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Journal articles on the topic 'Imagerie RGB'

Author: Grafiati

Published: 1 June 2024

Last updated: 31 July 2025

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1

Vigneau, Nathalie, Corentin Chéron, Aleixandre Verger, and Frédéric Baret. "Imagerie aérienne par drone : exploitation des données pour l'agriculture de précision." Revue Française de Photogrammétrie et de Télédétection, no. 213 (April 26, 2017): 125–31. http://dx.doi.org/10.52638/rfpt.2017.203.

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La technologie des drones devenant plus accessible et les réglementations nationales encadrant les vols des drones commençant à émerger, de nombreuses sociétés utilisent désormais des drones pour réaliser des acquisitions d'images.Parmi celles-ci AIRINOV a choisi de se spécialiser dans l'agriculture et offre ses services aux agriculteurs ainsi qu'aux expérimentateurs. AIRINOV exploite les drones eBee de senseFly. Le drone a une envergure d'1 m pour un poids de 700 g charge comprise et son vol est entièrment automatique. Le vol est programmé à l'avance puis contrôlé par unauto-pilote connecté à

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Shen, Xin, Lin Cao, Bisheng Yang, Zhong Xu, and Guibin Wang. "Estimation of Forest Structural Attributes Using Spectral Indices and Point Clouds from UAS-Based Multispectral and RGB Imageries." Remote Sensing 11, no. 7 (2019): 800. http://dx.doi.org/10.3390/rs11070800.

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Forest structural attributes are key indicators for parameterization of forest growth models, which play key roles in understanding the biophysical processes and function of the forest ecosystem. In this study, UAS-based multispectral and RGB imageries were used to estimate forest structural attributes in planted subtropical forests. The point clouds were generated from multispectral and RGB imageries using the digital aerial photogrammetry (DAP) approach. Different suits of spectral and structural metrics (i.e., wide-band spectral indices and point cloud metrics) derived from multispectral an

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Priyankara, Prabath, and Takehiro Morimoto. "UAV Based Agricultural Crop Canopy Mapping for Crop Field Monitoring." Abstracts of the ICA 1 (July 15, 2019): 1. http://dx.doi.org/10.5194/ica-abs-1-303-2019.

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<p><strong>Abstract.</strong> Nowadays, mapping of agricultural crop canopy in different growing stages are vital data for crop field monitoring than field-based observations in large scale agricultural crop fields. By mapping agricultural crop canopy, it is very easy to analyse the status of an agricultural crop field by using different vegetation indices. Further, the data can be used to estimate the yield. These information are timely and reliable spatial information to the farmers and decision makers. Mapping of crop canopy in an agricultural crop field in different growi

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Purwanto, Anang Dwi, and Wikanti Asriningrum. "IDENTIFICATION OF MANGROVE FORESTS USING MULTISPECTRAL SATELLITE IMAGERIES." International Journal of Remote Sensing and Earth Sciences (IJReSES) 16, no. 1 (2019): 63. http://dx.doi.org/10.30536/j.ijreses.2019.v16.a3097.

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The visual identification of mangrove forests is greatly constrained by combinations of RGB composite. This research aims to determine the best combination of RGB composite for identifying mangrove forest in Segara Anakan, Cilacap using the Optimum Index Factor (OIF) method. The OIF method uses the standard deviation value and correlation coefficient from a combination of three image bands. The image data comprise Landsat 8 imagery acquired on 30 May 2013, Sentinel 2A imagery acquired on 18 March 2018 and images from SPOT 6 acquired on 10 January 2015. The results show that the band composites

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Chhatkuli, S., T. Satoh, and K. Tachibana. "MULTI SENSOR DATA INTEGRATION FOR AN ACCURATE 3D MODEL GENERATION." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XL-4/W5 (May 11, 2015): 103–6. http://dx.doi.org/10.5194/isprsarchives-xl-4-w5-103-2015.

