{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,11]],"date-time":"2026-06-11T03:44:04Z","timestamp":1781149444079,"version":"3.54.1"},"reference-count":46,"publisher":"Springer Science and Business Media LLC","issue":"17","license":[{"start":{"date-parts":[[2023,12,4]],"date-time":"2023-12-04T00:00:00Z","timestamp":1701648000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,12,4]],"date-time":"2023-12-04T00:00:00Z","timestamp":1701648000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61771034"],"award-info":[{"award-number":["61771034"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Neural Comput &amp; Applic"],"published-print":{"date-parts":[[2024,6]]},"DOI":"10.1007\/s00521-023-09230-4","type":"journal-article","created":{"date-parts":[[2023,12,4]],"date-time":"2023-12-04T17:02:07Z","timestamp":1701709327000},"page":"9779-9803","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":9,"title":["Rapid density estimation of tiny pests from sticky traps using Qpest RCNN in conjunction with UWB-UAV-based IoT framework"],"prefix":"10.1007","volume":"36","author":[{"given":"Yong","family":"Juan","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ziyi","family":"Ke","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ziqiang","family":"Chen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Debiao","family":"Zhong","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Weifeng","family":"Chen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Liang","family":"Yin","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2023,12,4]]},"reference":[{"issue":"4","key":"9230_CR1","doi-asserted-by":"publisher","first-page":"1062","DOI":"10.1016\/j.aspen.2020.08.016","volume":"23","author":"YK Shin","year":"2020","unstructured":"Shin YK, Kim SB, Kim D-SJJOA-PE (2020) Attraction characteristics of insect pests and natural enemies according to the vertical position of yellow sticky traps in a strawberry farm with high-raised bed cultivation. J Asia-Pacific Entomol 23(4):1062\u20131066","journal-title":"J Asia-Pacific Entomol"},{"key":"9230_CR2","doi-asserted-by":"publisher","first-page":"74","DOI":"10.1016\/j.cropro.2013.01.009","volume":"47","author":"DM Pinto-Zevallos","year":"2013","unstructured":"Pinto-Zevallos DM, V\u00e4nninen IJCP (2013) Yellow sticky traps for decision-making in whitefly management: what has been achieved? Crop Prot 47:74\u201384","journal-title":"Crop Prot"},{"issue":"1","key":"9230_CR3","doi-asserted-by":"publisher","first-page":"25","DOI":"10.1016\/j.aspen.2008.03.002","volume":"11","author":"M Qiao","year":"2008","unstructured":"Qiao M et al (2008) Density estimation of Bemisia tabaci (Hemiptera: Aleyrodidae) in a greenhouse using sticky traps in conjunction with an image processing system. J Asia-Pac Entomol 11(1):25\u201329","journal-title":"J Asia-Pac Entomol"},{"issue":"11","key":"9230_CR4","first-page":"1341","volume":"29","author":"DG Hall","year":"2010","unstructured":"Hall DG, S\u00e9tamou M, Mizell RFJCP III (2010) A comparison of sticky traps for monitoring Asian citrus psyllid (Diaphorina citri Kuwayama). Elsevier 29(11):1341\u20131346","journal-title":"Elsevier"},{"key":"9230_CR5","doi-asserted-by":"publisher","first-page":"646","DOI":"10.1016\/j.matcom.2020.11.022","volume":"182","author":"LAR Rodr\u00edguez","year":"2021","unstructured":"Rodr\u00edguez LAR, Casta\u00f1eda-Miranda CL, Luci\u00f3 MM, Sol\u00eds-S\u00e1nchez LO, Casta\u00f1eda-Miranda RJM, Simulation CI (2021) Quarternion color image processing as an alternative to classical grayscale conversion approaches for pest detection using yellow sticky traps. Math Comput Simul 182:646\u2013660","journal-title":"Math Comput Simul"},{"key":"9230_CR6","doi-asserted-by":"publisher","first-page":"82","DOI":"10.1016\/j.biosystemseng.2016.11.001","volume":"153","author":"Y Sun","year":"2017","unstructured":"Sun Y et al (2017) A smart-vision algorithm for counting whiteflies and thrips on sticky traps using two-dimensional Fourier transform spectrum. Biosys Eng 153:82\u201388","journal-title":"Biosys Eng"},{"issue":"2","key":"9230_CR7","first-page":"225","volume":"15","author":"H Lee","year":"2022","unstructured":"Lee H, Choi W, Eom S, Park J-JJJOA-PB (2022) \"Rapid estimation of the density of whiteflies (Hemiptera: Aleyrodidae) on sticky traps in paprika greenhouses using