{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,22]],"date-time":"2026-07-22T04:21:04Z","timestamp":1784694064575,"version":"3.55.0"},"reference-count":248,"publisher":"MDPI AG","issue":"21","license":[{"start":{"date-parts":[[2021,10,30]],"date-time":"2021-10-30T00:00:00Z","timestamp":1635552000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["No. 41901376"],"award-info":[{"award-number":["No. 41901376"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>In recent years unmanned aerial vehicles (UAVs) have emerged as a popular and cost-effective technology to capture high spatial and temporal resolution remote sensing (RS) images for a wide range of precision agriculture applications, which can help reduce costs and environmental impacts by providing detailed agricultural information to optimize field practices. Furthermore, deep learning (DL) has been successfully applied in agricultural applications such as weed detection, crop pest and disease detection, etc. as an intelligent tool. However, most DL-based methods place high computation, memory and network demands on resources. Cloud computing can increase processing efficiency with high scalability and low cost, but results in high latency and great pressure on the network bandwidth. The emerging of edge intelligence, although still in the early stages, provides a promising solution for artificial intelligence (AI) applications on intelligent edge devices at the edge of the network close to data sources. These devices are with built-in processors enabling onboard analytics or AI (e.g., UAVs and Internet of Things gateways). Therefore, in this paper, a comprehensive survey on the latest developments of precision agriculture with UAV RS and edge intelligence is conducted for the first time. The major insights observed are as follows: (a) in terms of UAV systems, small or light, fixed-wing or industrial rotor-wing UAVs are widely used in precision agriculture; (b) sensors on UAVs can provide multi-source datasets, and there are only a few public UAV dataset for intelligent precision agriculture, mainly from RGB sensors and a few from multispectral and hyperspectral sensors; (c) DL-based UAV RS methods can be categorized into classification, object detection and segmentation tasks, and convolutional neural network and recurrent neural network are the mostly common used network architectures; (d) cloud computing is a common solution to UAV RS data processing, while edge computing brings the computing close to data sources; (e) edge intelligence is the convergence of artificial intelligence and edge computing, in which model compression especially parameter pruning and quantization is the most important and widely used technique at present, and typical edge resources include central processing units, graphics processing units and field programmable gate arrays.<\/jats:p>","DOI":"10.3390\/rs13214387","type":"journal-article","created":{"date-parts":[[2021,11,1]],"date-time":"2021-11-01T22:24:22Z","timestamp":1635805462000},"page":"4387","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":178,"title":["Boost Precision Agriculture with Unmanned Aerial Vehicle Remote Sensing and Edge Intelligence: A Survey"],"prefix":"10.3390","volume":"13","author":[{"given":"Jia","family":"Liu","sequence":"first","affiliation":[{"name":"School of Computer Science, China University of Geosciences, Wuhan 430078, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jianjian","family":"Xiang","sequence":"additional","affiliation":[{"name":"School of Computer Science, China University of Geosciences, Wuhan 430078, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yongjun","family":"Jin","sequence":"additional","affiliation":[{"name":"School of Computer Science, China University of Geosciences, Wuhan 430078, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Renhua","family":"Liu","sequence":"additional","affiliation":[{"name":"School of Computer Science, China University of Geosciences, Wuhan 430078, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jining","family":"Yan","sequence":"additional","affiliation":[{"name":"School of Computer Science, China University of Geosciences, Wuhan 430078, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lizhe","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Computer Science, China University of Geosciences, Wuhan 430078, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2021,10,30]]},"reference":[{"key":"ref_1","unstructured":"ISPA (2021, October 17). Precision Ag Definition. Available online: https:\/\/www.ispag.org\/about\/definition."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Messina, G., and Modica, G. (2020). Applications of UAV Thermal Imagery in Precision Agriculture: State of the Art and Future Research Outlook. Remote Sens., 12.","DOI":"10.3390\/rs12091491"},{"key":"ref_3","unstructured":"Schimmelpfennig, D. (2016). Farm profits and adoption of precision agriculture."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"152","DOI":"10.1016\/j.tplants.2018.11.007","article-title":"Perspectives for Remote Sensing with Unmanned Aerial Vehicles in Precision Agriculture","volume":"24","author":"Maes","year":"2019","journal-title":"Trends Plant Sci."},{"key":"ref_5","unstructured":"Lillesand, T., Kiefer, R.W., and Chipman, J. (2015). Remote Sensing and Image Interpretation, John Wiley & Sons."},{"key":"ref_6","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_7","doi-asserted-by":"crossref","unstructured":"Eskandari, R., Mahdianpari, M., Mohammadimanesh, F., Salehi, B., Brisco, B., and Homayouni, S. (2020). Meta-Analysis of Unmanned Aerial Vehicle (UAV) Imagery for Agro-Environmental Monitoring Using Machine Learning and Statistical Models. Remote Sens., 12.","DOI":"10.3390\/rs12213511"},{"key":"ref_8","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_9","doi-asserted-by":"crossref","unstructured":"Zhang, H., Wang, L., Tian, T., and Yin, J. (2021). A Review of Unmanned Aerial Vehicle Low-Altitude Remote Sensing (UAV-LARS) Use in Agricultural Monitoring in China. Remote Sens., 13.","DOI":"10.3390\/rs13061221"},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Jang, G., Kim, J., Yu, J.-K., Kim, H.-J., Kim, Y., Kim, D.-W., Kim, K.-H., Lee, C.W., and Chung, Y.S. (2020). Review: Cost-Effective Unmanned Aerial Vehicle (UAV) Platform for Field Plant Breeding Application. Remote Sens., 12.","DOI":"10.3390\/rs12060998"},{"key":"ref_11","unstructured":"US Department of Defense (2021, October 19). Unmanned Aerial Vehicle, Available online: https:\/\/www.thefreedictionary.com\/Unmanned+Aerial+Vehicle."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"124","DOI":"10.1016\/j.isprsjprs.2018.09.008","article-title":"UAV-based multispectral remote sensing for precision agriculture: A comparison between different cameras","volume":"146","author":"Deng","year":"2018","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Christiansen, M.P., Laursen, M.S., J\u00f8rgensen, R.N., Skovsen, S., and Gislum, R. (2017). Designing and Testing a UAV Mapping System for Agricultural Field Surveying. Sensors, 17.","DOI":"10.3390\/s17122703"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Popescu, D., Stoican, F., Stamatescu, G., Ichim, L., and Dragana, C. (2020). Advanced UAV\u2013WSN System for Intelligent Monitoring in Precision Agriculture. Sensors, 20.","DOI":"10.3390\/s20030817"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"246","DOI":"10.1016\/j.isprsjprs.2017.05.003","article-title":"Predicting grain yield in rice using multi-temporal vegetation indices from UAV-based multispectral and digital imagery","volume":"130","author":"Zhou","year":"2017","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"142","DOI":"10.1016\/j.fcr.2019.02.022","article-title":"Deep convolutional neural networks for rice grain yield estimation at the ripening stage using UAV-based remotely sensed images","volume":"235","author":"Yang","year":"2019","journal-title":"Field Crops Res."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"157","DOI":"10.1016\/j.compag.2018.10.017","article-title":"Wheat yellow rust monitoring by learning from multispectral UAV aerial imagery","volume":"155","author":"Su","year":"2018","journal-title":"Comput. Electron. Agric."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Guo, A., Huang, W., Dong, Y., Ye, H., Ma, H., Liu, B., Wu, W., Ren, Y., Ruan, C., and Geng, Y. (2021). Wheat Yellow Rust Detection Using UAV-Based Hyperspectral Technology. Remote Sens., 13.","DOI":"10.3390\/rs13010123"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"723","DOI":"10.1614\/WS-D-15-00064.1","article-title":"Nonconventional Weed Management Strategies for Modern Agriculture","volume":"63","author":"Bajwa","year":"2015","journal-title":"Weed Sci."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"2","DOI":"10.1017\/wet.2017.89","article-title":"UAV Low-Altitude Remote Sensing for Precision Weed Management","volume":"32","author":"Huang","year":"2018","journal-title":"Weed Technol."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"105709","DOI":"10.1016\/j.compag.2020.105709","article-title":"Crop yield prediction using machine learning: A systematic literature review","volume":"177","author":"Kassahun","year":"2020","journal-title":"Comput. Electron. Agric."