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The aim of this paper is to introduce a novel technique of data integration between two different data sets, i.e. laser scanned RGB point cloud and oblique imageries derived 3D model, to create a 3D model with more details and better accuracy. In general, aerial imageries are used to create a 3D city model. Aerial imageries produce an overall decent 3D city models and generally suit to generate 3D model of building roof and some non-complex terrain. However, the automatically generated 3D model, from aerial imageries, generally suffers from the lack of accuracy in deriving the 3D model of road

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Argyrou, Argyro, Athos Agapiou, Apostolos Papakonstantinou, and Dimitrios D. Alexakis. "Comparison of Machine Learning Pixel-Based Classifiers for Detecting Archaeological Ceramics." Drones 7, no. 9 (2023): 578. http://dx.doi.org/10.3390/drones7090578.

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Recent improvements in low-altitude remote sensors and image processing analysis can be utilised to support archaeological research. Over the last decade, the increased use of remote sensing sensors and their products for archaeological science and cultural heritage studies has been reported in the literature. Therefore, different spatial and spectral analysis datasets have been applied to recognise archaeological remains or map environmental changes over time. Recently, more thorough object detection approaches have been adopted by researchers for the automated detection of surface ceramics.

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Mawardi, Sonny, Emi Sukiyah, and Iyan Haryanto. "Morphotectonic Characteristics Of Cisadane Watersshed Based On Satellite Images Analysis." Jurnal Geologi dan Sumberdaya Mineral 20, no. 3 (2019): 175. http://dx.doi.org/10.33332/jgsm.geologi.v20i3.464.

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Cisadane Watershed is one of the most rapidly growing areas and infrastructure development, and has developed as a residential, industrial, administrative centers and other economic activities. The purpose of this paper is to use remote sensing satellite imageries to identify the morphotectonic characteristics of the Cisadane watershed both qualitatively and quantitatively. Processing stereomodel, stereoplotting and stereocompilation on TerraSAR-X Digital Surface Model (DSM) and SPOT 6 imageries, produced the Digital Terrain Model (DTM) image, which has not been affected by land cover. Fusion

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8

Semah, Franck. "Imagerie médicale et épilepsies." Revue Générale Nucléaire, no. 4 (August 2001): 36–37. http://dx.doi.org/10.1051/rgn/20014036.

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Simes, Tomás, Luís Pádua, and Alexandra Moutinho. "Wildfire Burnt Area Severity Classification from UAV-Based RGB and Multispectral Imagery." Remote Sensing 16, no. 1 (2023): 30. http://dx.doi.org/10.3390/rs16010030.

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Wildfires present a significant threat to ecosystems and human life, requiring effective prevention and response strategies. Equally important is the study of post-fire damages, specifically burnt areas, which can provide valuable insights. This research focuses on the detection and classification of burnt areas and their severity using RGB and multispectral aerial imagery captured by an unmanned aerial vehicle. Datasets containing features computed from multispectral and/or RGB imagery were generated and used to train and optimize support vector machine (SVM) and random forest (RF) models. Hy

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Vanbrabant, Yasmin, Stephanie Delalieux, Laurent Tits, Klaas Pauly, Joke Vandermaesen, and Ben Somers. "Pear Flower Cluster Quantification Using RGB Drone Imagery." Agronomy 10, no. 3 (2020): 407. http://dx.doi.org/10.3390/agronomy10030407.

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High quality fruit production requires the regulation of the crop load on fruit trees by reducing the number of flowers and fruitlets early in the growing season, if the bearing is too high. Several automated flower cluster quantification methods based on proximal and remote imagery methods have been proposed to estimate flower cluster numbers, but their overall performance is still far from satisfactory. For other methods, the performance of the method to estimate flower clusters within a tree is unknown since they were only tested on images from one perspective. One of the main reported bott

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Goswami, Anurupa, Unmesh Khati, Ishan Goyal, Anam Sabir, and Sakshi Jain. "Automated Stock Volume Estimation Using UAV-RGB Imagery." Sensors 24, no. 23 (2024): 7559. http://dx.doi.org/10.3390/s24237559.

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Forests play a critical role in the global carbon cycle, with carbon storage being an important carbon pool in the terrestrial ecosystem with tree crown size serving as a versatile ecological indicator influencing factors such as tree growth, wind resistance, shading, and carbon sequestration. They help with habitat function, herbicide application, temperature regulation, etc. Understanding the relationship between tree crown area and stock volume is crucial, as it provides a key metric for assessing the impact of land-use changes on ecological processes. Traditional ground-based stock volume

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Dong, Junliang, Jian Zhang, Suo Zhang, Zhiyong Yu, Ziheng Song, and Tianya Meng. "Vegetation extraction through UAV RGB imagery and efficient feature selection." PLOS One 20, no. 5 (2025): e0322180. https://doi.org/10.1371/journal.pone.0322180.

