{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,4]],"date-time":"2026-07-04T00:35:22Z","timestamp":1783125322105,"version":"3.54.6"},"reference-count":70,"publisher":"MDPI AG","issue":"13","license":[{"start":{"date-parts":[[2020,7,3]],"date-time":"2020-07-03T00:00:00Z","timestamp":1593734400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/100000199","name":"U.S. Department of Agriculture","doi-asserted-by":"publisher","award":["1011130"],"award-info":[{"award-number":["1011130"]}],"id":[{"id":"10.13039\/100000199","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Nebraska Research Initiative (NRI) Collaboration Initiative Seed Grant","award":["2132250011"],"award-info":[{"award-number":["2132250011"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Mid- to late-season weeds that escape from the routine early-season weed management threaten agricultural production by creating a large number of seeds for several future growing seasons. Rapid and accurate detection of weed patches in field is the first step of site-specific weed management. In this study, object detection-based convolutional neural network models were trained and evaluated over low-altitude unmanned aerial vehicle (UAV) imagery for mid- to late-season weed detection in soybean fields. The performance of two object detection models, Faster RCNN and the Single Shot Detector (SSD), were evaluated and compared in terms of weed detection performance using mean Intersection over Union (IoU) and inference speed. It was found that the Faster RCNN model with 200 box proposals had similar good weed detection performance to the SSD model in terms of precision, recall, f1 score, and IoU, as well as a similar inference time. The precision, recall, f1 score and IoU were 0.65, 0.68, 0.66 and 0.85 for Faster RCNN with 200 proposals, and 0.66, 0.68, 0.67 and 0.84 for SSD, respectively. However, the optimal confidence threshold of the SSD model was found to be much lower than that of the Faster RCNN model, which indicated that SSD might have lower generalization performance than Faster RCNN for mid- to late-season weed detection in soybean fields using UAV imagery. The performance of the object detection model was also compared with patch-based CNN model. The Faster RCNN model yielded a better weed detection performance than the patch-based CNN with and without overlap. The inference time of Faster RCNN was similar to patch-based CNN without overlap, but significantly less than patch-based CNN with overlap. Hence, Faster RCNN was found to be the best model in terms of weed detection performance and inference time among the different models compared in this study. This work is important in understanding the potential and identifying the algorithms for an on-farm, near real-time weed detection and management.<\/jats:p>","DOI":"10.3390\/rs12132136","type":"journal-article","created":{"date-parts":[[2020,7,3]],"date-time":"2020-07-03T06:51:20Z","timestamp":1593759080000},"page":"2136","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":180,"title":["Comparison of Object Detection and Patch-Based Classification Deep Learning Models on Mid- to Late-Season Weed Detection in UAV Imagery"],"prefix":"10.3390","volume":"12","author":[{"given":"Arun Narenthiran","family":"Veeranampalayam Sivakumar","sequence":"first","affiliation":[{"name":"Department of Biological Systems Engineering, University of Nebraska-Lincoln, Lincoln, NE 68583, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9017-7808","authenticated-orcid":false,"given":"Jiating","family":"Li","sequence":"additional","affiliation":[{"name":"Department of Biological Systems Engineering, University of Nebraska-Lincoln, Lincoln, NE 68583, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Stephen","family":"Scott","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Engineering, University of Nebraska-Lincoln, Lincoln, NE 68588, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7836-298X","authenticated-orcid":false,"given":"Eric","family":"Psota","sequence":"additional","affiliation":[{"name":"Department of Electrical and Computer Engineering, University of Nebraska-Lincoln, Lincoln, NE 