the presence\u2013absence model. J Asia-Pac Biodiv 15(2):225\u2013230","journal-title":"J Asia-Pac Biodiv"},{"key":"9230_CR8","doi-asserted-by":"publisher","DOI":"10.1016\/j.compag.2021.106048","volume":"183","author":"W Li","year":"2021","unstructured":"Li W et al (2021) Field detection of tiny pests from sticky trap images using deep learning in agricultural greenhouse. Comput Electron Agric 183:106048","journal-title":"Comput Electron Agric"},{"key":"9230_CR9","doi-asserted-by":"publisher","DOI":"10.1016\/j.iot.2020.100187","volume":"18","author":"AD Boursianis","year":"2022","unstructured":"Boursianis AD et al (2022) Internet of things (IoT) and agricultural unmanned aerial vehicles (UAVs) in smart farming: a comprehensive review. Internet of Things 18:100187","journal-title":"Internet of Things"},{"key":"9230_CR10","first-page":"100258","volume":"16","author":"K Srivastava","year":"2019","unstructured":"Srivastava K, Bhutoria AJ, Sharma JK, Sinha A, PCJRSAS Pandey and Environment (2019) UAVs technology for the development of GUI based application for precision agriculture and environmental research. Remote Sens Appl: Soc Environ 16:100258","journal-title":"Remote Sens Appl: Soc Environ"},{"key":"9230_CR11","first-page":"100449","volume":"24","author":"S Anand","year":"2022","unstructured":"Anand S, Sharma AJMS (2022) AgroKy: an approach for enhancing security services in precision agriculture. Meas: Sens 24:100449","journal-title":"Meas: Sens"},{"key":"9230_CR12","first-page":"200102","volume":"16","author":"Kamsu-Foguem B CouliablyS","year":"2022","unstructured":"CouliablyS Kamsu-Foguem B, Kamissoko D, Traore DJISwA, (2022) Deep learning for precision agriculture: A bibliometric analysis. Intel Syst Appl 16:200102","journal-title":"Intel Syst Appl"},{"key":"9230_CR13","doi-asserted-by":"publisher","first-page":"237","DOI":"10.1016\/j.jrurstud.2022.04.002","volume":"92","author":"M Cui","year":"2022","unstructured":"Cui M, Qian J, LJJoRS Cui (2022) Developing precision agriculture through creating information processing capability in rural China. J Rural Stud 92:237\u2013252","journal-title":"J Rural Stud"},{"key":"9230_CR14","doi-asserted-by":"publisher","first-page":"102087","DOI":"10.1016\/j.techsoc.2022.102087","volume":"71","author":"ED Hanson","year":"2022","unstructured":"Hanson ED, Cossette MK, Roberts DCJTiS (2022) The adoption and usage of precision agriculture technologies in North Dakota. Tech Soc 71:102087","journal-title":"Tech Soc"},{"key":"9230_CR15","doi-asserted-by":"publisher","first-page":"105457","DOI":"10.1016\/j.compag.2020.105457","volume":"174","author":"Y Ampatzidis","year":"2020","unstructured":"Ampatzidis Y, Partel V, Costa LJ (2020) Agroview: Cloud-based application to process, analyze and visualize UAV-collected data for precision agriculture applications utilizing artificial intelligence. Comput Electron Agri 174:105457","journal-title":"Comput Electron Agri"},{"issue":"84","key":"9230_CR16","first-page":"95","volume":"155","author":"L Comba","year":"2018","unstructured":"Comba L, Biglia A, Aimonino DR, Gay PJC, EI Agriculture (2018) Unsupervised detection of vineyards by 3D point-cloud UAV photogrammetry for precision agriculture. Comput Electron Agric 155(84):95","journal-title":"Comput Electron Agric"},{"issue":"30","key":"9230_CR17","doi-asserted-by":"publisher","first-page":"251","DOI":"10.1016\/j.ifacol.2019.12.530","volume":"52","author":"TJI-P Elmokadem","year":"2019","unstructured":"Elmokadem TJI-P (2019) Distributed coverage control of quadrotor multi-UAV systems for precision agriculture. IFAC-PapersOnLine 52(30):251\u2013256","journal-title":"IFAC-PapersOnLine"},{"key":"9230_CR18","doi-asserted-by":"publisher","first-page":"107148","DOI":"10.1016\/j.comnet.2020.107148","volume":"172","author":"P Radoglou-Grammatikis","year":"2020","unstructured":"Radoglou-Grammatikis P, Sarigiannidis P, Lagkas T, Moscholios IJCN (2020) A compilation of UAV applications for precision agriculture. Comput Netw 172:107148","journal-title":"Comput Netw"},{"key":"9230_CR19","doi-asserted-by":"publisher","first-page":"242","DOI":"10.1016\/j.neucom.2022.11.020","volume":"518","author":"J Su","year":"2022","unstructured":"Su J, Zhu X, Li S, Chen W-HJN (2022) AI meets UAVs: A survey on AI empowered UAV perception systems for precision agriculture. Neurocomputing 