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Accurate identification of vegetation in mining areas is crucial for conducting pre-mining ecological assessments and post-mining ecological monitoring. However, the vegetation in the mining area is always highly heterogeneous including both field crops and naturally scattered growing vegetation, which brings great challenges for fine vegetation mapping. Feature combinations are an important factor to influence the vegetation mapping. Thus, to effectively identify the vegetation, this study utilized an unmanned aerial vehicle (UAV) RGB image to extract vegetation indexes and textures, and then

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Aarts, L., A. LaRocque, B. Leblon, and A. Douglas. "USE OF UAV IMAGERY FOR EELGRASS MAPPING IN ATLANTIC CANADA." ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences V-3-2020 (August 3, 2020): 287–92. http://dx.doi.org/10.5194/isprs-annals-v-3-2020-287-2020.

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Abstract. Eelgrass beds are critical in coastal ecosystems and can be useful as a measure of nearshore ecosystem health. Population declines have been seen around the world, including in Atlantic Canada. Restoration has the potential to aid the eelgrass population. Traditionally, field-level protocols would be used to monitor restoration; however, using unmanned aerial vehicles (UAVs) would be faster, more cost-efficient, and produce images with higher spatial resolution. This project used RGB UAV imagery and data acquired over five sites with eelgrass beds in the northern part of the Shediac

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Pham, Daniel, Deepak Gautam, and Kathryn Sheffield. "Classifying Serrated Tussock Cover from Aerial Imagery Using RGB Bands, RGB Indices, and Texture Features." Remote Sensing 16, no. 23 (2024): 4538. https://doi.org/10.3390/rs16234538.

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Monitoring the location and severity of invasive plant infestations is critical to the management of their spread. Remote sensing can be an effective tool for mapping invasive plants due to its capture speed, continuous coverage, and low cost, compared to ground-based surveys. Serrated tussock (Nassella trichotoma) is a highly problematic invasive plant in Victoria, Australia, as it competes with the species in the communities that it invades. In this study, a workflow was developed and assessed for classifying the cover of serrated tussock in a mix of grazing pastures and grasslands. Using hi

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Berndt, Emily, Nicholas Elmer, Lori Schultz, and Andrew Molthan. "A Methodology to Determine Recipe Adjustments for Multispectral Composites Derived from Next-Generation Advanced Satellite Imagers." Journal of Atmospheric and Oceanic Technology 35, no. 3 (2018): 643–64. http://dx.doi.org/10.1175/jtech-d-17-0047.1.

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AbstractThe European Organisation for the Exploitation of Meteorological Satellites (EUMETSAT) began creating multispectral [i.e., red–green–blue (RGB)] composites in the early 2000s with the advent of the Meteosat-8 Spinning Enhanced Visible and Infrared Imager (SEVIRI). As new satellite sensors—for example, the Himawari-8 Advanced Himawari Imager (AHI) and the Geostationary Operational Environmental Satellite Advanced Baseline Imager (ABI)—become available, there is a need to adjust the EUMETSAT RGB standard thresholds (i.e., recipes) to account for differences in spectral characteristics, s

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Htun, Nyo Me, Toshiaki Owari, Satoshi N. Suzuki, et al. "Spatial Localization of Broadleaf Species in Mixed Forests in Northern Japan Using UAV Multi-Spectral Imagery and Mask R-CNN Model." Remote Sensing 17, no. 13 (2025): 2111. https://doi.org/10.3390/rs17132111.

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Precise spatial localization of broadleaf species is crucial for efficient forest management and ecological studies. This study presents an advanced approach for segmenting and classifying broadleaf tree species, including Japanese oak (Quercus crispula), in mixed forests using multi-spectral imagery captured by unmanned aerial vehicles (UAVs) and deep learning. High-resolution UAV images, including RGB and NIR bands, were collected from two study sites in Hokkaido, Japan: Sub-compartment 97g in the eastern region and Sub-compartment 68E in the central region. A Mask Region-based Convolutional

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Wu, Jingru, Qixia Man, Xinming Yang, et al. "Fine Classification of Urban Tree Species Based on UAV-Based RGB Imagery and LiDAR Data." Forests 15, no. 2 (2024): 390. http://dx.doi.org/10.3390/f15020390.