68588, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8599-4996","authenticated-orcid":false,"given":"Amit","family":"J. Jhala","sequence":"additional","affiliation":[{"name":"Department of Agronomy and Horticulture, University of Nebraska-Lincoln, Lincoln, NE 68583, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Joe D.","family":"Luck","sequence":"additional","affiliation":[{"name":"Department of Biological Systems Engineering, University of Nebraska-Lincoln, Lincoln, NE 68583, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3964-2855","authenticated-orcid":false,"given":"Yeyin","family":"Shi","sequence":"additional","affiliation":[{"name":"Department of Biological Systems Engineering, University of Nebraska-Lincoln, Lincoln, NE 68583, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2020,7,3]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"314","DOI":"10.1016\/j.compag.2017.10.027","article-title":"Weed detection in soybean crops using ConvNets","volume":"143","author":"Pistori","year":"2017","journal-title":"Comput. Electron. Agric."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"477","DOI":"10.1007\/s11119-004-5321-1","article-title":"A Review on Remote Sensing of Weeds in Agriculture","volume":"5","author":"Thorp","year":"2004","journal-title":"Precis. Agric."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"171","DOI":"10.1007\/s10343-008-0195-1","article-title":"Precision farming for weed management: Techniques","volume":"60","author":"Weis","year":"2008","journal-title":"Gesunde Pflanz."},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Christensen, S., S\u00d8gaard, H.T., Kudsk, P., N\u00d8rremark, M., Lund, I., Nadimi, E.S., and J\u00d8rgensen, R. (2009). Site-specific weed control technologies. Weed Res.","DOI":"10.1111\/j.1365-3180.2009.00696.x"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"113","DOI":"10.1016\/S0168-1699(02)00096-0","article-title":"Precision agriculture\u2014A worldwide overview","volume":"36","author":"Zhang","year":"2002","journal-title":"Comput. Electron. Agric."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"O\u2019Donovan, J.T., De St. Remy, E.A., O\u2019Sullivan, P.A., Dew, D.A., and Sharma, A.K. (1985). Influence of the Relative Time of Emergence of Wild Oat ( Avena fatua ) on Yield Loss of Barley ( Hordeum vulgare ) and Wheat ( Triticum aestivum). Weed Sci.","DOI":"10.1017\/S0043174500082722"},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Swanton, C.J., Mahoney, K.J., Chandler, K., and Gulden, R.H. (2008). Integrated Weed Management: Knowledge-Based Weed Management Systems. Weed Sci.","DOI":"10.1614\/WS-07-126.1"},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"JUDGE, C.A., NEAL, J.C., and DERR, J.F. (2005). Response of Japanese Stiltgrass (Microstegium vimineum) to Application Timing, Rate, and Frequency of Postemergence Herbicides 1. Weed Technol.","DOI":"10.1614\/WT-04-272R.1"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"57","DOI":"10.1016\/j.cropro.2012.03.010","article-title":"Ecology and management of weeds under conservation agriculture: A review","volume":"38","author":"Chauhan","year":"2012","journal-title":"Crop. Prot."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"De Castro, A.I., Torres-S\u00e1nchez, J., Pe\u00f1a, J.M., Jim\u00e9nez-Brenes, F.M., Csillik, O., and L\u00f3pez-Granados, F. (2018). An automatic random forest-OBIA algorithm for early weed mapping between and within crop rows using UAV imagery. Remote Sens., 10.","DOI":"10.3390\/rs10020285"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"259","DOI":"10.1111\/wre.12307","article-title":"Is the current state of the art of weed monitoring suitable for site-specific weed management in arable crops?","volume":"58","author":"Dorado","year":"2018","journal-title":"Weed Res."