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"537","DOI":"10.1016\/j.sjbs.2017.01.024","article-title":"Support vector machine-based open crop model (SBOCM): Case of rice production in China","volume":"24","author":"Su","year":"2017","journal-title":"Saudi J. Biol. Sci."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"27","DOI":"10.1007\/s13593-016-0364-z","article-title":"Accurate prediction of sugarcane yield using a random forest algorithm","volume":"36","author":"Everingham","year":"2016","journal-title":"Agron. Sustain. Dev."},{"key":"ref_24","unstructured":"Chandra, A.L., Desai, S.V., Guo, W., and Balasubramanian, V.N. (2020). Computer vision with deep learning for plant phenotyping in agriculture: A survey. arXiv Prepr."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"1793","DOI":"10.1111\/1541-4337.12492","article-title":"Application of Deep Learning in Food: A Review","volume":"18","author":"Zhou","year":"2019","journal-title":"Compr. Rev. Food Sci. Food Saf."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"436","DOI":"10.1038\/nature14539","article-title":"Deep learning","volume":"521","author":"LeCun","year":"2015","journal-title":"Nature"},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Bah, M.D., Hafiane, A., and Canals, R. (2018). Deep Learning with Unsupervised Data Labeling for Weed Detection in Line Crops in UAV Images. Remote Sens., 10.","DOI":"10.20944\/preprints201809.0088.v1"},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Kitano, B.T., Mendes, C.C.T., Geus, A.R., Oliveira, H.C., and Souza, J.R. (2019). Corn plant counting using deep learning and UAV images. IEEE Geosci. Remote. Sens. Lett., 1\u20135.","DOI":"10.1109\/LGRS.2019.2930549"},{"key":"ref_29","first-page":"102313","article-title":"Crop type mapping by using transfer learning","volume":"98","author":"Nowakowski","year":"2021","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"166","DOI":"10.1016\/j.isprsjprs.2019.04.015","article-title":"Deep learning in remote sensing applications: A meta-analysis and review","volume":"152","author":"Ma","year":"2019","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"1655","DOI":"10.1109\/JPROC.2019.2921977","article-title":"Deep Learning with Edge Computing: A Review","volume":"107","author":"Chen","year":"2019","journal-title":"Proc. IEEE"},{"key":"ref_32","unstructured":"Simonyan, K., and Zisserman, A. (2015, January 7\u20139). Very Deep Convolutional Networks for Large-Scale Image Recognition. Proceedings of the International Conference on Learning Representations, San Diego, CA, USA."},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Liu, J., Liu, R., Ren, K., Li, X., Xiang, J., and Qiu, S. (2020, January 14\u201316). High-Performance Object Detection for Optical Remote Sensing Images with Lightweight Convolutional Neural Networks. Proceedings of the 2020 IEEE 22nd International Conference on High Performance Computing and Communications; IEEE 18th International Conference on Smart City; IEEE 6th International Conference on Data Science and Systems (HPCC\/SmartCity\/DSS), Yanuca Island, Cuvu, Fiji.","DOI":"10.1109\/HPCC-SmartCity-DSS50907.2020.00074"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"1738","DOI":"10.1109\/JPROC.2019.2918951","article-title":"Edge Intelligence: Paving the Last Mile of Artificial Intelligence with Edge Computing","volume":"107","author":"Zhou","year":"2019","journal-title":"Proc. IEEE"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"421","DOI":"10.1145\/2829988.2787505","article-title":"Low latency geo-distributed data analytics","volume":"45","author":"Pu","year":"2015","journal-title":"ACM SIGCOMM Comp. Com. Rev."},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Sitt\u00f3n-Candanedo, I., Alonso, R.S., Rodr\u00edguez-Gonz\u00e1lez, S., Coria, J.A.G., and De La Prieta, F. (2019). Edge Computing Architectures in Industry 4.0: A General Survey and Comparison. International Workshop on Soft Computing Models in Industrial and Environmental Applications, Springer.","DOI":"10.1007\/978-3-030-20055-8_12"},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Plastiras, G., Terzi, M., Kyrkou, C., and Theocharidcs, T. (2018, January 10\u201312). Edge intelligence: Challenges and opportunities of near-sensor machine learning applications. Proceedings of the 2018 IEEE 29th International Conference on Application-Specific Systems, Architectures and Processors (ASAP), Milano, Italy.","DOI":"10.1109\/ASAP.2018.8445118"},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"7457","DOI":"10.1109\/JIOT.2020.2984887","article-title":"Edge Intelligence: The Confluence of Edge Computing and Artificial Intelligence","volume":"7","author":"Deng","year":"2020","journal-title":"IEEE Internet Things J."},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Boursianis, A.D., Papadopoulou, M.S., Diamantoulakis, P., Liopa-Tsakalidi, A., Barouchas, P., Salahas, G., Karagiannidis, G., Wan, S., and Goudos, S.K. (2020). Internet of Things (IoT) and Agricultural Unmanned Aerial Vehicles (UAVs) in smart farming: A comprehensive review. Internet Things, 100187. in press.","DOI":"10.1016\/j.iot.2020.100187"},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"105100","DOI":"10.1109\/ACCESS.2019.2932119","article-title":"Unmanned Aerial Vehicles in Agriculture: A Review of Perspective of Platform, Control, and Applications","volume":"7","author":"Kim","year":"2019","journal-title":"IEEE Access"},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"502","DOI":"10.1016\/j.procs.2018.07.063","article-title":"Review on Application of Drone Systems in Precision Agriculture","volume":"133","author":"Mogili","year":"2018","journal-title":"Procedia Comput. Sci."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"312","DOI":"10.1017\/S0021859618000436","article-title":"A review of the use of convolutional neural networks in agriculture","volume":"156","author":"Kamilaris","year":"2018","journal-title":"J. Agric. Sci."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"70","DOI":"10.1016\/j.compag.2018.02.016","article-title":"Deep learning in agriculture: A survey","volume":"147","author":"Kamilaris","year":"2018","journal-title":"Comput. Electron. Agric."},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Santos, L., Santos, F.N., Oliveira, P.M., and Shinde, P. (2019). Deep Learning Applications in Agriculture: A Short Review. Iberian Robotics Conference, Springer.","DOI":"10.1007\/978-3-030-35990-4_12"},{"key":"ref_45","unstructured":"Civil Aviation Administration of China (2021, October 17). Interim Regulations on Flight Management of Unmanned Aerial Vehicles, Available online: http:\/\/www.caac.gov.cn\/HDJL\/YJZJ\/201801\/t20180126_48853.html."},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"Park, M., Lee, S., and Lee, S. (2020). Dynamic topology reconstruction protocol for uav swarm networking. Symmetry, 12.","DOI":"10.3390\/sym12071111"},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"107148","DOI":"10.1016\/j.comnet.2020.107148","article-title":"A compilation of UAV applications for precision agriculture","volume":"172","author":"Sarigiannidis","year":"2020","journal-title":"Comput. Netw."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"2624","DOI":"10.1109\/COMST.2016.2560343","article-title":"Survey on Unmanned Aerial Vehicle Networks for Civil Applications: A Communications Viewpoint","volume":"18","author":"Hayat","year":"2016","journal-title":"IEEE Commun. Surv. Tutor."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"105731","DOI":"10.1016\/j.compag.2020.105731","article-title":"A review on plant high-throughput phenotyping traits using UAV-based sensors","volume":"178","author":"Xie","year":"2020","journal-title":"Comput. Electron. Agric."},{"key":"ref_50","doi-asserted-by":"crossref","unstructured":"Delavarpour, N., Koparan, C., Nowatzki, J., Bajwa, S., and Sun, X. (2021). A Technical Study on UAV Characteristics for Precision Agriculture Applications and Associated Practical Challenges. Remote Sens., 13.","DOI":"10.3390\/rs13061204"},{"key":"ref_51","doi-asserted-by":"crossref","unstructured":"Tsouros, D.C., Triantafyllou, A., Bibi, S., and Sarigannidis, P.G. (2019, January 29\u201331). Data acquisition and analysis methods in UAV-based applications for Precision Agriculture. Proceedings of the 2019 15th International Conference on Distributed Computing in Sensor Systems (DCOSS), Santorini Island, Greece.","DOI":"10.1109\/DCOSS.2019.00080"},{"key":"ref_52","unstructured":"Tahir, M.N., Lan, Y., Zhang, Y., Wang, Y., Nawaz, F., Shah, M.A.A., Gulzar, A., Qureshi, W.S., Naqvi, S.M., and Naqvi, S.Z.A. (2020). Real time estimation of leaf area index and groundnut yield using multispectral UAV. Int. J. Precis. Agric. Aviat., 3."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"5432","DOI":"10.1080\/01431161.2018.1441569","article-title":"Early season weed mapping in rice crops using multi-spectral UAV data","volume":"39","author":"Stroppiana","year":"2018","journal-title":"Int. J. Remote Sens."