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Rapid and accurate classification of urban tree species is crucial for the protection and management of urban ecology. However, tree species classification remains a great challenge because of the high spatial heterogeneity and biodiversity. Addressing this challenge, in this study, unmanned aerial vehicle (UAV)-based high-resolution RGB imagery and LiDAR data were utilized to extract seven types of features, including RGB spectral features, texture features, vegetation indexes, HSV spectral features, HSV texture features, height feature, and intensity feature. Seven experiments involving diff

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García-Fernández, Marta, Enoc Sanz-Ablanedo, and José Ramón Rodríguez-Pérez. "High-Resolution Drone-Acquired RGB Imagery to Estimate Spatial Grape Quality Variability." Agronomy 11, no. 4 (2021): 655. http://dx.doi.org/10.3390/agronomy11040655.

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Remotesensing techniques can help reduce time and resources spent collecting samples of crops and analyzing quality variables. The main objective of this work was to demonstrate that it is possible to obtain information on the distribution of must quality variables from conventional photographs. Georeferenced berry samples were collected and analyzed in the laboratory, and RGB images were taken using a low-cost drone from which an orthoimage was made. Transformation equations were calculated to obtain absolute reflectances for the different bands and to calculate 10 vegetation indices plus two

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19

Niu, Yaxiao, Liyuan Zhang, Huihui Zhang, Wenting Han, and Xingshuo Peng. "Estimating Above-Ground Biomass of Maize Using Features Derived from UAV-Based RGB Imagery." Remote Sensing 11, no. 11 (2019): 1261. http://dx.doi.org/10.3390/rs11111261.

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The rapid, accurate, and economical estimation of crop above-ground biomass at the farm scale is crucial for precision agricultural management. The unmanned aerial vehicle (UAV) remote-sensing system has a great application potential with the ability to obtain remote-sensing imagery with high temporal-spatial resolution. To verify the application potential of consumer-grade UAV RGB imagery in estimating maize above-ground biomass, vegetation indices and plant height derived from UAV RGB imagery were adopted. To obtain a more accurate observation, plant height was directly derived from UAV RGB

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Koukiou, Georgia. "Perceptually Optimal Color Representation of Fully Polarimetric SAR Imagery." Journal of Imaging 8, no. 3 (2022): 67. http://dx.doi.org/10.3390/jimaging8030067.

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The four bands of fully polarimetric SAR data convey scattering characteristics of the Earth’s background, but perceptually are not very easy for an observer to use. In this work, the four different channels of fully polarimetric SAR images, namely HH, HV, VH, and VV, are combined so that a color image of the Earth’s background is derived that is perceptually excellent for the human eye and at the same time provides accurate information regarding the scattering mechanisms in each pixel. Most of the elementary scattering mechanisms are related to specific color and land cover types. The innovat

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Eltner, A., D. Mader, N. Szopos, B. Nagy, J. Grundmann, and L. Bertalan. "USING THERMAL AND RGB UAV IMAGERY TO MEASURE SURFACE FLOW VELOCITIES OF RIVERS." International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLIII-B2-2021 (June 28, 2021): 717–22. http://dx.doi.org/10.5194/isprs-archives-xliii-b2-2021-717-2021.

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Abstract. This study assesses the suitability to use RGB and thermal infrared imagery acquired from an UAV to measure surface flow velocities of rivers. The reach of a medium-scale river in Hungary is investigated. Image sequences with a frame rate of 2 Hz were captured with two sensors, a RGB and an uncooled thermal camera, at a flying height that ensures the visibility of both shores. The interior geometry of both cameras were calibrated with an in-house designed target field. The image sequences were automatically co-registered to account for UAV movements during the image acquisition. The

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Fernandez-Gallego, Jose, Ma Buchaillot, Nieves Aparicio Gutiérrez, María Nieto-Taladriz, José Araus, and Shawn Kefauver. "Automatic Wheat Ear Counting Using Thermal Imagery." Remote Sensing 11, no. 7 (2019): 751. http://dx.doi.org/10.3390/rs11070751.