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"L\u00f3pez-Granados, F. (2011). Weed detection for site-specific weed management: Mapping and real-time approaches. Weed Res.","DOI":"10.1111\/j.1365-3180.2010.00829.x"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Barroso, J., Fern\u00e0ndez-Quintanilla, C., Ruiz, D., Hernaiz, P., and Rew, L.J. (2004). Spatial stability of Avena sterilis ssp. ludoviciana populations under annual applications of low rates of imazamethabenz. Weed Res.","DOI":"10.1111\/j.1365-3180.2004.00389.x"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Koger, C.H., Shaw, D.R., Watson, C.E., and Reddy, K.N. (2003). Detecting Late-Season Weed Infestations in Soybean (Glycine max) 1. Weed Technol.","DOI":"10.1614\/WT02-122"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"De Castro, A.I., Jurado-Exp\u00f3sito, M., Pe\u00f1a-Barrag\u00e1n, J.M., and L\u00f3pez-Granados, F. (2012). Airborne multi-spectral imagery for mapping cruciferous weeds in cereal and legume crops. Precis. Agric.","DOI":"10.1007\/s11119-011-9247-0"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"De Castro, A.I., L\u00f3pez-Granados, F., and Jurado-Exp\u00f3sito, M. (2013). Broad-scale cruciferous weed patch classification in winter wheat using QuickBird imagery for in-season site-specific control. Precis. Agric.","DOI":"10.1007\/s11119-013-9304-y"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Castillejo-Gonz\u00e1lez, I.L., L\u00f3pez-Granados, F., Garc\u00eda-Ferrer, A., Pe\u00f1a-Barrag\u00e1n, J.M., Jurado-Exp\u00f3sito, M., de la Orden, M.S., and Gonz\u00e1lez-Audicana, M. (2009). Object- and pixel-based analysis for mapping crops and their agro-environmental associated measures using QuickBird imagery. Comput. Electron. Agric.","DOI":"10.1016\/j.compag.2009.06.004"},{"key":"ref_18","unstructured":"Meyer, G.E., Mehta, T., Kocher, M.F., Mortensen, D.A., and Samal, A. (1998). Textural imaging and discriminant analysis for distinguishing weeds for spot spraying. Trans. Am. Soc. Agric. Eng."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"411","DOI":"10.13031\/2013.2723","article-title":"Classification of weed species using color texture features and discriminant analysis","volume":"43","author":"Burks","year":"2000","journal-title":"Trans. Am. Soc. Agric. Eng."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"226","DOI":"10.1016\/j.compag.2019.02.005","article-title":"A review on weed detection using ground-based machine vision and image processing techniques","volume":"158","author":"Wang","year":"2019","journal-title":"Comput. Electron. Agric."},{"key":"ref_21","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_22","doi-asserted-by":"crossref","unstructured":"Rasmussen, J., Nielsen, J., Streibig, J.C., Jensen, J.E., Pedersen, K.S., and Olsen, S.I. (2019). Pre-harvest weed mapping of Cirsium arvense in wheat and barley with off-the-shelf UAVs. Precis. Agric.","DOI":"10.1007\/s11119-018-09625-7"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Casa, R., Pascucci, S., Pignatti, S., Palombo, A., Nanni, U., Harfouche, A., Laura, L., Di Rocco, M., and Fantozzi, P. (2019, January 8\u201311). UAV-based hyperspectral imaging for weed discrimination in maize. Proceedings of the Precision Agriculture 2019\u2014Papers Presented at the 12th European Conference on Precision Agriculture, ECPA 2019, Montpellier, France.","DOI":"10.3920\/978-90-8686-888-9_45"},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"S\u00e1nchez-Sastre, L.F., Casterad, M.A., Guill\u00e9n, M., Ruiz-Potosme, N.M., Veiga, N.M.S.A., da Navas-Gracia, L.M., and Mart\u00edn-Ramos, P. (2020). UAV Detection of Sinapis arvensis Infestation in Alfalfa Plots Using Simple Vegetation Indices from Conventional Digital Cameras. AgriEngineering, 2.","DOI":"10.3390\/agriengineering2020012"},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Pe\u00f1a-Barrag\u00e1n, J.M., L\u00f3pez-Granados, F., Jurado-Exp\u00f3sito, M., and Garc\u00eda-Torres, L. (2006). Spectral discrimination of Ridolfia segetum and sunflower as affected by phenological stage. Weed Res.","DOI":"10.1111\/j.1365-3180.2006.00488.x"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Gray, C.J., Shaw, D.R., Gerard, P.D., and Bruce, L.M. (2008). Utility of Multispectral Imagery for Soybean and Weed Species Differentiation. Weed Technol.","DOI":"10.1614\/WT-07-116.1"},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Martin, M.P., Barreto, L., Ria\u00f1O, D., Fernandez-Quintanilla, C., and Vaughan, P. (2011). Assessing the potential of hyperspectral remote sensing for the discrimination of grassweeds in winter cereal crops. Int. J. Remote Sens.","DOI":"10.1080\/01431160903439874"},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"De