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"2467","DOI":"10.1080\/01431161.2019.1569783","article-title":"Estimating the nitrogen nutrition index in grass seed crops using a UAV-mounted multispectral camera","volume":"40","author":"Wang","year":"2019","journal-title":"Int. J. Remote Sens."},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"80","DOI":"10.1016\/j.compag.2017.11.027","article-title":"A novel approach for vegetation classification using UAV-based hyperspectral imaging","volume":"144","author":"Ishida","year":"2018","journal-title":"Comput. Electron. Agric."},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"e6926","DOI":"10.7717\/peerj.6926","article-title":"Combining UAV-based hyperspectral imagery and machine learning algorithms for soil moisture content monitoring","volume":"7","author":"Ge","year":"2019","journal-title":"PeerJ"},{"key":"ref_57","first-page":"110","article-title":"Estimation of soybean breeding yield based on optimization of spatial scale of UAV hyperspectral image","volume":"33","author":"Zhao","year":"2017","journal-title":"Trans. Chin. Soc. Agric. Eng."},{"key":"ref_58","first-page":"239","article-title":"Thermal remote sensing: Concepts, issues and applications","volume":"33","author":"Prakash","year":"2000","journal-title":"Int. Arch. Photogramm. Remote Sens."},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"335","DOI":"10.1016\/j.isprsjprs.2009.03.007","article-title":"Thermal infrared remote sensing for urban climate and environmental studies: Methods, applications, and trends","volume":"64","author":"Weng","year":"2009","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"22","DOI":"10.1016\/j.compag.2017.05.001","article-title":"An overview of current and potential applications of thermal remote sensing in precision agriculture","volume":"139","author":"Khanal","year":"2017","journal-title":"Comput. Electron. Agric."},{"key":"ref_61","doi-asserted-by":"crossref","unstructured":"Dong, P., and Chen, Q. (2017). LiDAR Remote Sensing and Applications, CRC Press.","DOI":"10.4324\/9781351233354"},{"key":"ref_62","doi-asserted-by":"crossref","unstructured":"Zhou, L., Gu, X., Cheng, S., Yang, G., Shu, M., and Sun, Q. (2020). Analysis of plant height changes of lodged maize using UAV-LiDAR data. Agriculture, 10.","DOI":"10.3390\/agriculture10050146"},{"key":"ref_63","first-page":"102177","article-title":"Fine-scale prediction of biomass and leaf nitrogen content in sugarcane using UAV LiDAR and multispectral imaging","volume":"92","author":"Shendryk","year":"2020","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_64","doi-asserted-by":"crossref","unstructured":"Ndikumana, E., Minh, D.H.T., Baghdadi, N., Courault, D., and Hossard, L. (2018). Deep Recurrent Neural Network for Agricultural Classification using multitemporal SAR Sentinel-1 for Camargue, France. Remote Sens., 10.","DOI":"10.3390\/rs10081217"},{"key":"ref_65","doi-asserted-by":"crossref","unstructured":"Lyalin, K.S., Biryuk, A.A., Sheremet, A.Y., Tsvetkov, V.K., and Prikhodko, D.V. (February, January 29). UAV synthetic aperture radar system for control of vegetation and soil moisture. Proceedings of the 2018 IEEE Conference of Russian Young Researchers in Electrical and Electronic Engineering (EIConRus), St. Petersburg and Moscow, Russia.","DOI":"10.1109\/EIConRus.2018.8317425"},{"key":"ref_66","doi-asserted-by":"crossref","first-page":"506","DOI":"10.1016\/S2095-3119(18)62016-7","article-title":"Research advances of SAR remote sensing for agriculture applications: A review","volume":"18","author":"Liu","year":"2019","journal-title":"J. Integr. Agric."},{"key":"ref_67","doi-asserted-by":"crossref","first-page":"2349","DOI":"10.1080\/01431161.2017.1297548","article-title":"UAS, sensors, and data processing in agroforestry: A review towards practical applications","volume":"38","author":"Vanko","year":"2017","journal-title":"Int. J. Remote Sens."},{"key":"ref_68","doi-asserted-by":"crossref","first-page":"132","DOI":"10.1016\/j.agwat.2017.11.011","article-title":"Effective and efficient agricultural drainage pipe mapping with UAS thermal infrared imagery: A case study","volume":"197","author":"Allred","year":"2018","journal-title":"Agric. Water Manag."},{"key":"ref_69","doi-asserted-by":"crossref","first-page":"49","DOI":"10.1016\/j.agwat.2016.08.026","article-title":"High-resolution UAV-based thermal imaging to estimate the instantaneous and seasonal variability of plant water status within a vineyard","volume":"183","author":"Santesteban","year":"2017","journal-title":"Agric. Water Manag."},{"key":"ref_70","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1155\/2017\/1353691","article-title":"Significant Remote Sensing Vegetation Indices: A Review of Developments and Applications","volume":"2017","author":"Xue","year":"2017","journal-title":"J. Sensors"},{"key":"ref_71","doi-asserted-by":"crossref","unstructured":"Dai, B., He, Y., Gu, F., Yang, L., Han, J., and Xu, W. (2017, January 5\u20138). A vision-based autonomous aerial spray system for precision agriculture. Proceedings of the 2017 IEEE International Conference on Robotics and Biomimetics (ROBIO), Macau, China.","DOI":"10.1109\/ROBIO.2017.8324467"},{"key":"ref_72","doi-asserted-by":"crossref","first-page":"210","DOI":"10.1016\/j.compag.2017.04.011","article-title":"An adaptive approach for UAV-based pesticide spraying in dynamic environments","volume":"138","author":"Freitas","year":"2017","journal-title":"Comput. Electron. Agric."},{"key":"ref_73","doi-asserted-by":"crossref","first-page":"1660003","DOI":"10.1142\/S0218213016600034","article-title":"Fine-Tuning of UAV Control Rules for Spraying Pesticides on Crop Fields: An Approach for Dynamic Environments","volume":"25","author":"Pessin","year":"2016","journal-title":"Int. J. Artif. Intell. Tools"},{"key":"ref_74","doi-asserted-by":"crossref","first-page":"18","DOI":"10.1186\/s40538-021-00217-8","article-title":"Drone and sensor technology for sustainable weed management: A review","volume":"8","author":"Esposito","year":"2021","journal-title":"Chem. Biol. Technol. Agric."},{"key":"ref_75","doi-asserted-by":"crossref","unstructured":"Bah, M.D., Dericquebourg, E., Hafiane, A., and Canals, R. (2018). Deep Learning based Classification System for Identifying Weeds using High-Resolution UAV Imagery. Science and Information Conference, Springer.","DOI":"10.1007\/978-3-030-01177-2_13"},{"key":"ref_76","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_77","first-page":"1","article-title":"DeepWeeds: A Multiclass Weed Species Image Dataset for Deep Learning","volume":"9","author":"Olsen","year":"2019","journal-title":"Sci. Rep. UK"},{"key":"ref_78","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_79","doi-asserted-by":"crossref","first-page":"044516","DOI":"10.1117\/1.JRS.13.044516","article-title":"Hyperspectral imaging and neural networks to classify herbicide-resistant weeds","volume":"13","author":"Scherrer","year":"2019","journal-title":"J. Appl. Remote Sens."},{"key":"ref_80","doi-asserted-by":"crossref","first-page":"3446","DOI":"10.1080\/01431161.2019.1706112","article-title":"Deep learning versus Object-based Image Analysis (OBIA) in weed mapping of UAV imagery","volume":"41","author":"Huang","year":"2020","journal-title":"Int. J. Remote Sens."},{"key":"ref_81","doi-asserted-by":"crossref","unstructured":"Hasan, R.I., Yusuf, S.M., and Alzubaidi, L. (2020). Review of the State of the Art of Deep Learning for Plant Diseases: A Broad Analysis and Discussion. Plants, 9.","DOI":"10.3390\/plants9101302"},{"key":"ref_82","doi-asserted-by":"crossref","unstructured":"Abdulridha, J., Batuman, O., and Ampatzidis, Y. (2019). UAV-Based Remote Sensing Technique to Detect Citrus Canker Disease Utilizing Hyperspectral Imaging and Machine Learning. Remote Sens., 11.","DOI":"10.3390\/rs11111373"},{"key":"ref_83","doi-asserted-by":"crossref","first-page":"105836","DOI":"10.1016\/j.compag.2020.105836","article-title":"Detection and classification of soybean pests using deep learning with UAV images","volume":"179","author":"Tetila","year":"2020","journal-title":"Comput. Electron. Agric."},{"key":"ref_84","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_85","doi-asserted-by":"crossref","first-page":"138","DOI":"10.1016\/j.biosystemseng.2020.03.021","article-title":"Recognition of diseased Pinus trees in UAV images using deep learning and AdaBoost classifier","volume":"194","author":"Hu","year":"2020","journal-title":"Biosyst. Eng."},{"key":"ref_86","doi-asserted-by":"crossref","first-page":"903","DOI":"10.1109\/LGRS.2019.2932385","article-title":"Automatic Recognition of Soybean Leaf Diseases Using UAV Images and Deep Convolutional Neural Networks","volume":"17","author":"Tetila","year":"2019","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_87","doi-asserted-by":"crossref","first-page":"1550","DOI":"10.3389\/fpls.2019.01550","article-title":"Millimeter-Level Plant Disease Detection from Aerial Photographs via Deep Learning and Crowdsourced Data","volume":"10","author":"Wu","year":"2019","journal-title":"Front. Plant Sci."