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Ear density is one of the most important agronomical yield components in wheat. Ear counting is time-consuming and tedious as it is most often conducted manually in field conditions. Moreover, different sampling techniques are often used resulting in a lack of standard protocol, which may eventually affect inter-comparability of results. Thermal sensors capture crop canopy features with more contrast than RGB sensors for image segmentation and classification tasks. An automatic thermal ear counting system is proposed to count the number of ears using zenithal/nadir thermal images acquired from

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Doornbos, Jurrian, Önder Babur, and João Valente. "Evaluating Generalization of Methods for Artificially Generating NDVI from UAV RGB Imagery in Vineyards." Remote Sensing 17, no. 3 (2025): 512. https://doi.org/10.3390/rs17030512.

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High-resolution NDVI maps derived from UAV imagery are valuable in precision agriculture, supporting vineyard management decisions such as disease risk and vigor assessments. However, the expense and complexity of multispectral sensors limit their widespread use. In this study, we evaluate Generative Adversarial Network (GAN) approaches—trained on either multispectral-derived or true RGB data—to convert low-cost RGB imagery into NDVI maps. We benchmark these models against simpler, explainable RGB-based indices (RGBVI, vNDVI) using Botrytis bunch rot (BBR) risk and vigor mapping as application

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Tubau Comas, A., J. Valente, and L. Kooistra. "AUTOMATIC APPLE TREE BLOSSOM ESTIMATION FROM UAV RGB IMAGERY." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-2/W13 (June 4, 2019): 631–35. http://dx.doi.org/10.5194/isprs-archives-xlii-2-w13-631-2019.

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<p><strong>Abstract.</strong> Apple trees often produce high amount of fruits, which results in small, low quality fruits. Thinning in apple orchards is used to improve the quality of the apples by reducing the number of flowers or fruits the tree is producing. The current method used to estimate how much thinning is necessary is to measure flowering intensity, currently done by human visual inspection of trees in the orchard. The use of images of apple trees from ground-level to measure flowering intensity and its spatial variation through orchards has been researched with p

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Chen, Jianqu, Xunmeng Li, Kai Wang, Shouyu Zhang, Jun Li, and Mingbo Sun. "Assessment of intertidal seaweed biomass based on RGB imagery." PLOS ONE 17, no. 2 (2022): e0263416. http://dx.doi.org/10.1371/journal.pone.0263416.

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The Above Ground Biomass (AGB) of seaweeds is the most fundamental ecological parameter as the material and energy basis of intertidal ecosystems. Therefore, there is a need to develop an efficient survey method that has less impact on the environment. With the advent of technology and the availability of popular filming devices such as smartphones and cameras, intertidal seaweed wet biomass can be surveyed by remote sensing using popular RGB imaging sensors. In this paper, 143 in situ sites of seaweed in the intertidal zone of GouQi Island, ShengSi County, Zhejiang Province, were sampled and

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Pádua, Luís, Pedro Marques, Jonáš Hruška, et al. "Multi-Temporal Vineyard Monitoring through UAV-Based RGB Imagery." Remote Sensing 10, no. 12 (2018): 1907. http://dx.doi.org/10.3390/rs10121907.

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This study aimed to characterize vineyard vegetation thorough multi-temporal monitoring using a commercial low-cost rotary-wing unmanned aerial vehicle (UAV) equipped with a consumer-grade red/green/blue (RGB) sensor. Ground-truth data and UAV-based imagery were acquired on nine distinct dates, covering the most significant vegetative growing cycle until harvesting season, over two selected vineyard plots. The acquired UAV-based imagery underwent photogrammetric processing resulting, per flight, in an orthophoto mosaic, used for vegetation estimation. Digital elevation models were used to comp

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Yassine, H., K. Tout, and M. Jaber. "IMPROVING LULC CLASSIFICATION FROM SATELLITE IMAGERY USING DEEP LEARNING – EUROSAT DATASET." International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLIII-B3-2021 (June 28, 2021): 369–76. http://dx.doi.org/10.5194/isprs-archives-xliii-b3-2021-369-2021.