Castro, A.I., Jurado-Exp\u00f3sito, M., G\u00f3mez-Casero, M.T., and L\u00f3pez-Granados, F. (2012). Applying neural networks to hyperspectral and multispectral field data for discrimination of cruciferous weeds in winter crops. Sci. World J.","DOI":"10.1100\/2012\/630390"},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Pe\u00f1a, J.M., Torres-S\u00e1nchez, J., de Castro, A.I., Kelly, M., and L\u00f3pez-Granados, F. (2013). Weed Mapping in Early-Season Maize Fields Using Object-Based Analysis of Unmanned Aerial Vehicle (UAV) Images. PLoS ONE, 8.","DOI":"10.1371\/journal.pone.0077151"},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Torres-S\u00e1nchez, J., L\u00f3pez-Granados, F., De Castro, A.I., and Pe\u00f1a-Barrag\u00e1n, 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_31","doi-asserted-by":"crossref","unstructured":"Torres-S\u00e1nchez, J., Pe\u00f1a, J.M., de Castro, A.I., and L\u00f3pez-Granados, F. (2014). Multi-temporal mapping of the vegetation fraction in early-season wheat fields using images from UAV. Comput. Electron. Agric.","DOI":"10.1016\/j.compag.2014.02.009"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"533","DOI":"10.1016\/j.asoc.2015.08.027","article-title":"A semi-supervised system for weed mapping in sunflower crops using unmanned aerial vehicles and a crop row detection method","volume":"37","year":"2015","journal-title":"Appl. Soft Comput."},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Castaldi, F., Pelosi, F., Pascucci, S., and Casa, R. (2017). Assessing the potential of images from unmanned aerial vehicles (UAV) to support herbicide patch spraying in maize. Precis. Agric.","DOI":"10.1007\/s11119-016-9468-3"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"183","DOI":"10.1007\/s11119-015-9415-8","article-title":"Early season weed mapping in sunflower using UAV technology: Variability of herbicide treatment maps against weed thresholds","volume":"17","year":"2016","journal-title":"Precis. Agric."},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Liu, D., and Xia, F. (2010). Assessing object-based classification: Advantages and limitations Assessing object-based classification: Advantages and limitations. Remote Sens. Lett.","DOI":"10.1080\/01431161003743173"},{"key":"ref_36","unstructured":"Krizhevsky, A., Sutskever, I., and Hinton, G.E. (2012, January 3\u20136). ImageNet classification with deep convolutional neural networks. Proceedings of the Advances in Neural Information Processing Systems, Lake Tahoe, NV, USA."},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Steen, K., Christiansen, P., Karstoft, H., J\u00f8rgensen, R., Steen, K.A., Christiansen, P., Karstoft, H., and J\u00f8rgensen, R.N. (2016). Using Deep Learning to Challenge Safety Standard for Highly Autonomous Machines in Agriculture. J. Imaging, 2.","DOI":"10.3390\/jimaging2010006"},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"734","DOI":"10.1007\/s40333-016-0049-0","article-title":"Modeling spatio-temporal distribution of soil moisture by deep learning-based cellular automata model","volume":"8","author":"Song","year":"2016","journal-title":"J. Arid Land"},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"1419","DOI":"10.3389\/fpls.2016.01419","article-title":"Using Deep Learning for Image-Based Plant Disease Detection","volume":"7","author":"Mohanty","year":"2016","journal-title":"Front. Plant. Sci."},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Kuwata, K., and Shibasaki, R. (2015, January 26\u201331). Estimating crop yields with deep learning and remotely sensed data. Proceedings of the 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), Milan, Italy.","DOI":"10.1109\/IGARSS.2015.7325900"},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Rahnemoonfar, M., Sheppard, C., Rahnemoonfar, M., and Sheppard, C. (2017). Deep Count: Fruit Counting Based on Deep Simulated Learning. Sensors, 17.","DOI":"10.3390\/s17040905"},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Andrea, C.