},{"key":"ref_88","doi-asserted-by":"crossref","unstructured":"Albetis, J., Jacquin, A., Goulard, M., Poilv\u00e9, H., Rousseau, J., Clenet, H., Dedieu, G., and Duthoit, S. (2018). On the Potentiality of UAV Multispectral Imagery to Detect Flavescence dor\u00e9e and Grapevine Trunk Diseases. Remote Sens., 11.","DOI":"10.3390\/rs11010023"},{"key":"ref_89","doi-asserted-by":"crossref","first-page":"105446","DOI":"10.1016\/j.compag.2020.105446","article-title":"Vine disease detection in UAV multispectral images using optimized image registration and deep learning segmentation approach","volume":"174","author":"Kerkech","year":"2020","journal-title":"Comput. Electron. Agric."},{"key":"ref_90","doi-asserted-by":"crossref","first-page":"45","DOI":"10.5194\/isprsarchives-XL-1-W2-45-2013","article-title":"Very high resolution crop surface models (CSMs) from UAV-based stereo images for rice growth monitoring In Northeast China","volume":"40","author":"Bendig","year":"2013","journal-title":"Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci."},{"key":"ref_91","doi-asserted-by":"crossref","unstructured":"Ni, J., Yao, L., Zhang, J., Cao, W., Zhu, Y., and Tai, X. (2017). Development of an Unmanned Aerial Vehicle-Borne Crop-Growth Monitoring System. Sensors, 17.","DOI":"10.3390\/s17030502"},{"key":"ref_92","doi-asserted-by":"crossref","unstructured":"Fu, Z., Jiang, J., Gao, Y., Krienke, B., Wang, M., Zhong, K., Cao, Q., Tian, Y., Zhu, Y., and Cao, W. (2020). Wheat Growth Monitoring and Yield Estimation based on Multi-Rotor Unmanned Aerial Vehicle. Remote Sens., 12.","DOI":"10.3390\/rs12030508"},{"key":"ref_93","doi-asserted-by":"crossref","unstructured":"Zhao, J., Zhang, X., Gao, C., Qiu, X., Tian, Y., Zhu, Y., and Cao, W. (2019). Rapid Mosaicking of Unmanned Aerial Vehicle (UAV) Images for Crop Growth Monitoring Using the SIFT Algorithm. Remote Sens., 11.","DOI":"10.3390\/rs11101226"},{"key":"ref_94","doi-asserted-by":"crossref","first-page":"161","DOI":"10.1016\/j.isprsjprs.2020.02.013","article-title":"Above-ground biomass estimation and yield prediction in potato by using UAV-based RGB and hyperspectral imaging","volume":"162","author":"Li","year":"2020","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_95","doi-asserted-by":"crossref","first-page":"111599","DOI":"10.1016\/j.rse.2019.111599","article-title":"Soybean yield prediction from UAV using multimodal data fusion and deep learning","volume":"237","author":"Maimaitijiang","year":"2020","journal-title":"Remote Sens. Environ."},{"key":"ref_96","doi-asserted-by":"crossref","unstructured":"Nebiker, S., Lack, N., Ab\u00e4cherli, M., and L\u00e4derach, S. (2016). Light-weight multispectral UAV sensors and their capabilities for predicting grain yield and detecting plant diseases. Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci., 41.","DOI":"10.5194\/isprsarchives-XLI-B1-963-2016"},{"key":"ref_97","doi-asserted-by":"crossref","unstructured":"Stroppiana, D., Migliazzi, M., Chiarabini, V., Crema, A., Musanti, M., Franchino, C., and Villa, P. (2015, January 26\u201331). Rice yield estimation using multispectral data from UAV: A preliminary experiment in northern Italy. Proceedings of the 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), Milan, Italy.","DOI":"10.1109\/IGARSS.2015.7326869"},{"key":"ref_98","doi-asserted-by":"crossref","first-page":"778","DOI":"10.1109\/LGRS.2017.2681128","article-title":"Deep Learning Classification of Land Cover and Crop Types Using Remote Sensing Data","volume":"14","author":"Kussul","year":"2017","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_99","doi-asserted-by":"crossref","unstructured":"Teimouri, N., Dyrmann, M., and J\u00f8rgensen, R.N. (2019). A Novel Spatio-Temporal FCN-LSTM Network for Recognizing Various Crop Types Using Multi-Temporal Radar Images. Remote Sens., 11.","DOI":"10.3390\/rs11080990"},{"key":"ref_100","doi-asserted-by":"crossref","unstructured":"Wang, S., Di Tommaso, S., Faulkner, J., Friedel, T., Kennepohl, A., Strey, R., and Lobell, D. (2020). Mapping Crop Types in Southeast India with Smartphone Crowdsourcing and Deep Learning. Remote Sens., 12.","DOI":"10.3390\/rs12182957"},{"key":"ref_101","unstructured":"Rebetez, J., Satiz\u00e1bal, H.F., Mota, M., Noll, D., B\u00fcchi, L., Wendling, M., Cannelle, B., Perez-Uribe, A., and Burgos, S. (2016). Augmenting a Convolutional Neural Network with Local Histograms-A Case Study in Crop Classification from High-Resolution UAV Imagery, ESANN."},{"key":"ref_102","doi-asserted-by":"crossref","unstructured":"Zhao, L., Shi, Y., Liu, B., Hovis, C., Duan, Y., and Shi, Z. (2019). Finer Classification of Crops by Fusing UAV Images and Sentinel-2A Data. Remote Sens., 11.","DOI":"10.3390\/rs11243012"},{"key":"ref_103","doi-asserted-by":"crossref","first-page":"504","DOI":"10.1126\/science.1127647","article-title":"Reducing the Dimensionality of Data with Neural Networks","volume":"313","author":"Hinton","year":"2006","journal-title":"Science"},{"key":"ref_104","first-page":"1097","article-title":"ImageNet classification with deep convolutional neural networks","volume":"25","author":"Krizhevsky","year":"2012","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref_105","doi-asserted-by":"crossref","unstructured":"Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., Erhan, D., Vanhoucke, V., and Rabinovich, A. (2015, January 7\u201312). Going deeper with convolutions. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Boston, MA, USA.","DOI":"10.1109\/CVPR.2015.7298594"},{"key":"ref_106","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 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref_107","unstructured":"Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., and Bengio, Y. (2014). Generative adversarial nets. Adv. Neural Inf. Process. Syst., 27."},{"key":"ref_108","doi-asserted-by":"crossref","unstructured":"Reyes, M.F., Auer, S., Merkle, N.M., Henry, C., and Schmitt, M. (2019). SAR-to-Optical Image Translation Based on Conditional Generative Adversarial Networks - Optimization, Opportunities and Limits. Remote Sens., 11.","DOI":"10.3390\/rs11172067"},{"key":"ref_109","doi-asserted-by":"crossref","unstructured":"Wang, X., Yan, H., Huo, C., Yu, J., and Pant, C. (2018, January 20\u201324). Enhancing Pix2Pix for Remote Sensing Image Classification. Proceedings of the International Conference on Pattern Recognition, Beijing, China.","DOI":"10.1109\/ICPR.2018.8545870"},{"key":"ref_110","doi-asserted-by":"crossref","unstructured":"Lv, N., Ma, H., Chen, C., Pei, Q., Zhou, Y., Xiao, F., and Li, J. (2020). Remote Sensing Data Augmentation Through Adversarial Training. Int. Geosci. Remote Sens. Symp., 2511\u20132514.","DOI":"10.1109\/IGARSS39084.2020.9324263"},{"key":"ref_111","doi-asserted-by":"crossref","unstructured":"Ren, C.X., Ziemann, A., Theiler, J., and Durieux, A.M.S. (2020, January 19). Deep snow: Synthesizing remote sensing imagery with generative adversarial nets. Proceedings of the 2020 Algorithms, Technologies, and Applications for Multispectral and Hyperspectral Imagery XXVI, Online only.","DOI":"10.1117\/12.2560716"},{"key":"ref_112","doi-asserted-by":"crossref","first-page":"98","DOI":"10.1007\/s11263-014-0733-5","article-title":"The Pascal Visual Object Classes Challenge: A Retrospective","volume":"111","author":"Everingham","year":"2015","journal-title":"Int. J. Comput. Vis."},{"key":"ref_113","doi-asserted-by":"crossref","unstructured":"Ha, J.G., Moon, H., Kwak, J.T., Hassan, S.I., Dang, M., Lee, O.N., and Park, H.Y. (2017). Deep convolutional neural network for classifying Fusarium wilt of radish from unmanned aerial vehicles. J. Appl. Remote Sens., 11.","DOI":"10.1117\/1.JRS.11.042621"},{"key":"ref_114","doi-asserted-by":"crossref","unstructured":"Huang, H., Deng, J., Lan, Y., Yang, A., Zhang, L., Wen, S., Zhang, H., Zhang, Y., and Deng, Y. (2019). Detection of Helminthosporium Leaf Blotch Disease Based on UAV Imagery. Appl. Sci., 9.","DOI":"10.3390\/app9030558"},{"key":"ref_115","doi-asserted-by":"crossref","unstructured":"De Camargo, T., Schirrmann, M., Landwehr, N., Dammer, K.-H., and Pflanz, M. (2021). Optimized Deep Learning Model as a Basis for Fast UAV Mapping of Weed Species in Winter Wheat Crops. Remote Sens., 13.","DOI":"10.3390\/rs13091704"},{"key":"ref_116","doi-asserted-by":"crossref","unstructured":"Ukaegbu, U., Tartibu, L., Okwu, M., and Olayode, I. (2021). Development of a Light-Weight Unmanned Aerial Vehicle for Precision Agriculture. Sensors, 21.","DOI":"10.3390\/s21134417"},{"key":"ref_117","unstructured":"Onishi, M., and Ise, T. (2018). Automatic classification of trees using a UAV onboard camera and deep learning. arXiv Prepr."},{"key":"ref_118","doi-asserted-by":"crossref","first-page":"111605","DOI":"10.1016\/j.rse.2019.111605","article-title":"A robust spectral-spatial approach to identifying heterogeneous crops using remote sensing imagery with high spectral and spatial resolutions","volume":"239","author":"Zhao","year":"2020","journal-title":"Remote Sens. Environ."