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Abstract. Machine learning (ML) has proven useful for a very large number of applications in several domains. It has realized a remarkable growth in remote-sensing image analysis over the past few years. Deep Learning (DL) a subset of machine learning were applied in this work to achieve a better classification of Land Use Land Cover (LULC) in satellite imagery using Convolutional Neural Networks (CNNs). EuroSAT benchmarking data set is used as training data set which uses Sentinel-2 satellite images. Sentinel-2 provides images with 13 spectral feature bands, but surprisingly little attention

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Iskandar, Beni, I. Nengah Surati Jaya, and Muhammad Buce Saleh. "Crown closure segmentation on wetland lowland forest using the mean shift algorithm." Indonesian Journal of Electrical Engineering and Computer Science 24, no. 2 (2021): 965. http://dx.doi.org/10.11591/ijeecs.v24.i2.pp965-977.

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The availability of high and very high-resolution imagery is helpful for forest inventory, particularly to measure the stand variables such as canopy dimensions, canopy density, and crown closure. This paper describes the examination of mean shift (MS) algorithm on wetland lowland forest. The study objective was to find the optimal parameters for crown closure segmentation Pleiades-1B and SPOT-6 imageries. The study shows that the segmentation of crown closure with the red band of Pleiades-1B image would be well segmented by using the parameter combination of (hs: 6, hr: 5, M: 33) having overa

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Iskandar, Beni, I. Nengah Surati Jaya, and Muhammad Buce Saleh. "Crown closure segmentation on wetland lowland forest using the mean shift algorithm." Indonesian Journal of Electrical Engineering and Computer Science 24, no. 2 (2021): 965–77. https://doi.org/10.11591/ijeecs.v24.i2.pp965-977.

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The availability of high and very high-resolution imagery is helpful for forest inventory, particularly to measure the stand variables such as canopy dimensions, canopy density, and crown closure. This paper describes the examination of mean shift (MS) algorithm on wetland lowland forest. The study objective was to find the optimal parameters for crown closure segmentation Pleiades-1B and SPOT-6 imageries. The study shows that the segmentation of crown closure with the red band of Pleiades-1B image would be well segmented by using the parameter combination of (hs: 6, hr: 5, M: 33) having overa

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Rau, J. Y., J. P. Jhan, and C. Y. Huang. "ORTHO-RECTIFICATION OF NARROW BAND MULTI-SPECTRAL IMAGERY ASSISTED BY DSLR RGB IMAGERY ACQUIRED BY A FIXED-WING UAS." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XL-1/W4 (August 26, 2015): 67–74. http://dx.doi.org/10.5194/isprsarchives-xl-1-w4-67-2015.

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Miniature Multiple Camera Array (MiniMCA-12) is a frame-based multilens/multispectral sensor composed of 12 lenses with narrow band filters. Due to its small size and light weight, it is suitable to mount on an Unmanned Aerial System (UAS) for acquiring high spectral, spatial and temporal resolution imagery used in various remote sensing applications. However, due to its wavelength range is only 10 nm that results in low image resolution and signal-to-noise ratio which are not suitable for image matching and digital surface model (DSM) generation. In the meantime, the spectral correlation amon

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Shi, Weibo, Xiaohan Liao, Jia Sun, et al. "Optimizing Observation Plans for Identifying Faxon Fir (Abies fargesii var. Faxoniana) Using Monthly Unmanned Aerial Vehicle Imagery." Remote Sensing 15, no. 8 (2023): 2205. http://dx.doi.org/10.3390/rs15082205.

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Faxon fir (Abies fargesii var. faxoniana), as a dominant tree species in the subalpine coniferous forest of Southwest China, has strict requirements regarding the temperature and humidity of the growing environment. Therefore, the dynamic and continuous monitoring of Faxon fir distribution is very important to protect this highly sensitive ecological environment. Here, we combined unmanned aerial vehicle (UAV) imagery and convolutional neural networks (CNNs) to identify Faxon fir and explored the identification capabilities of multispectral (five bands) and red-green-blue (RGB) imagery under d

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Fu, Yuanyuan, Guijun Yang, Zhenhai Li, et al. "Winter Wheat Nitrogen Status Estimation Using UAV-Based RGB Imagery and Gaussian Processes Regression." Remote Sensing 12, no. 22 (2020): 3778. http://dx.doi.org/10.3390/rs12223778.