518:242\u2013270","journal-title":"Neurocomputing"},{"key":"9230_CR20","doi-asserted-by":"publisher","first-page":"107912","DOI":"10.1016\/j.compeleceng.2022.107912","volume":"100","author":"PK Singh","year":"2022","unstructured":"Singh PK, Sharma AJC, Engineering E (2022) An intelligent WSN-UAV-based IoT framework for precision agriculture application. Comput Electr Eng 100:107912","journal-title":"Comput Electr Eng"},{"key":"9230_CR21","doi-asserted-by":"publisher","DOI":"10.1016\/j.compag.2020.105836","volume":"179","author":"EC Tetila","year":"2020","unstructured":"Tetila EC et al (2020) Detection and classification of soybean pests using deep learning with UAV images. Comput Electron Agric 179:105836","journal-title":"Comput Electron Agric"},{"key":"9230_CR22","unstructured":"Nieuwenhuizen AT, Hemming J, Suh HK (2018) Detection and classification of insects on stick-traps in a tomato crop using Faster R-CNN. In: The Netherlands Conference on Computer Vision"},{"key":"9230_CR23","unstructured":"Nieuwenhuizen A et al (2019) Raw data from Yellow Sticky Traps with insects for training of deep learning convolutional neural network for object detection"},{"key":"9230_CR24","doi-asserted-by":"crossref","unstructured":"DesernoM, Briassouli A (2021) Faster r-CNN and efficientnet for accurate insect identification in a relabeled yellow sticky traps dataset. In: 2021 IEEE international workshop on metrology for agriculture and forestry (MetroAgriFor), 2021: IEEE, pp 209\u2013214","DOI":"10.1109\/MetroAgriFor52389.2021.9628708"},{"issue":"12","key":"9230_CR25","first-page":"135","volume":"2019","author":"H Mo","year":"2019","unstructured":"Mo H, Zhang J, Ma Y (2019) Application and system design of dw1000 in UAV cluster. Electron World 2019(12):135\u2013137","journal-title":"Electron World"},{"issue":"1","key":"9230_CR26","first-page":"83","volume":"1","author":"Y Zheng","year":"2019","unstructured":"Zheng Y, Xue L, Dong L (2019) Formation control of mobile robots with UWB localization technology. Chin J Intell Sci Technol 1(1):83\u201387","journal-title":"Chin J Intell Sci Technol"},{"issue":"2","key":"9230_CR27","first-page":"168","volume":"14","author":"B Liu","year":"2019","unstructured":"Liu B, Zhang R, Li Y, Wang Y, Fu P (2019) An UWB-based wireless module set with transparent transmission. J China Acad Electron Inf Technol 14(2):168\u2013176","journal-title":"J China Acad Electron Inf Technol"},{"issue":"4","key":"9230_CR28","first-page":"302","volume":"49","author":"D Shi","year":"2022","unstructured":"Shi D, Liu C, She F (2022) Cooperation localization method based on location confidence of multi-UAV in GPS-denied environment. Comput Sci 49(4):302\u2013311","journal-title":"Comput Sci"},{"key":"9230_CR29","volume-title":"Design and realization of flight control for UAV Group's autonomous formation","author":"S Yu","year":"2020","unstructured":"Yu S (2020) Design and realization of flight control for UAV Group\u2019s autonomous formation. Inner Mongolia University, Hohhot"},{"issue":"10","key":"9230_CR30","first-page":"118","volume":"39","author":"Tong Q HuZ","year":"2020","unstructured":"HuZ Tong Q, Liu SJJCJU (2020) Lane departure identification and early warning method for autonomous vehicle. J Chongqing Jiaotong Univer (Nat Sci) 39(10):118\u2013125","journal-title":"J Chongqing Jiaotong Univer (Nat Sci)"},{"key":"9230_CR31","unstructured":"Wang L (2017) Research and implementation of car lane departure warning system based on image processing. University of Electronic Science and Technology of China"},{"issue":"1","key":"9230_CR32","first-page":"114","volume":"43","author":"G Li","year":"2021","unstructured":"Li G (2021) Design of UAV navigation system based on image recognition. J Agric Mech Res 43(1):114\u2013118","journal-title":"J Agric Mech Res"},{"issue":"22","key":"9230_CR33","doi-asserted-by":"publisher","first-page":"15781","DOI":"10.1007\/s00521-021-06198-x","volume":"33","author":"MH Ibrahim","year":"2021","unstructured":"Ibrahim MH (2021) ODBOT: outlier detection-based oversampling technique for imbalanced datasets learning. Neural Comput Appl 33(22):15781\u201315806","journal-title":"Neural Comput Appl"},{"key":"9230_CR34","unstructured":"RenS, He K, Girshick R, Sun JJA i n i p s (2015) Faster r-cnn: toward real-time object detection with region proposal networks. vol 28"},{"key":"9230_CR35","doi-asserted-by":"crossref","unstructured":"Girshick R (2015) Fast r-cnn. In: Proceedings of the IEEE international conference on computer vision, pp 1440\u20131448","DOI":"10.1109\/ICCV.2015.169"},{"key":"9230_CR36","doi-asserted-by":"crossref","unstructured":"Lin T-Y, Doll\u00e1r P, Girshick R, He K, Hariharan B, Belongie S (2017) Feature pyramid networks for object detection. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 2117\u20132125","DOI":"10.1109\/CVPR.2017.106"},{"key":"9230_CR37","doi-asserted-by":"crossref","unstructured":"Yang C, Huang Z, Wang N (2022) QueryDet: cascaded sparse query for accelerating high-resolution small object detection. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp 13668\u201313677","DOI":"10.1109\/CVPR52688.2022.01330"},{"key":"9230_CR38","doi-asserted-by":"crossref","unstructured":"Lin T-Y, Goyal P, Girshick R, He K, Doll\u00e1r P (2017) Focal loss for dense object detection. In: Proceedings of the IEEE international conference on computer vision, pp 2980\u20132988","DOI":"10.1109\/ICCV.2017.324"},{"key":"9230_CR39","doi-asserted-by":"crossref","unstructured":"BodlaN, Singh B, Chellappa R, Davis LS (2017) Soft-NMS-improving object detection with one line of code. In: Proceedings of the IEEE international conference on computer vision, 2017, pp 5561\u20135569","DOI":"10.1109\/ICCV.2017.593"},{"key":"9230_CR40","unstructured":"S\u00f8nderby SK, S\u00f8nderby CK, Maal\u00f8e L, Winther OJapa (2015) Recurrent spatial transformer networks. arXiv preprint arXiv:1509.05329"},{"key":"9230_CR41","doi-asserted-by":"publisher","unstructured":"Xu Z (2021) Research on traffic target detection algorithm based on improved faster RCNN. https:\/\/doi.org\/10.26976\/dcnki.Gchau.2021.000610","DOI":"10.26976\/dcnki.Gchau.2021.000610"},{"key":"9230_CR42","doi-asserted-by":"crossref","unstructured":"ZhuX, Cheng D, Zhang Z, Lin S, Dai J (2019) An empirical study of spatial attention mechanisms in deep networks. In: Proceedings of the IEEE\/CVF international conference on computer vision, pp. 6688\u20136697","DOI":"10.1109\/ICCV.2019.00679"},{"key":"9230_CR43","volume-title":"Smoothing methods in statistics","author":"JS Simonoff","year":"2012","unstructured":"Simonoff JS (2012) Smoothing methods in statistics. Springer, Berlin"},{"issue":"5","key":"9230_CR44","doi-asserted-by":"publisher","first-page":"2916","DOI":"10.1214\/10-AOS799","volume":"38","author":"ZI Botev","year":"2010","unstructured":"Botev ZI, Grotowski JF, Kroese DP (2010) Kernel density estimation via diffusion. Ann Stat 38(5):2916\u20132957","journal-title":"Ann Stat"},{"key":"9230_CR45","doi-asserted-by":"crossref","unstructured":"Cheng C (2022) Real-time mask detection based on SSD-MobileNetV2. In: 2022 IEEE 5th international conference on automation, electronics and electrical engineering (AUTEEE), IEEE, pp 761\u2013767","DOI":"10.1109\/AUTEEE56487.2022.9994442"},{"key":"9230_CR46","unstructured":"Redmon J, Farhadi AJapa (2018) Yolov3: an incremental improvement. arXiv e-prints"}],"container-title":["Neural Computing and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-023-09230-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00521-023-09230-4\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-023-09230-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,5,27]],"date-time":"2024-05-27T08:09:09Z","timestamp":1716797349000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00521-023-09230-4"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,12,4]]},"references-count":46,"journal-issue":{"issue":"17","published-print":{"date-parts":[[2024,6]]}},"alternative-id":["9230"],"URL":"https:\/\/doi.org\/10.1007\/s00521-023-09230-4","relation":{},"ISSN":["0941-0643","1433-3058"],"issn-type":[{"value":"0941-0643","type":"print"},{"value":"1433-3058","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,12,4]]},"assertion":[{"value":"28 March 2023","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"26 October 2023","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"4 December 2023","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare that they are unaware of competition for financial interests or personal relationships that may affect the work reported in this paper.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}