},{"key":"ref_119","doi-asserted-by":"crossref","first-page":"21986","DOI":"10.1109\/ACCESS.2021.3056082","article-title":"Identification of Fruit Tree Pests with Deep Learning on Embedded Drone to Achieve Accurate Pesticide Spraying","volume":"9","author":"Chen","year":"2021","journal-title":"IEEE Access"},{"key":"ref_120","doi-asserted-by":"crossref","first-page":"66346","DOI":"10.1109\/ACCESS.2021.3073929","article-title":"A Remote Sensing and Airborne Edge-Computing Based Detection System for Pine Wilt Disease","volume":"9","author":"Li","year":"2021","journal-title":"IEEE Access"},{"key":"ref_121","doi-asserted-by":"crossref","first-page":"179","DOI":"10.5194\/isprs-annals-IV-2-W5-179-2019","article-title":"Detecting rumex obtusifolius weed plants in grasslands from UAV RGB imagery using deep learning","volume":"4","author":"Valente","year":"2019","journal-title":"ISPRS Ann. Photogramm. Remote Sens. Spat. Inf. Sci."},{"key":"ref_122","doi-asserted-by":"crossref","unstructured":"Veeranampalayam Sivakumar, A.N., Li, J., Scott, S., Psota, E., Jhala, A.J., Luck, J.D., and Shi, Y. (2020). Comparison of object detection and patch-based classification deep learning models on mid-to late-season weed detection in UAV imagery. Remote Sens., 12.","DOI":"10.3390\/rs12132136"},{"key":"ref_123","doi-asserted-by":"crossref","first-page":"126030","DOI":"10.1016\/j.eja.2020.126030","article-title":"Deep learning techniques for estimation of the yield and size of citrus fruits using a UAV","volume":"115","author":"Egea","year":"2020","journal-title":"Eur. J. Agron."},{"key":"ref_124","doi-asserted-by":"crossref","unstructured":"Chen, Y., Lee, W.S., Gan, H., Peres, N., Fraisse, C., Zhang, Y., and He, Y. (2019). Strawberry Yield Prediction Based on a Deep Neural Network Using High-Resolution Aerial Orthoimages. Remote Sens., 11.","DOI":"10.3390\/rs11131584"},{"key":"ref_125","doi-asserted-by":"crossref","unstructured":"Csillik, O., Cherbini, J., Johnson, R., Lyons, A., and Kelly, M. (2018). Identification of Citrus Trees from Unmanned Aerial Vehicle Imagery Using Convolutional Neural Networks. Drones, 2.","DOI":"10.3390\/drones2040039"},{"key":"ref_126","doi-asserted-by":"crossref","unstructured":"Zhang, Z., Flores, P., Igathinathane, C., Naik, D.L., Kiran, R., and Ransom, J.K. (2020). Wheat Lodging Detection from UAS Imagery Using Machine Learning Algorithms. Remote Sens., 12.","DOI":"10.3390\/rs12111838"},{"key":"ref_127","doi-asserted-by":"crossref","unstructured":"Stewart, E.L., Wiesner-Hanks, T., Kaczmar, N., DeChant, C., Wu, H., Lipson, H., Nelson, R.J., and Gore, M.A. (2019). Quantitative Phenotyping of Northern Leaf Blight in UAV Images Using Deep Learning. Remote Sens., 11.","DOI":"10.3390\/rs11192209"},{"key":"ref_128","doi-asserted-by":"crossref","unstructured":"Kerkech, M., Hafiane, A., and Canals, R. (2020). VddNet: Vine Disease Detection Network Based on Multispectral Images and Depth Map. Remote Sens., 12.","DOI":"10.3390\/rs12203305"},{"key":"ref_129","doi-asserted-by":"crossref","unstructured":"Zou, K., Chen, X., Zhang, F., Zhou, H., and Zhang, C. (2021). A Field Weed Density Evaluation Method Based on UAV Imaging and Modified U-Net. Remote Sens., 13.","DOI":"10.3390\/rs13020310"},{"key":"ref_130","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1007\/s11119-020-09777-5","article-title":"Semantic segmentation of citrus-orchard using deep neural networks and multispectral UAV-based imagery","volume":"22","author":"Osco","year":"2021","journal-title":"Precis. Agric."},{"key":"ref_131","doi-asserted-by":"crossref","unstructured":"Zhang, J., Xie, T., Yang, C., Song, H., Jiang, Z., Zhou, G., Zhang, D., Feng, H., and Xie, J. (2020). Segmenting Purple Rapeseed Leaves in the Field from UAV RGB Imagery Using Deep Learning as an Auxiliary Means for Nitrogen Stress Detection. Remote Sens., 12.","DOI":"10.3390\/rs12091403"},{"key":"ref_132","doi-asserted-by":"crossref","first-page":"105762","DOI":"10.1016\/j.compag.2020.105762","article-title":"Establishing a model to predict the single boll weight of cotton in northern Xinjiang by using high resolution UAV remote sensing data","volume":"179","author":"Xu","year":"2020","journal-title":"Comput. Electron. Agric."},{"key":"ref_133","doi-asserted-by":"crossref","first-page":"e11373","DOI":"10.1002\/aps3.11373","article-title":"Instance segmentation for the fine detection of crop and weed plants by precision agricultural robots","volume":"8","author":"Champ","year":"2020","journal-title":"Appl. Plant Sci."},{"key":"ref_134","unstructured":"Mora-Fallas, A., Go\u00ebau, H., Joly, A., Bonnet, P., and Mata-Montero, E. (2021, October 17). Instance segmentation for automated weeds and crops detection in farmlands. A first approach to Acoustic Characterization of Costa Rican Children\u2019s Speech. Available online: https:\/\/redmine.mdpi.cn\/issues\/2225524#change-20906846."},{"key":"ref_135","doi-asserted-by":"crossref","first-page":"173","DOI":"10.1038\/s42003-020-0905-5","article-title":"Training instance segmentation neural network with synthetic datasets for crop seed phenotyping","volume":"3","author":"Toda","year":"2020","journal-title":"Commun. Biol."},{"key":"ref_136","doi-asserted-by":"crossref","unstructured":"Khan, S., Tufail, M., Khan, M.T., Khan, Z.A., Iqbal, J., and Wasim, A. (2021). Real-time recognition of spraying area for UAV sprayers using a deep learning approach. PLoS ONE, 16.","DOI":"10.1371\/journal.pone.0249436"},{"key":"ref_137","doi-asserted-by":"crossref","unstructured":"Deng, J., Zhong, Z., Huang, H., Lan, Y., Han, Y., and Zhang, Y. (2020). Lightweight Semantic Segmentation Network for Real-Time Weed Mapping Using Unmanned Aerial Vehicles. Appl. Sci., 10.","DOI":"10.3390\/app10207132"},{"key":"ref_138","doi-asserted-by":"crossref","first-page":"198","DOI":"10.1109\/LGRS.2020.2972313","article-title":"Identification and Grading of Maize Drought on RGB Images of UAV Based on Improved U-Net","volume":"18","author":"Liu","year":"2020","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_139","doi-asserted-by":"crossref","unstructured":"Tri, N.C., Duong, H.N., Van Hoai, T., Van Hoa, T., Nguyen, V.H., Toan, N.T., and Snasel, V. (2017, January 19\u201321). A novel approach based on deep learning techniques and UAVs to yield assessment of paddy fields. Proceedings of the 2017 9th International Conference on Knowledge and Systems Engineering (KSE), Hue, Vietnam.","DOI":"10.1109\/KSE.2017.8119468"},{"key":"ref_140","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.isprsjprs.2021.01.024","article-title":"A CNN approach to simultaneously count plants and detect plantation-rows from UAV imagery","volume":"174","author":"Osco","year":"2021","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_141","doi-asserted-by":"crossref","first-page":"97","DOI":"10.1016\/j.isprsjprs.2019.12.010","article-title":"A convolutional neural network approach for counting and geolocating citrus-trees in UAV multispectral imagery","volume":"160","author":"Osco","year":"2020","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_142","doi-asserted-by":"crossref","first-page":"95","DOI":"10.1016\/j.isprsjprs.2021.01.008","article-title":"Growing status observation for oil palm trees using Unmanned Aerial Vehicle (UAV) images","volume":"173","author":"Zheng","year":"2021","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_143","doi-asserted-by":"crossref","first-page":"105457","DOI":"10.1016\/j.compag.2020.105457","article-title":"Agroview: Cloud-based application to process, analyze and visualize UAV-collected data for precision agriculture applications utilizing artificial intelligence","volume":"174","author":"Ampatzidis","year":"2020","journal-title":"Comput. Electron. Agric."},{"key":"ref_144","doi-asserted-by":"crossref","first-page":"105766","DOI":"10.1016\/j.compag.2020.105766","article-title":"Improved crop row detection with deep neural network for early-season maize stand count in UAV imagery","volume":"178","author":"Pang","year":"2020","journal-title":"Comput. Electron. Agric."},{"key":"ref_145","doi-asserted-by":"crossref","unstructured":"Wu, J., Yang, G., Yang, X., Xu, B., Han, L., and Zhu, Y. (2019). Automatic Counting of in situ Rice Seedlings from UAV Images Based on a Deep Fully Convolutional Neural Network. Remote Sens., 11.","DOI":"10.3390\/rs11060691"},{"key":"ref_146","doi-asserted-by":"crossref","unstructured":"Yang, M.-D., Tseng, H.-H., Hsu, Y.-C., Yang, C.-Y., Lai, M.-H., and Wu, D.