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Predicting the crop nitrogen (N) nutrition status is critical for optimizing nitrogen fertilizer application. The present study examined the ability of multiple image features derived from unmanned aerial vehicle (UAV) RGB images for winter wheat N status estimation across multiple critical growth stages. The image features consisted of RGB-based vegetation indices (VIs), color parameters, and textures, which represented image features of different aspects and different types. To determine which N status indicators could be well-estimated, we considered two mass-based N status indicators (i.e.

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Feng, Haikuan, Huilin Tao, Zhenhai Li, Guijun Yang, and Chunjiang Zhao. "Comparison of UAV RGB Imagery and Hyperspectral Remote-Sensing Data for Monitoring Winter Wheat Growth." Remote Sensing 14, no. 15 (2022): 3811. http://dx.doi.org/10.3390/rs14153811.

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Although crop-growth monitoring is important for agricultural managers, it has always been a difficult research topic. However, unmanned aerial vehicles (UAVs) equipped with RGB and hyperspectral cameras can now acquire high-resolution remote-sensing images, which facilitates and accelerates such monitoring. To explore the effect of monitoring a single crop-growth indicator and multiple indicators, this study combines six growth indicators (plant nitrogen content, above-ground biomass, plant water content, chlorophyll, leaf area index, and plant height) into the new comprehensive growth index

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Wang, Hongquan, Keshav D. Singh, Hari P. Poudel, Manoj Natarajan, Prabahar Ravichandran, and Brandon Eisenreich. "Forage Height and Above-Ground Biomass Estimation by Comparing UAV-Based Multispectral and RGB Imagery." Sensors 24, no. 17 (2024): 5794. http://dx.doi.org/10.3390/s24175794.

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Crop height and biomass are the two important phenotyping traits to screen forage population types at local and regional scales. This study aims to compare the performances of multispectral and RGB sensors onboard drones for quantitative retrievals of forage crop height and biomass at very high resolution. We acquired the unmanned aerial vehicle (UAV) multispectral images (MSIs) at 1.67 cm spatial resolution and visible data (RGB) at 0.31 cm resolution and measured the forage height and above-ground biomass over the alfalfa (Medicago sativa L.) breeding trials in the Canadian Prairies. (1) For

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López-García, Patricia, Diego Intrigliolo, Miguel A. Moreno, et al. "Machine Learning-Based Processing of Multispectral and RGB UAV Imagery for the Multitemporal Monitoring of Vineyard Water Status." Agronomy 12, no. 9 (2022): 2122. http://dx.doi.org/10.3390/agronomy12092122.

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The development of unmanned aerial vehicles (UAVs) and light sensors has required new approaches for high-resolution remote sensing applications. High spatial and temporal resolution spectral data acquired by multispectral and conventional cameras (or red, green, blue (RGB) sensors) onboard UAVs can be useful for plant water status determination and, as a consequence, for irrigation management. A study in a vineyard located in south-eastern Spain was carried out during the 2018, 2019, and 2020 seasons to assess the potential uses of these techniques. Different water qualities and irrigation ap

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Wu, Qiang, Yongping Zhang, Min Xie, et al. "Estimation of Fv/Fm in Spring Wheat Using UAV-Based Multispectral and RGB Imagery with Multiple Machine Learning Methods." Agronomy 13, no. 4 (2023): 1003. http://dx.doi.org/10.3390/agronomy13041003.

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The maximum quantum efficiency of photosystem II (Fv/Fm) is a widely used indicator of photosynthetic health in plants. Remote sensing of Fv/Fm using MS (multispectral) and RGB imagery has the potential to enable high-throughput screening of plant health in agricultural and ecological applications. This study aimed to estimate Fv/Fm in spring wheat at an experimental base in Hanghou County, Inner Mongolia, from 2020 to 2021. RGB and MS images were obtained at the wheat flowering stage using a Da-Jiang Phantom 4 multispectral drone. A total of 51 vegetation indices were constructed, and the mea

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Lussem, U., A. Bolten, M. L. Gnyp, J. Jasper, and G. Bareth. "EVALUATION OF RGB-BASED VEGETATION INDICES FROM UAV IMAGERY TO ESTIMATE FORAGE YIELD IN GRASSLAND." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-3 (April 30, 2018): 1215–19. http://dx.doi.org/10.5194/isprs-archives-xlii-3-1215-2018.

