EVALUATION OF RGB-BASED VEGETATION INDICES FROM UAV IMAGERY TO ESTIMATE FORAGE YIELD IN GRASSLAND

55 citations as recorded by crossref.

1. Prediction of Cereal Rye Cover Crop Biomass and Nutrient Accumulation Using Multi-Temporal Unmanned Aerial Vehicle Based Visible-Spectrum Vegetation Indices R. Roth et al. https://doi.org/10.3390/rs15030580
2. A Review on UAV-Based Applications for Precision Agriculture D. Tsouros et al. https://doi.org/10.3390/info10110349
3. Selection of a Complex of Informative Features for Assessing the Internal Characteristics of Eggs for Consumption T. Kolev et al. https://doi.org/10.3390/agriengineering7030053
4. Early Visible Greenness Change in Forest Burned Areas Across Burn Severity and Mountainous Topography Using UAV RGB Imagery Q. Gu et al. https://doi.org/10.3390/fire9060258
5. UAV-Based Estimation of Grain Yield for Plant Breeding: Applied Strategies for Optimizing the Use of Sensors, Vegetation Indices, Growth Stages, and Machine Learning Algorithms L. Prey et al. https://doi.org/10.3390/rs14246345
6. Evaluating Remote Sensing Resolutions and Machine Learning Methods for Biomass Yield Prediction in Northern Great Plains Pastures S. Subhashree et al. https://doi.org/10.3390/agriculture15050505
7. Optimizing Observation Plans for Identifying Faxon Fir (Abies fargesii var. Faxoniana) Using Monthly Unmanned Aerial Vehicle Imagery W. Shi et al. https://doi.org/10.3390/rs15082205
8. Selecting High Forage-Yielding Alfalfa Populations in a Mediterranean Drought-Prone Environment Using High-Throughput Phenotyping H. Noushahi et al. https://doi.org/10.3390/rs17091517
9. Utilizing Spectral, Structural and Textural Features for Estimating Oat Above-Ground Biomass Using UAV-Based Multispectral Data and Machine Learning R. Dhakal et al. https://doi.org/10.3390/s23249708
10. Evaluation of water hyacinth (Eichhornia crassipes) response to herbicides using unmanned aerial system imagery A. Riner et al. https://doi.org/10.1017/wet.2025.28
11. RGB Imaging as a Tool for Remote Sensing of Characteristics of Terrestrial Plants: A Review A. Kior et al. https://doi.org/10.3390/plants13091262
12. Using multispectral data from an unmanned aerial system to estimate pasture depletion during grazing P. Alvarez-Hess et al. https://doi.org/10.1016/j.anifeedsci.2021.114880
13. UAV Monitoring of Stream Restorations J. Langhammer https://doi.org/10.3390/hydrology6020029
14. Urban tree analysis using unmanned aerial vehicle (uav) images and object-based classification (case study: university of indonesia campus) A. Wicaksono & R. Hernina https://doi.org/10.1088/1755-1315/683/1/012105
15. Unoccupied-Aerial-Systems-Based Biophysical Analysis of Montmorency Cherry Orchards: A Comparative Study G. Morgan & L. Stevenson https://doi.org/10.3390/drones8090494
16. UAV image-derived canopy traits for predicting alfalfa fall dormancy and forage yield in Mediterranean environments F. González et al. https://doi.org/10.3389/fpls.2026.1841672
17. Utilizing Spectral Indices on UAV Multispectral Images for Paddy Healthiness Detection: A Case Study in Perlis, Malaysia N. Aziz et al. https://doi.org/10.1051/e3sconf/202455703005
18. Monitoring Saffron Crops with UAVs K. Kiropoulos et al. https://doi.org/10.3390/telecom3020017
19. Estimation of Biophysical Parameters of Forage Cactus Under Different Agricultural Systems Through Vegetation Indices and Machine Learning Using RGB Images Acquired with Unmanned Aerial Vehicles G. Silva et al. https://doi.org/10.3390/agriculture14122166
20. From badland to bushland? Analysis of geomorphic process dynamics and vegetation development in a sub-humid calanchi area based on high-resolution UAS data (2014–2024) M. Stark et al. https://doi.org/10.1016/j.catena.2026.110360
21. How do we feed grazing livestock in the future? A case for knowledge‐driven grazing systems J. Horn & J. Isselstein https://doi.org/10.1111/gfs.12577
22. A Comparison of Several UAV-Based Multispectral Imageries in Monitoring Rice Paddy (A Case Study in Paddy Fields in Tottori Prefecture, Japan) M. Dimyati et al. https://doi.org/10.3390/ijgi12020036
23. Comparison of Green Red Vegetation Index (GRVI) with other RGB vegetation indices in forage crop breeding T. Kikawada et al. https://doi.org/10.1270/jsbbr.22J07
24. Image-based yield prediction for tall fescue using random forests and convolutional neural networks S. Ghysels et al. https://doi.org/10.3389/fpls.2025.1549099
25. EVALUATION OF URBAN VEGETATION COVER USING GOOGLE EARTH HIGH-RESOLUTION IMAGES: A CASE STUDY OF ELBASANI CITY N. Frashëri & H. Haska https://doi.org/10.67537/ajnts3103027
26. Using UAV RGB Image for Object-based Feature Detection A. Iyoob et al. https://doi.org/10.1016/j.procs.2025.03.290
27. Estimating biomass in temperate grassland with high resolution canopy surface models from UAV-based RGB images and vegetation indices U. Lussem et al. https://doi.org/10.1117/1.JRS.13.034525