-H. (2021). A UAV Open Dataset of Rice Paddies for Deep Learning Practice. Remote Sens., 13.","DOI":"10.3390\/rs13071358"},{"key":"ref_147","doi-asserted-by":"crossref","unstructured":"Zhao, W., Yamada, W., Li, T., Digman, M., and Runge, T. (2020). Augmenting Crop Detection for Precision Agriculture with Deep Visual Transfer Learning\u2014A Case Study of Bale Detection. Remote Sens., 13.","DOI":"10.3390\/rs13010023"},{"key":"ref_148","doi-asserted-by":"crossref","unstructured":"Ampatzidis, Y., and Partel, V. (2019). UAV-Based High Throughput Phenotyping in Citrus Utilizing Multispectral Imaging and Artificial Intelligence. Remote Sens., 11.","DOI":"10.3390\/rs11040410"},{"key":"ref_149","doi-asserted-by":"crossref","unstructured":"Aeberli, A., Johansen, K., Robson, A., Lamb, D., and Phinn, S. (2021). Detection of Banana Plants Using Multi-Temporal Multispectral UAV Imagery. Remote Sens., 13.","DOI":"10.3390\/rs13112123"},{"key":"ref_150","doi-asserted-by":"crossref","first-page":"876","DOI":"10.1109\/JSTARS.2018.2793849","article-title":"Automatic Tobacco Plant Detection in UAV Images via Deep Neural Networks","volume":"11","author":"Fan","year":"2018","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_151","doi-asserted-by":"crossref","unstructured":"Zan, X., Zhang, X., Xing, Z., Liu, W., Zhang, X., Su, W., Liu, Z., Zhao, Y., and Li, S. (2020). Automatic Detection of Maize Tassels from UAV Images by Combining Random Forest Classifier and VGG16. Remote Sens., 12.","DOI":"10.3390\/rs12183049"},{"key":"ref_152","doi-asserted-by":"crossref","unstructured":"Liu, Y., Cen, C., Che, Y., Ke, R., Ma, Y., and Ma, Y. (2020). Detection of Maize Tassels from UAV RGB Imagery with Faster R-CNN. Remote Sens., 12.","DOI":"10.3390\/rs12020338"},{"key":"ref_153","doi-asserted-by":"crossref","unstructured":"Yuan, W., and Choi, D. (2021). UAV-Based Heating Requirement Determination for Frost Management in Apple Orchard. Remote Sens., 13.","DOI":"10.3390\/rs13020273"},{"key":"ref_154","doi-asserted-by":"crossref","unstructured":"Dyson, J., Mancini, A., Frontoni, E., and Zingaretti, P. (2019). Deep Learning for Soil and Crop Segmentation from Remotely Sensed Data. Remote Sens., 11.","DOI":"10.3390\/rs11161859"},{"key":"ref_155","doi-asserted-by":"crossref","unstructured":"Feng, Q., Yang, J., Liu, Y., Ou, C., Zhu, D., Niu, B., Liu, J., and Li, B. (2020). Multi-Temporal Unmanned Aerial Vehicle Remote Sensing for Vegetable Mapping Using an Attention-Based Recurrent Convolutional Neural Network. Remote Sens., 12.","DOI":"10.3390\/rs12101668"},{"key":"ref_156","doi-asserted-by":"crossref","unstructured":"Der Yang, M., Tseng, H.H., Hsu, Y.C., and Tseng, W.C. (2020, January 10\u201313). Real-time Crop Classification Using Edge Computing and Deep Learning. Proceedings of the 2020 IEEE 17th Annual Consumer Communications & Networking Conference, Las Vegas, NV, USA.","DOI":"10.1109\/CCNC46108.2020.9045498"},{"key":"ref_157","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_158","doi-asserted-by":"crossref","unstructured":"Zhang, Q., Liu, Y., Gong, C., Chen, Y., and Yu, H. (2020). Applications of Deep Learning for Dense Scenes Analysis in Agriculture: A Review. Sensors, 20.","DOI":"10.3390\/s20051520"},{"key":"ref_159","doi-asserted-by":"crossref","first-page":"112012","DOI":"10.1016\/j.rse.2020.112012","article-title":"WHU-Hi: UAV-borne hyperspdectral with high spatial resolution (H2) benchmark datasets and classifier for precise crop identification based on deep convolutional neural network with CRF","volume":"250","author":"Zhong","year":"2020","journal-title":"Remote Sens. Environ."},{"key":"ref_160","doi-asserted-by":"crossref","unstructured":"Wiesner-Hanks, T., Stewart, E.L., Kaczmar, N., DeChant, C., Wu, H., Nelson, R.J., Lipson, H., and Gore, M.A. (2018). Image set for deep learning: Field images of maize annotated with disease symptoms. BMC Res. Notes, 11.","DOI":"10.1186\/s13104-018-3548-6"},{"key":"ref_161","doi-asserted-by":"crossref","first-page":"102783","DOI":"10.1016\/j.cviu.2019.07.003","article-title":"Multitask learning for large-scale semantic change detection","volume":"187","author":"Daudt","year":"2019","journal-title":"Comput. Vis. Image Underst."},{"key":"ref_162","unstructured":"Zhang, Y. (2018). CSIF. figshare. Dataset."},{"key":"ref_163","doi-asserted-by":"crossref","first-page":"106553","DOI":"10.1016\/j.dib.2020.106553","article-title":"LEM+ dataset: For agricultural remote sensing applications","volume":"33","author":"Oldoni","year":"2020","journal-title":"Data Brief"},{"key":"ref_164","doi-asserted-by":"crossref","first-page":"4773","DOI":"10.1109\/JSTARS.2019.2917024","article-title":"Eyes in the Skies: A Data-Driven Fusion Approach to Identifying Drug Crops from Remote Sensing Images","volume":"12","author":"Ferreira","year":"2019","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_165","doi-asserted-by":"crossref","unstructured":"Ru\u00dfwurm, M., Pelletier, C., Zollner, M., Lef\u00e8vre, S., and K\u00f6rner, M. (2019). BreizhCrops: A time series dataset for crop type mapping. arXiv Prepr.","DOI":"10.5194\/isprs-archives-XLIII-B2-2020-1545-2020"},{"key":"ref_166","unstructured":"Rustowicz, R., Cheong, R., Wang, L., Ermon, S., Burke, M., and Lobell, D. (2021, October 17). Semantic Segmentation of Crop Type in Ghana Dataset. Available online: https:\/\/doi.org\/10.34911\/rdnt.ry138p."},{"key":"ref_167","unstructured":"Rustowicz, R., Cheong, R., Wang, L., Ermon, S., Burke, M., and Lobell, D. (2021, October 17). Semantic Segmentation of Crop Type in South Sudan Dataset. Available online: https:\/\/doi.org\/10.34911\/rdnt.v6kx6n."},{"key":"ref_168","doi-asserted-by":"crossref","first-page":"726","DOI":"10.1109\/JSTARS.2020.2971061","article-title":"DeepNEM: Deep Network Energy-Minimization for Agricultural Field Segmentation","volume":"13","author":"Torre","year":"2020","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_169","unstructured":"United States Geological Survey (2021, October 17). EarthExplorer, Available online: https:\/\/earthexplorer.usgs.gov\/."},{"key":"ref_170","unstructured":"European Space Agency (2021, October 17). Copernicus Open Access Hub. Available online: https:\/\/scihub.copernicus.eu\/dhus\/#\/home."},{"key":"ref_171","doi-asserted-by":"crossref","first-page":"4699","DOI":"10.1109\/JSTARS.2021.3073965","article-title":"TimeSen2Crop: A Million Labeled Samples Dataset of Sentinel 2 Image Time Series for Crop-Type Classification","volume":"14","author":"Weikmann","year":"2021","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_172","doi-asserted-by":"crossref","first-page":"219","DOI":"10.1016\/j.future.2019.02.050","article-title":"Edge computing: A survey","volume":"97","author":"Khan","year":"2019","journal-title":"Future Gener. Comput. Syst."},{"key":"ref_173","doi-asserted-by":"crossref","first-page":"2810","DOI":"10.1109\/JSTARS.2019.2920077","article-title":"High-Performance Time-Series Quantitative Retrieval from Satellite Images on a GPU Cluster","volume":"12","author":"Liu","year":"2019","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_174","first-page":"1","article-title":"A Review on Mobile Cloud Computing and Issues in it","volume":"75","author":"Hakak","year":"2013","journal-title":"Int. J. Comput. Appl."},{"key":"ref_175","first-page":"1","article-title":"Vision Based Displacement Detection for Stabilized UAV Control on Cloud Server","volume":"2016","author":"Jeong","year":"2016","journal-title":"Mob. Inf. Syst."},{"key":"ref_176","doi-asserted-by":"crossref","first-page":"1086","DOI":"10.3389\/fpls.2020.01086","article-title":"A Cloud-Based Environment for Generating Yield Estimation Maps from Apple Orchards Using UAV Imagery and a Deep Learning Technique","volume":"11","author":"Guanter","year":"2020","journal-title":"Front. Plant Sci."},{"key":"ref_177","doi-asserted-by":"crossref","first-page":"637","DOI":"10.1109\/JIOT.2016.2579198","article-title":"Edge Computing: Vision and Challenges","volume":"3","author":"Shi","year":"2016","journal-title":"IEEE Internet Things J."},{"key":"ref_178","doi-asserted-by":"crossref","first-page":"42","DOI":"10.1016\/j.simpat.2014.07.001","article-title":"Network-centric performance analysis of runtime application migration in mobile cloud computing","volume":"50","author":"Ahmed","year":"2015","journal-title":"Simul. Model. Pr. Theory"},{"key":"ref_179","doi-asserted-by":"crossref","first-page":"66919","DOI":"10.1109\/ACCESS.2019.2913957","article-title":"A UAV Platform Based on a Hyperspectral Sensor for Image Capturing and On-Board Processing","volume":"7","author":"Horstrand","year":"2019","journal-title":"IEEE Access"},{"key":"ref_180","doi-asserted-by":"crossref","unstructured":"Da Silva, J.F., Brito, A.V., De Lima, J.A.G., and De Moura, H.N. (2015, January 3\u20136). An embedded system for aerial image processing from unmanned aerial vehicles. Proceedings of the 2015 Brazilian Symposium on Computing Systems Engineering (SBESC), Foz do Iguacu, Brazil.","DOI":"10.1109\/SBESC.2015.36"},{"key":"ref_181","unstructured":"Xu, D., Li, T., Li, Y., Su, X., Tarkoma, S., Jiang, T., Crowcroft, J., and Hui, P. (2020). Edge Intelligence: Architectures, Challenges, and Applications. arXiv Prepr."},{"key":"ref_182","doi-asserted-by":"crossref","first-page":"2295","DOI":"10.1109\/JPROC.2017.2761740","article-title":"Efficient Processing of Deep Neural Networks: A Tutorial and Survey","volume":"105","author":"Sze","year":"2017","journal-title":"Proc. IEEE"},{"key":"ref_183","unstructured":"Han, S., Mao, H., and Dally, W.J. (2016, January 2\u20134). Deep Compression: Compressing Deep Neural Networks with Pruning, Trained Quantization and Huffman Coding. Proceedings of the International Conference on Learning Representations, San Juan, Puerto Rico."},{"key":"ref_184","doi-asserted-by":"crossref","unstructured":"Egli, S., and H\u00f6pke, M. (2020). CNN-Based Tree Species Classification Using High Resolution RGB Image Data from Automated UAV Observations. Remote Sens., 12.","DOI":"10.3390\/rs12233892"},{"key":"ref_185","doi-asserted-by":"crossref","unstructured":"Fountsop, A.N., Fendji, J.L.E.K., and Atemkeng, M. (2020). Deep Learning Models Compression for Agricultural Plants. Appl. Sci., 10.","DOI":"10.3390\/app10196866"},{"key":"ref_186","doi-asserted-by":"crossref","unstructured":"Blekos, K., Nousias, S., and Lalos, A.S. (2020, January 20\u201323). Efficient automated U-Net based tree crown delineation using UAV multi-spectral imagery on embedded devices. Proceedings of the 2020 IEEE 18th International Conference on Industrial Informatics (INDIN), Warwick, UK.","DOI":"10.1109\/INDIN45582.2020.9442183"},{"key":"ref_187","unstructured":"Iandola, F.N., Han, S., Moskewicz, M.W., Ashraf, K., Dally, W.J., and Keutzer, K. (2016). SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and <0.5 MB model size. arXiv Prepr."