-C., Mauricio Daniel, B.B., and Jose Misael, J.B. (2017, January 16\u201320). Precise weed and maize classification through convolutional neuronal networks. Proceedings of the 2017 IEEE Second Ecuador Technical Chapters Meeting (ETCM), Salinas, Ecuador.","DOI":"10.1109\/ETCM.2017.8247469"},{"key":"ref_43","unstructured":"Dyrmann, M., Mortensen, A., Midtiby, H., and J\u00f8rgensen, R. (2016, January 26\u201329). Pixel-wise classification of weeds and crops in images by using a fully convolutional neural network. Proceedings of the International Conference on Agricultural Engineering, Aarhus, Denmark."},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Milioto, A., Lottes, P., and Stachniss, C. (2018, January 21\u201325). Real-Time Semantic Segmentation of Crop and Weed for Precision Agriculture Robots Leveraging Background Knowledge in CNNs. Proceedings of the 2018 IEEE International Conference on Robotics and Automation (ICRA), Brisbane, Australia.","DOI":"10.1109\/ICRA.2018.8460962"},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"2870","DOI":"10.1109\/LRA.2018.2846289","article-title":"Fully Convolutional Networks With Sequential Information for Robust Crop and Weed Detection in Precision Farming","volume":"3","author":"Lottes","year":"2018","journal-title":"IEEE Robot. Autom. Lett."},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"Lottes, P., Khanna, R., Pfeifer, J., Siegwart, R., and Stachniss, C. (June, January 29). UAV-based crop and weed classification for smart farming. Proceedings of the 2017 IEEE International Conference on Robotics and Automation (ICRA), Singapore.","DOI":"10.1109\/ICRA.2017.7989347"},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"20","DOI":"10.1002\/rob.21901","article-title":"Robust joint stem detection and crop-weed classification using image sequences for plant-specific treatment in precision farming","volume":"37","author":"Lottes","year":"2020","journal-title":"J. Field Robot."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"588","DOI":"10.1109\/LRA.2017.2774979","article-title":"WeedNet: Dense Semantic Weed Classification Using Multispectral Images and MAV for Smart Farming","volume":"3","author":"Sa","year":"2018","journal-title":"IEEE Robot. Autom. Lett."},{"key":"ref_49","doi-asserted-by":"crossref","unstructured":"Sa, I., Popovi\u0107, M., Khanna, R., Chen, Z., Lottes, P., Liebisch, F., Nieto, J., Stachniss, C., Walter, A., and Siegwart, R. (2018). WeedMap: A Large-Scale Semantic Weed Mapping Framework Using Aerial Multispectral Imaging and Deep Neural Network for Precision Farming. Remote Sens., 10.","DOI":"10.3390\/rs10091423"},{"key":"ref_50","first-page":"176","article-title":"Deep Learning Based Classification System for Identifying Weeds Using High-Resolution UAV Imagery","volume":"Volume 857","author":"Bah","year":"2019","journal-title":"Intelligent Computing. SAI 2018"},{"key":"ref_51","doi-asserted-by":"crossref","unstructured":"Huang, H., Deng, J., Lan, Y., Yang, A., Deng, X., and Zhang, L. (2018). A fully convolutional network for weed mapping of unmanned aerial vehicle (UAV) imagery. PLoS ONE, 13.","DOI":"10.1371\/journal.pone.0196302"},{"key":"ref_52","doi-asserted-by":"crossref","unstructured":"Yu, J., Schumann, A.W., Cao, Z., Sharpe, S.M., and Boyd, N.S. (2019). Weed Detection in Perennial Ryegrass With Deep Learning Convolutional Neural Network. Front. Plant Sci., 10.","DOI":"10.3389\/fpls.2019.01422"},{"key":"ref_53","unstructured":"Tzutalin LabelImg (2020, June 30). LabelImg 2015. Available online: https:\/\/github.com\/tzutalin\/labelImg."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"2278","DOI":"10.1109\/5.726791","article-title":"Gradient-based learning applied to document recognition","volume":"86","author":"LeCun","year":"1998","journal-title":"Proc. IEEE"},{"key":"ref_55","doi-asserted-by":"crossref","unstructured":"Torrey, L., and Shavlik, J. (2010). Transfer learning. Handbook of Research on Machine Learning Applications and Trends: Algorithms, Methods, and Techniques, IGI Global.","DOI":"10.4018\/978-1-60566-766-9.ch011"},{"key":"ref_56","doi-asserted-by":"crossref","unstructured":"Karimi, Y., Prasher, S.O., McNairn, H., Bonnell, R.B., Dutilleul, P., and Goel, P.K. (2005). Classification accuracy of discriminant analysis, artificial neural networks, and decision trees for weed and nitrogen stress detection in corn. Trans. Am. Soc. Agric. Eng.","DOI":"10.13031\/2013.18490"},{"key":"ref_57","doi-asserted-by":"crossref","unstructured":"Sandler, M., Howard, A., Zhu, M., Zhmoginov, A., and Chen, L.C. (2018, January 18\u201323). MobileNetV2: Inverted Residuals and Linear Bottlenecks. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00474"},{"key":"ref_58","unstructured":"Chollet, F. (2020, June 30). Transfer Learning Using Pretrained ConvNets. Available online: https:\/\/www.tensorflow.org\/alpha\/tutorials\/images\/transfer_learning."},{"key":"ref_59","doi-asserted-by":"crossref","unstructured":"Lin, T.Y., Maire, M., Belongie, S., Hays, J., Perona, P., Ramanan, D., Doll\u00e1r, P., and Zitnick, C.L. (2014). Microsoft COCO: Common objects in context. Computer Vision\u2014ECCV 2014, Lecture Notes in Computer Science.","DOI":"10.1007\/978-3-319-10602-1_48"},{"key":"ref_60","doi-asserted-by":"crossref","unstructured":"Huang, J., Rathod, V., Sun, C., Zhu, M., Korattikara, A., Fathi, A., and Murphy, K. (2017, January 21\u201326). Speed\/accuracy trade-offs for modern convolutional object detectors. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.351"},{"key":"ref_61","doi-asserted-by":"crossref","unstructured":"Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., and Wojna, Z. (2016, January 27\u201330). Rethinking the Inception Architecture for Computer Vision. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.308"},{"key":"ref_62","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., and Sun, J. (2016, January 27\u201330). Deep residual learning for image recognition. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref_63","doi-asserted-by":"crossref","unstructured":"Liu, S., and Deng, W. (2015, January 3\u20136). Very deep convolutional neural network based image classification using small training sample size. Proceedings of the 3rd IAPR Asian Conference on Pattern Recognition, ACPR 2015, Kuala Lumpur, Malaysia.","DOI":"10.1109\/ACPR.2015.7486599"},{"key":"ref_64","doi-asserted-by":"crossref","first-page":"142","DOI":"10.1109\/TPAMI.2015.2437384","article-title":"Region-Based Convolutional Networks for Accurate Object Detection and Segmentation","volume":"38","author":"Girshick","year":"2016","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_65","doi-asserted-by":"crossref","unstructured":"Girshick, R. (2015, January 7\u201313). Fast R-CNN. Proceedings of the IEEE International Conference on Computer Vision, Santiago, Chile.","DOI":"10.1109\/ICCV.2015.169"},{"key":"ref_66","doi-asserted-by":"crossref","first-page":"1137","DOI":"10.1109\/TPAMI.2016.2577031","article-title":"Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks","volume":"39","author":"Ren","year":"2017","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_67","doi-asserted-by":"crossref","unstructured":"Liu, W., Anguelov, D., Erhan, D., Szegedy, C., Reed, S., Fu, C.-Y., and Berg, A.C. (2015). SSD: Single Shot MultiBox Detector. Computer Vision\u2014ECCV 2016, Lecture Notes in Computer Science.","DOI":"10.1007\/978-3-319-46448-0_2"},{"key":"ref_68","doi-asserted-by":"crossref","first-page":"303","DOI":"10.1007\/s11263-009-0275-4","article-title":"The pascal visual object classes (VOC) challenge","volume":"88","author":"Everingham","year":"2010","journal-title":"Int. J. Comput. Vis."},{"key":"ref_69","doi-asserted-by":"crossref","unstructured":"Doersch, C., Gupta, A., and Efros, A.A. (2015, January 7\u201313). Unsupervised visual representation learning by context prediction. Proceedings of the IEEE International Conference on Computer Vision, Santiago, Chile.","DOI":"10.1109\/ICCV.2015.167"},{"key":"ref_70","first-page":"181","article-title":"Active learning for deep object detection","volume":"Volume 5","author":"Brust","year":"2019","journal-title":"Proceedings of the VISIGRAPP 2019\u201414th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications"}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/12\/13\/2136\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T09:46:50Z","timestamp":1760176010000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/12\/13\/2136"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,7,3]]},"references-count":70,"journal-issue":{"issue":"13","published-online":{"date-parts":[[2020,7]]}},"alternative-id":["rs12132136"],"URL":"https:\/\/doi.org\/10.3390\/rs12132136","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,7,3]]}}}