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For: Buters, Belton, Cross. Seed and Seedling Detection Using Unmanned Aerial Vehicles and Automated Image Classification in the Monitoring of Ecological Recovery. Drones 2019;3:53. [DOI: 10.3390/drones3030053] [Cited by in Crossref: 18] [Cited by in F6Publishing: 21] [Article Influence: 6.0] [Reference Citation Analysis]
Number Citing Articles
1 Ren D, Peng Y, Sun H, Yu M, Yu J, Liu Z. A Global Multi-Scale Channel Adaptation Network for Pine Wilt Disease Tree Detection on UAV Imagery by Circle Sampling. Drones 2022;6:353. [DOI: 10.3390/drones6110353] [Reference Citation Analysis]
2 Galuszynski NC, Duker R, Potts AJ, Kattenborn T. Automated mapping of Portulacaria afra canopies for restoration monitoring with convolutional neural networks and heterogeneous unmanned aerial vehicle imagery. PeerJ 2022;10:e14219. [DOI: 10.7717/peerj.14219] [Reference Citation Analysis]
3 Yuan J, Yan Q, Wang J, Xie J, Li R. Different responses of growth and physiology to warming and reduced precipitation of two co-existing seedlings in a temperate secondary forest. Front Plant Sci 2022;13:946141. [DOI: 10.3389/fpls.2022.946141] [Reference Citation Analysis]
4 Robinson JM, Harrison PA, Mavoa S, Breed MF. Existing and emerging uses of drones in restoration ecology. Methods Ecol Evol. [DOI: 10.1111/2041-210x.13912] [Cited by in Crossref: 2] [Cited by in F6Publishing: 2] [Article Influence: 2.0] [Reference Citation Analysis]
5 Huamanchahua D, Huamanchahua JC, Fanny-flores F. Use of Drones (UAVs) for Pollutant Identification in the Industrial Sector: A Technology Review. 2022 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS) 2022. [DOI: 10.1109/iemtronics55184.2022.9795812] [Reference Citation Analysis]
6 Liu S, Yin D, Feng H, Li Z, Xu X, Shi L, Jin X. Estimating maize seedling number with UAV RGB images and advanced image processing methods. Precision Agric. [DOI: 10.1007/s11119-022-09899-y] [Cited by in Crossref: 1] [Article Influence: 1.0] [Reference Citation Analysis]
7 Gao J, Gao X, Wu N, Yang H. Bi-directional LSTM with multi-scale dense attention mechanism for hyperspectral image classification. Multimed Tools Appl. [DOI: 10.1007/s11042-022-12809-z] [Cited by in Crossref: 2] [Cited by in F6Publishing: 1] [Article Influence: 2.0] [Reference Citation Analysis]
8 Finn A, Kumar P, Peters S, O'hehir J. Unsupervised spectral-spatial processing of drone imagery for identification of pine seedlings. ISPRS Journal of Photogrammetry and Remote Sensing 2022;183:363-88. [DOI: 10.1016/j.isprsjprs.2021.11.013] [Cited by in Crossref: 1] [Cited by in F6Publishing: 2] [Article Influence: 1.0] [Reference Citation Analysis]
9 Harrison PA, Camarretta N, Krisanski S, Bailey TG, Davidson NJ, Bain G, Hamer R, Gardiner R, Proft K, Taskhiri MS, Turner P, Turner D, Lucieer A. From communities to individuals: Using remote sensing to inform and monitor woodland restoration. Eco Management Restoration 2021;22:127-39. [DOI: 10.1111/emr.12505] [Cited by in Crossref: 2] [Cited by in F6Publishing: 2] [Article Influence: 2.0] [Reference Citation Analysis]
10 Margapuri V, Penumajji N, Neilsen M. Seed Classification using Synthetic Image Datasets Generated from Low-Altitude UAV Imagery. 2021 20th IEEE International Conference on Machine Learning and Applications (ICMLA) 2021. [DOI: 10.1109/icmla52953.2021.00026] [Reference Citation Analysis]
11 Banerjee BP, Sharma V, Spangenberg G, Kant S. Machine Learning Regression Analysis for Estimation of Crop Emergence Using Multispectral UAV Imagery. Remote Sensing 2021;13:2918. [DOI: 10.3390/rs13152918] [Cited by in Crossref: 4] [Cited by in F6Publishing: 4] [Article Influence: 4.0] [Reference Citation Analysis]
