural land values in Brazil","volume":"76","author":"Telles","year":"2018","journal-title":"Land Use Policy"},{"key":"ref_117","doi-asserted-by":"crossref","unstructured":"Yeom, J., Jung, J., Chang, A., Ashapure, A., Maeda, M., Maeda, A., and Landivar, J. (2019). Comparison of Vegetation Indices Derived from UAV Data for Differentiation of Tillage Effects in Agriculture. Remote Sens., 11.","DOI":"10.3390\/rs11131548"},{"key":"ref_118","doi-asserted-by":"crossref","unstructured":"Bisong, E. (2019). Google AutoML: Cloud Vision. Building Machine Learning and Deep Learning Models on Google Cloud Platform, Springer Nature.","DOI":"10.1007\/978-1-4842-4470-8"}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/13\/16\/3190\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T06:44:37Z","timestamp":1760165077000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/13\/16\/3190"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,8,12]]},"references-count":118,"journal-issue":{"issue":"16","published-online":{"date-parts":[[2021,8]]}},"alternative-id":["rs13163190"],"URL":"https:\/\/doi.org\/10.3390\/rs13163190","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,8,12]]}}}