},{"key":"ref_188","doi-asserted-by":"crossref","unstructured":"Sandler, M., Howard, A., Zhu, M., Zhmoginov, A., and Chen, L. (2018, January 18\u201323). MobileNetV2: Inverted Residuals and Linear Bottlenecks. Proceedings of the 2018 IEEE Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00474"},{"key":"ref_189","doi-asserted-by":"crossref","unstructured":"Ma, N., Zhang, X., Zheng, H., and Sun, J. (2018, January 18\u201323). ShuffleNet V2: Practical Guidelines for Efficient CNN Architecture Design. Proceedings of the European Conference on Computer Vision, Salt Lake City, UT, USA.","DOI":"10.1007\/978-3-030-01264-9_8"},{"key":"ref_190","doi-asserted-by":"crossref","unstructured":"Wang, S., Zhao, J., Ta, N., Zhao, X., Xiao, M., and Wei, H. (2021). A real-time deep learning forest fire monitoring algorithm based on an improved Pruned + KD model. J. Real-Time Image Process., 1\u201311.","DOI":"10.1007\/s11554-021-01124-9"},{"key":"ref_191","doi-asserted-by":"crossref","first-page":"107071","DOI":"10.1016\/j.knosys.2021.107071","article-title":"Light-weight UAV object tracking network based on strategy gradient and attention mechanism","volume":"224","author":"Hua","year":"2021","journal-title":"Knowledge-Based Syst."},{"key":"ref_192","first-page":"1135","article-title":"Learning both weights and connections for efficient neural networks","volume":"28","author":"Han","year":"2015","journal-title":"Neural Inf. Process. Syst."},{"key":"ref_193","doi-asserted-by":"crossref","unstructured":"Srinivas, S., and Babu, R.V. (2015, January 7\u201310). Data-free Parameter Pruning for Deep Neural Networks. Proceedings of the British Machine Vision Conference 2015 (BMVC), Swansea, UK.","DOI":"10.5244\/C.29.31"},{"key":"ref_194","unstructured":"Li, H., Kadav, A., Durdanovic, I., Samet, H., and Graf, H.P. (2016, January 2\u20134). Pruning Filters for Efficient ConvNets. Proceedings of the International Conference on Learning Representations, San Juan, Puerto Rico."},{"key":"ref_195","doi-asserted-by":"crossref","unstructured":"Fan, W., Xu, Z., Liu, H., and Zongwei, Z. (2020, January 13\u201318). Machine Learning Agricultural Application Based on the Secure Edge Computing Platform. Proceedings of the International Conference on Machine Learning, Online.","DOI":"10.1007\/978-3-030-62223-7_18"},{"key":"ref_196","unstructured":"Lebedev, V., Ganin, Y., Rakhuba, M., Oseledets, I., and Lempitsky, V. (2015, January 7\u20139). Speeding-up Convolutional Neural Networks Using Fine-tuned CP-Decomposition. Proceedings of the International Conference on Learning Representations, San Diego, CA, USA."},{"key":"ref_197","doi-asserted-by":"crossref","unstructured":"Kim, Y., Park, E., Yoo, S., Choi, T., Yang, L., and Shin, D. (2015). Compression of deep convolutional neural networks for fast and low power mobile applications. arXiv Prepr.","DOI":"10.14257\/astl.2016.140.36"},{"key":"ref_198","doi-asserted-by":"crossref","unstructured":"Jaderberg, M., Vedaldi, A., and Zisserman, A. (2014, January 1\u20135). Speeding up Convolutional Neural Networks with Low Rank Expansions. Proceedings of the British Machine Vision Conference, Nottingham, UK.","DOI":"10.5244\/C.28.88"},{"key":"ref_199","doi-asserted-by":"crossref","first-page":"1943","DOI":"10.1109\/TPAMI.2015.2502579","article-title":"Accelerating Very Deep Convolutional Networks for Classification and Detection","volume":"38","author":"Zhang","year":"2016","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_200","doi-asserted-by":"crossref","first-page":"468","DOI":"10.1109\/JETCAS.2021.3098454","article-title":"A Low-Cost, Low-Power and Real-Time Image Detector for Grape Leaf Esca Disease Based on a Compressed CNN","volume":"11","author":"Falaschetti","year":"2021","journal-title":"IEEE J. Emerg. Sel. Top. Circuits Syst."},{"key":"ref_201","first-page":"2285","article-title":"Compressing Neural Networks with the Hashing Trick","volume":"3","author":"Chen","year":"2015","journal-title":"Int. Conf. Mach. Learn."},{"key":"ref_202","unstructured":"Dettmers, T. (2016, January 2\u20134). 8-Bit Approximations for Parallelism in Deep Learning. Proceedings of the International Conference on Learning Representations, San Juan, Puerto Rico."},{"key":"ref_203","unstructured":"Zhou, A., Yao, A., Guo, Y., Xu, L., and Chen, Y. (2017, January 24\u201326). Incremental Network Quantization: Towards Lossless CNNs with Low-precision Weights. Proceedings of the International Conference on Learning Representations, Toulon, France."},{"key":"ref_204","doi-asserted-by":"crossref","first-page":"5113","DOI":"10.1007\/s10462-020-09816-7","article-title":"A comprehensive survey on model compression and acceleration","volume":"53","author":"Choudhary","year":"2020","journal-title":"Artif. Intell. Rev."},{"key":"ref_205","unstructured":"Romero, A., Ballas, N., Kahou, S.E., Chassang, A., Gatta, C., and Bengio, Y. (2015, January 7\u20139). FitNets: Hints for Thin Deep Nets. Proceedings of the International Conference on Learning Representations, San Diego, CA, USA."},{"key":"ref_206","first-page":"3438","article-title":"Bayesian dark knowledge","volume":"28","author":"Korattikara","year":"2015","journal-title":"Neural Inf. Process. Syst."},{"key":"ref_207","first-page":"2760","article-title":"Paraphrasing Complex Network: Network Compression via Factor Transfer","volume":"31","author":"Kim","year":"2018","journal-title":"Neural Inf. Process. Syst."},{"key":"ref_208","doi-asserted-by":"crossref","first-page":"1789","DOI":"10.1007\/s11263-021-01453-z","article-title":"Knowledge Distillation: A Survey","volume":"129","author":"Gou","year":"2021","journal-title":"Int. J. Comput. Vis."},{"key":"ref_209","unstructured":"La Rosa, L.E.C., Oliveira, D.A.B., Zortea, M., Gemignani, B.H., and Feitosa, R.Q. (2020). Learning Geometric Features for Improving the Automatic Detection of Citrus Plantation Rows in UAV Images. IEEE Geosci. Remote Sens. Lett., 1\u20135."},{"key":"ref_210","first-page":"109","article-title":"Distilled-MobileNet Model of Convolutional Neural Network Simplified Structure for Plant Disease Recognition","volume":"3","author":"Qiu","year":"2021","journal-title":"Smart Agric."},{"key":"ref_211","doi-asserted-by":"crossref","unstructured":"Ding, M., Li, N., Song, Z., Zhang, R., Zhang, X., and Zhou, H. (2020, January 14\u201316). A Lightweight Action Recognition Method for Unmanned-Aerial-Vehicle Video. Proceedings of the 2020 IEEE 3rd International Conference on Electronics and Communication Engineering (ICECE), Xi\u2019an, China.","DOI":"10.1109\/ICECE51594.2020.9353008"},{"key":"ref_212","doi-asserted-by":"crossref","unstructured":"Dong, J., Ota, K., and Dong, M. (2020, January 17\u201319). Real-Time Survivor Detection in UAV Thermal Imagery Based on Deep Learning. Proceedings of the 2020 16th International Conference on Mobility, Sensing and Networking (MSN), Tokyo, Japan.","DOI":"10.1109\/MSN50589.2020.00065"},{"key":"ref_213","doi-asserted-by":"crossref","unstructured":"Sherstjuk, V., Zharikova, M., and Sokol, I. (2018, January 21\u201325). Forest fire monitoring system based on UAV team, remote sensing, and image processing. Proceedings of the 2018 IEEE Second International Conference on Data Stream Mining & Processing (DSMP), Lviv, Ukraine.","DOI":"10.1109\/DSMP.2018.8478590"},{"key":"ref_214","doi-asserted-by":"crossref","unstructured":"Sandino, J., Vanegas, F., Maire, F., Caccetta, P., Sanderson, C., and Gonzalez, F. (2020). UAV Framework for Autonomous Onboard Navigation and People\/Object Detection in Cluttered Indoor Environments. Remote Sens., 12.","DOI":"10.3390\/rs12203386"},{"key":"ref_215","doi-asserted-by":"crossref","first-page":"1301","DOI":"10.1007\/s11554-019-00888-5","article-title":"Real-time implementation of moving object detection in UAV videos using GPUs","volume":"17","author":"Jaiswal","year":"2020","journal-title":"J. Real-Time Image Process."},{"key":"ref_216","doi-asserted-by":"crossref","first-page":"404","DOI":"10.1007\/s11241-012-9166-9","article-title":"Multi-Core Real-Time Scheduling for Generalized Parallel Task Models","volume":"49","author":"Saifullah","year":"2013","journal-title":"Real-Time Syst."