28. A framework for national-scale predictions of forage dry mass in Senegal: UAVs as an intermediate step between field measurements and Sentinel-2 images M. Nungi-Pambu et al. https://doi.org/10.1080/01431161.2023.2290992
29. The use of RGB vegetation indices to predict the buckwheat yield at the flowering stage E. Karunathilake et al. https://doi.org/10.1007/s12892-025-00283-6
30. Rice yield prediction using UAV-mounted RGB sensors and machine learning algorithms R. Tripathi et al. https://doi.org/10.1007/s43538-025-00479-y
31. Catchworks: A Historical Water-Distribution System on Mountain Meadows in Central Slovakia M. Slámová et al. https://doi.org/10.3390/su13031107
32. A two-level fusion for building irregularity detection in post-disaster VHR oblique images M. Kakooei & Y. Baleghi https://doi.org/10.1007/s12145-020-00449-6
33. Application of UAS with Remote Sensing Sensors for the Location of Marks in the Archaeological Site of the Europos, Greece D. Kaimaris & D. Tsokas https://doi.org/10.3390/rs15153843
34. Identification of damage severity in Fraxinus excelsior L. trees caused by ash dieback using multisensory and multitemporal UAV imagery L. Buchner et al. https://doi.org/10.1016/j.foreco.2025.122660
35. Spatiotemporal variations in alpine grassland quality and its response to climate change and grazing activity on the Qinghai-Tibet plateau T. Zhan et al. https://doi.org/10.1016/j.ecofro.2025.05.011
36. Machine learning methods for precision agriculture with UAV imagery: a review T. Shahi et al. https://doi.org/10.3934/era.2022218
37. Integrating UAV visible and multispectral imagery to assess grazing-induced vegetation responses in sandy grasslands Q. Guan et al. https://doi.org/10.3389/fpls.2025.1730583
38. Review: Cost-Effective Unmanned Aerial Vehicle (UAV) Platform for Field Plant Breeding Application G. Jang et al. https://doi.org/10.3390/rs12060998
39. Integrating UAV and Freely Available Space-Borne Data to Describe Tree Decline Across Semi-arid Mountainous Forests M. Ghasemi et al. https://doi.org/10.1007/s10666-023-09911-3
40. Roadmap for a participatory observatory for rangeland monitoring based on image analysis S. Taugourdeau et al. https://doi.org/10.3389/past.2025.15261
41. High-throughput phenotyping for non-destructive estimation of soybean fresh biomass using a machine learning model and temporal UAV data P. Ranđelović et al. https://doi.org/10.1186/s13007-023-01054-6
42. Limitation of RGB-Derived Vegetation Indices Using UAV Imagery for Biomass Estimation during Buckwheat Flowering E. Karunathilake et al. https://doi.org/10.32604/phyton.2025.067439
43. Asociación de índices de vegetación RGB con rendimiento y dosis de nitrógeno en trigo harinero A. Mandujano-Bueno et al. https://doi.org/10.29059/cienciauat.v19i2.1925
44. Unmanned aerial vehicle for the assessment of woody and herbaceous phytomass in Sahelian savanna M. Bossoukpe et al. https://doi.org/10.19182/remvt.36802
45. Estimation of forage biomass and vegetation cover in grasslands using UAV imagery J. Théau et al. https://doi.org/10.1371/journal.pone.0245784
46. From consumer to RTK-enabled UAVs: A comparative assessment of vineyard mapping accuracy and spectral indices J. Rodrigo-Comino et al. https://doi.org/10.1007/s11119-026-10363-4
47. Analisis Usia Tanaman Padi Berdasarkan Berbagai Indeks Vegetasi Menggunakan Citra Kamera F. Ramadhani & A. Jalil https://doi.org/10.47134/trilogi.v2i2.41
48. Classification of Zones with Different Levels of Atmospheric Pollution Through a Set of Optical Features Extracted from Mulberry and Linden Leaves D. Karadzhova et al. https://doi.org/10.3390/environments13040185
49. Estimating herbaceous aboveground biomass in Sahelian rangelands using Structure from Motion data collected on the ground and by UAV S. Taugourdeau et al. https://doi.org/10.1002/ece3.8867
50. RGB images-based vegetative index for phenotyping kenaf (Hibiscus cannabinus L.) G. Han et al. https://doi.org/10.1371/journal.pone.0256978
51. Use of remote sensing data obtained from UAVs to assess the biomass productivity of Silphium perfoliatum T. Myslyva et al. https://doi.org/10.29235/1817-7204-2021-59-2-186-197
52. Monitoring of maize cultivation with the aid of precision agriculture techniques T. Moreira et al. https://doi.org/10.54033/cadpedv22n10-158
53. BIOPHYSICAL AND NUTRITIONAL MAPPING AND MONITORING OF UROCHLOA SSP. USING UAV AERIAL IMAGES R. Coelho et al. https://doi.org/10.56083/RCV6N6-046
54. Monitoring the Degree of Mosaic Disease in Apple Leaves Using Hyperspectral Images D. Jiang et al. https://doi.org/10.3390/rs15102504
55. Machine Learning-Based Classification of Asbestos-Containing Roofs Using Airborne RGB and Thermal Imagery G. Kaplan et al. https://doi.org/10.3390/su15076067