12 Mohan M, Richardson G, Gopan G, Aghai MM, Bajaj S, Galgamuwa GAP, Vastaranta M, Arachchige PSP, Amorós L, Corte APD, de-Miguel S, Leite RV, Kganyago M, Broadbent EN, Doaemo W, Shorab MAB, Cardil A. UAV-Supported Forest Regeneration: Current Trends, Challenges and Implications. Remote Sensing 2021;13:2596. [DOI: 10.3390/rs13132596] [Cited by in Crossref: 17] [Cited by in F6Publishing: 17] [Article Influence: 17.0] [Reference Citation Analysis]
13 Katarzyna K, Justyna S, Jakub S, Marcin S. Estimation of Bare Soil Moisture from Remote Sensing Indices in the 0.4–2.5 mm Spectral Range. Transactions on Aerospace Research 2021;2021:1-11. [DOI: 10.2478/tar-2021-0007] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
14 Haskins J, Endris C, Thomsen AS, Gerbl F, Fountain MC, Wasson K. UAV to Inform Restoration: A Case Study From a California Tidal Marsh. Front Environ Sci 2021;9:642906. [DOI: 10.3389/fenvs.2021.642906] [Cited by in Crossref: 6] [Cited by in F6Publishing: 7] [Article Influence: 6.0] [Reference Citation Analysis]
15 Cross AT, Zhong H, Lambers H. Incorporating rock in surface covers improves the establishment of native pioneer vegetation on alkaline mine tailings. Sci Total Environ 2021;768:145373. [PMID: 33736352 DOI: 10.1016/j.scitotenv.2021.145373] [Cited by in Crossref: 5] [Cited by in F6Publishing: 5] [Article Influence: 5.0] [Reference Citation Analysis]
16 Castrignanò A, Belmonte A, Antelmi I, Quarto R, Quarto F, Shaddad S, Sion V, Muolo MR, Ranieri NA, Gadaleta G, Bartoccetti E, Riefolo C, Ruggieri S, Nigro F. A geostatistical fusion approach using UAV data for probabilistic estimation of Xylella fastidiosa subsp. pauca infection in olive trees. Science of The Total Environment 2021;752:141814. [DOI: 10.1016/j.scitotenv.2020.141814] [Cited by in Crossref: 9] [Cited by in F6Publishing: 8] [Article Influence: 9.0] [Reference Citation Analysis]
17 Sharma K, Singh H, Sharma DK, Kumar A, Nayyar A, Krishnamurthi R. Dynamic Models and Control Techniques for Drone Delivery of Medications and Other Healthcare Items in COVID-19 Hotspots. Studies in Systems, Decision and Control 2021. [DOI: 10.1007/978-3-030-60039-6_1] [Cited by in Crossref: 5] [Cited by in F6Publishing: 5] [Article Influence: 5.0] [Reference Citation Analysis]
18 de la Fuente Castillo V, Díaz-álvarez A, Manso-callejo M, Serradilla García F. Grammar Guided Genetic Programming for Network Architecture Search and Road Detection on Aerial Orthophotography. Applied Sciences 2020;10:3953. [DOI: 10.3390/app10113953] [Cited by in Crossref: 5] [Cited by in F6Publishing: 5] [Article Influence: 2.5] [Reference Citation Analysis]
19 Feng A, Zhou J, Vories E, Sudduth KA. Evaluation of Cotton Emergence Using UAV-Based Narrow-Band Spectral Imagery with Customized Image Alignment and Stitching Algorithms. Remote Sensing 2020;12:1764. [DOI: 10.3390/rs12111764] [Cited by in Crossref: 10] [Cited by in F6Publishing: 10] [Article Influence: 5.0] [Reference Citation Analysis]
20 Buters T, Belton D, Cross A. Multi-Sensor UAV Tracking of Individual Seedlings and Seedling Communities at Millimetre Accuracy. Drones 2019;3:81. [DOI: 10.3390/drones3040081] [Cited by in Crossref: 13] [Cited by in F6Publishing: 14] [Article Influence: 4.3] [Reference Citation Analysis]
21 Hunt ER, Daughtry CST, Stern AJ, Russ AL. Linear Transects of Imagery Increase Crop Monitoring Efficiency Using Fixed‐Wing Unmanned Aircraft Systems. Agric environ lett 2019;4:190040. [DOI: 10.2134/ael2019.09.0040] [Reference Citation Analysis]