},{"key":"ref_217","doi-asserted-by":"crossref","unstructured":"Madro\u00f1al, D., Palumbo, F., Capotondi, A., and Marongiu, A. (2021, January 18\u201320). Unmanned Vehicles in Smart Farming: A Survey and a Glance at Future Horizons. Proceedings of the 2021 Drone Systems Engineering (DroneSE) and Rapid Simulation and Performance Evaluation: Methods and Tools Proceedings (RAPIDO\u201921), Budapest, Hungary.","DOI":"10.1145\/3444950.3444958"},{"key":"ref_218","doi-asserted-by":"crossref","unstructured":"Li, W., He, C., Fu, H., Zheng, J., Dong, R., Xia, M., Yu, L., and Luk, W. (2019). A Real-Time Tree Crown Detection Approach for Large-Scale Remote Sensing Images on FPGAs. Remote Sens., 11.","DOI":"10.3390\/rs11091025"},{"key":"ref_219","doi-asserted-by":"crossref","unstructured":"Ma, Y., Li, Q., Chu, L., Zhou, Y., and Xu, C. (2021). Real-Time Detection and Spatial Localization of Insulators for UAV Inspection Based on Binocular Stereo Vision. Remote Sens., 13.","DOI":"10.3390\/rs13020230"},{"key":"ref_220","doi-asserted-by":"crossref","first-page":"1090","DOI":"10.3390\/rs4041090","article-title":"A Real-Time Method to Detect and Track Moving Objects (DATMO) from Unmanned Aerial Vehicles (UAVs) Using a Single Camera","volume":"4","author":"Thomas","year":"2012","journal-title":"Remote Sens."},{"key":"ref_221","doi-asserted-by":"crossref","unstructured":"Opromolla, R., Fasano, G., and Accardo, D. (2018). A Vision-Based Approach to UAV Detection and Tracking in Cooperative Applications. Sensors, 18.","DOI":"10.3390\/s18103391"},{"key":"ref_222","doi-asserted-by":"crossref","unstructured":"Li, B., Zhu, Y., Wang, Z., Li, C., Peng, Z.-R., and Ge, L. (2018). Use of Multi-Rotor Unmanned Aerial Vehicles for Radioactive Source Search. Remote Sens., 10.","DOI":"10.3390\/rs10050728"},{"key":"ref_223","doi-asserted-by":"crossref","unstructured":"Rebou\u00e7as, R.A., Da Cruz Eller, Q., Habermann, M., and Shiguemori, E.H. (2013, January 4\u20138). Embedded system for visual odometry and localization of moving objects in images acquired by unmanned aerial vehicles. Proceedings of the 2013 III Brazilian Symposium on Computing Systems Engineering, Rio De Janeiro, Brazil.","DOI":"10.1109\/SBESC.2013.34"},{"key":"ref_224","doi-asserted-by":"crossref","unstructured":"Kizar, S.N., and Satyanarayana, G. (2016, January 9\u201310). Object detection and location estimation using SVS for UAVs. Proceedings of the International Conference on Automatic Control and Dynamic Optimization Techniques, Pune, India.","DOI":"10.1109\/ICACDOT.2016.7877721"},{"key":"ref_225","doi-asserted-by":"crossref","first-page":"9149","DOI":"10.1007\/s11042-018-6508-1","article-title":"A video-based object detection and tracking system for weight sensitive UAVs","volume":"78","author":"Abughalieh","year":"2019","journal-title":"Multimedia Tools Appl."},{"key":"ref_226","doi-asserted-by":"crossref","unstructured":"Choi, H., Geeves, M., Alsalam, B., and Gonzalez, F. (2016, January 5\u201312). Open source computer-vision based guidance system for UAVs on-board decision making. Proceedings of the 2016 IEEE aerospace conference, Big Sky, MO, USA.","DOI":"10.1109\/AERO.2016.7500600"},{"key":"ref_227","doi-asserted-by":"crossref","unstructured":"De Oliveira, D.C., and Wehrmeister, M.A. (2018). Using Deep Learning and Low-Cost RGB and Thermal Cameras to Detect Pedestrians in Aerial Images Captured by Multirotor UAV. Sensors, 18.","DOI":"10.3390\/s18072244"},{"key":"ref_228","doi-asserted-by":"crossref","unstructured":"Kersnovski, T., Gonzalez, F., and Morton, K. (2017, January 4\u201311). A UAV system for autonomous target detection and gas sensing. Proceedings of the 2017 IEEE aerospace conference, Big Sky, MO, USA.","DOI":"10.1109\/AERO.2017.7943675"},{"key":"ref_229","doi-asserted-by":"crossref","unstructured":"Basso, M., Stocchero, D., Henriques, R.V.B., Vian, A.L., Bredemeier, C., Konzen, A.A., and De Freitas, E.P. (2019). Proposal for an Embedded System Architecture Using a GNDVI Algorithm to Support UAV-Based Agrochemical Spraying. Sensors, 19.","DOI":"10.3390\/s19245397"},{"key":"ref_230","doi-asserted-by":"crossref","unstructured":"Daryanavard, H., and Harifi, A. (2018, January 8\u201310). Implementing face detection system on uav using raspberry pi platform. Proceedings of the Iranian Conference on Electrical Engineering, Mashhad, Iran.","DOI":"10.1109\/ICEE.2018.8472476"},{"key":"ref_231","doi-asserted-by":"crossref","unstructured":"Safadinho, D., Ramos, J., Ribeiro, R., Filipe, V., Barroso, J., and Pereira, A. (2020). UAV Landing Using Computer Vision Techniques for Human Detection. Sensors, 20.","DOI":"10.3390\/s20030613"},{"key":"ref_232","doi-asserted-by":"crossref","unstructured":"Natesan, S., Armenakis, C., Benari, G., and Lee, R. (2018). Use of UAV-Borne Spectrometer for Land Cover Classification. Drones, 2.","DOI":"10.3390\/drones2020016"},{"key":"ref_233","doi-asserted-by":"crossref","unstructured":"Benhadhria, S., Mansouri, M., Benkhlifa, A., Gharbi, I., and Jlili, N. (2021). VAGADRONE: Intelligent and Fully Automatic Drone Based on Raspberry Pi and Android. Appl. Sci., 11.","DOI":"10.3390\/app11073153"},{"key":"ref_234","doi-asserted-by":"crossref","unstructured":"Ayoub, N., and Schneider-Kamp, P. (2021). Real-Time On-Board Deep Learning Fault Detection for Autonomous UAV Inspections. Electronics, 10.","DOI":"10.3390\/electronics10091091"},{"key":"ref_235","doi-asserted-by":"crossref","unstructured":"Xu, L., and Luo, H. (2016, January 6\u20139). Towards autonomous tracking and landing on moving target. Proceedings of the 2016 IEEE International Conference on Real-time Computing and Robotics (RCAR), Angkor Wat, Cambodia.","DOI":"10.1109\/RCAR.2016.7784101"},{"key":"ref_236","doi-asserted-by":"crossref","first-page":"40","DOI":"10.1109\/MM.2017.4241339","article-title":"Flying IoT: Toward Low-Power Vision in the Sky","volume":"37","author":"Genc","year":"2017","journal-title":"IEEE Micro"},{"key":"ref_237","doi-asserted-by":"crossref","unstructured":"Meng, L., Peng, Z., Zhou, J., Zhang, J., Lu, Z., Baumann, A., and Du, Y. (2020). Real-Time Detection of Ground Objects Based on Unmanned Aerial Vehicle Remote Sensing with Deep Learning: Application in Excavator Detection for Pipeline Safety. Remote Sens., 12.","DOI":"10.3390\/rs12010182"},{"key":"ref_238","doi-asserted-by":"crossref","unstructured":"Tijtgat, N., Van Ranst, W., Goedeme, T., Volckaert, B., and De Turck, F. (2017, January 22\u201329). Embedded real-time object detection for a UAV warning system. Proceedings of the IEEE International Conference on Computer Vision Workshops, Venice, Italy.","DOI":"10.1109\/ICCVW.2017.247"},{"key":"ref_239","doi-asserted-by":"crossref","unstructured":"Meli\u00e1n, J., Jim\u00e9nez, A., D\u00edaz, M., Morales, A., Horstrand, P., Guerra, R., L\u00f3pez, S., and L\u00f3pez, J. (2021). Real-Time Hyperspectral Data Transmission for UAV-Based Acquisition Platforms. Remote Sens., 13.","DOI":"10.3390\/rs13050850"},{"key":"ref_240","doi-asserted-by":"crossref","unstructured":"Balamuralidhar, N., Tilon, S., and Nex, F. (2021). MultEYE: Monitoring System for Real-Time Vehicle Detection, Tracking and Speed Estimation from UAV Imagery on Edge-Computing Platforms. Remote Sens., 13.","DOI":"10.3390\/rs13040573"},{"key":"ref_241","doi-asserted-by":"crossref","first-page":"51171","DOI":"10.1109\/ACCESS.2019.2911709","article-title":"Low-Power and High-Speed Deep FPGA Inference Engines for Weed Classification at the Edge","volume":"7","author":"Lammie","year":"2019","journal-title":"IEEE Access"},{"key":"ref_242","doi-asserted-by":"crossref","unstructured":"Caba, J., D\u00edaz, M., Barba, J., Guerra, R., and L\u00f3pez, J. (2020). FPGA-Based On-Board Hyperspectral Imaging Compression: Benchmarking Performance and Energy Efficiency against GPU Implementations. Remote Sens., 12.","DOI":"10.3390\/rs12223741"},{"key":"ref_243","unstructured":"Zoph, B., and Le, Q.V. (2016). Neural architecture search with reinforcement learning. arXiv Prepr."},{"key":"ref_244","unstructured":"Liu, D., Kong, H., Luo, X., Liu, W., and Subramaniam, R. (2020). Bringing AI to Edge: From Deep Learning\u2019s Perspective. arXiv Prepr."},{"key":"ref_245","doi-asserted-by":"crossref","first-page":"1345","DOI":"10.1109\/TKDE.2009.191","article-title":"A Survey on Transfer Learning","volume":"22","author":"Pan","year":"2009","journal-title":"IEEE Trans. Knowl. Data Eng."},{"key":"ref_246","doi-asserted-by":"crossref","unstructured":"Xiao, T., Zhang, J., Yang, K., Peng, Y., and Zhang, Z. (2014, January 3\u20137). Error-driven incremental learning in deep convolutional neural network for large-scale image classification. Proceedings of the 22nd ACM international conference on Multimedia, Orlando, FL, USA.","DOI":"10.1145\/2647868.2654926"},{"key":"ref_247","doi-asserted-by":"crossref","first-page":"624","DOI":"10.1109\/TCCN.2020.3018159","article-title":"Collaborative Cloud-Edge-End Task Offloading in Mobile-Edge Computing Networks With Limited Communication Capability","volume":"7","author":"Kai","year":"2020","journal-title":"IEEE Trans. Cogn. Commun. Netw."},{"key":"ref_248","unstructured":"Bonawitz, K., Eichner, H., Grieskamp, W., Huba, D., Ingerman, A., Ivanov, V., Kiddon, C., Kone\u010dn\u00fd, J., Mazzocchi, S., and McMahan, H.B. (2019). Towards federated learning at scale: System design. arXiv Prepr."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/13\/21\/4387\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T07:23:40Z","timestamp":1760167420000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/13\/21\/4387"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,10,30]]},"references-count":248,"journal-issue":{"issue":"21","published-online":{"date-parts":[[2021,11]]}},"alternative-id":["rs13214387"],"URL":"https:\/\/doi.org\/10.3390\/rs13214387","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,10,30]]}}}