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Monitoring forage yield throughout the growing season is of key importance to support management decisions on grasslands/pastures. Especially on intensely managed grasslands, where nitrogen fertilizer and/or manure are applied regularly, precision agriculture applications are beneficial to support sustainable, site-specific management decisions on fertilizer treatment, grazing management and yield forecasting to mitigate potential negative impacts. To support these management decisions, timely and accurate information is needed on plant parameters (e.g. forage yield) with a high spatial and te

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Dabrowski, R., A. Orych, A. Jenerowicz, and P. Walczykowski. "PRELIMINARY RESULTS FROM THE PORTABLE IMAGERY QUALITY ASSESSMENT TEST FIELD (PIQuAT) OF UAV IMAGERY FOR IMAGERY RECONNAISSANCE PURPOSES." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XL-1/W4 (August 26, 2015): 111–15. http://dx.doi.org/10.5194/isprsarchives-xl-1-w4-111-2015.

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The article presents a set of initial results of a quality assessment study of 2 different types of sensors mounted on an unmanned aerial vehicle, carried out over an especially designed and constructed test field. The PIQuAT (Portable Imagery Quality Assessment Test Field) field had been designed especially for the purposes of determining the quality parameters of UAV sensors, especially in terms of the spatial, spectral and radiometric resolutions and chosen geometric aspects. The sensor used include a multispectral framing camera and a high-resolution RGB sensor. The flights were conducted

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Rajwade, Yogesh, Narendra Chandel, Abhilash Chandel, et al. "Thermal–RGB Imagery and Computer Vision for Water Stress Identification of Okra (Abelmoschus esculentus L.)." Applied Sciences 14, no. 13 (2024): 5623. http://dx.doi.org/10.3390/app14135623.

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Crop canopy temperature has proven beneficial for qualitative and quantitative assessment of plants’ biotic and abiotic stresses. In this two-year study, water stress identification in okra crops was evaluated using thermal–RGB imaging and AI approaches. Experimental trials were developed for two irrigation types, sprinkler and flood, and four deficit treatment levels (100, 50, 75, and 25% crop evapotranspiration), replicated thrice. A total of 3200 thermal and RGB images acquired from different crop stages were processed using convolutional neural network architecture-based deep learning mode

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Jollet, D., U. Rascher, and M. Müller-Linow. "Assessing yield quality parameters in bush bean via RGB imagery." Acta Horticulturae, no. 1327 (November 2021): 421–28. http://dx.doi.org/10.17660/actahortic.2021.1327.56.

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Sebastian, C., B. Boom, T. van Lankveld, E. Bondarev, and P. H. N. De With. "BOOTSTRAPPED CNNS FOR BUILDING SEGMENTATION ON RGB-D AERIAL IMAGERY." ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences IV-4 (September 19, 2018): 187–92. http://dx.doi.org/10.5194/isprs-annals-iv-4-187-2018.

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<p><strong>Abstract.</strong> Detection of buildings and other objects from aerial images has various applications in urban planning and map making. Automated building detection from aerial imagery is a challenging task, as it is prone to varying lighting conditions, shadows and occlusions. Convolutional Neural Networks (CNNs) are robust against some of these variations, although they fail to distinguish easy and difficult examples. We train a detection algorithm from RGB-D images to obtain a segmented mask by using the CNN architecture DenseNet. First, we improve the perform

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Rodriguez-Gallo, Yakdiel, Byron Escobar-Benitez, and Jony Rodriguez-Lainez. "Robust Coffee Rust Detection Using UAV-Based Aerial RGB Imagery." AgriEngineering 5, no. 3 (2023): 1415–31. http://dx.doi.org/10.3390/agriengineering5030088.

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Timely detection of pests and diseases in crops is essential to mitigate severe damage and economic losses, especially in the context of climate change. This paper describes a method for detecting the presence of coffee leaf rust (CLR) using two databases: RoCoLe and a database obtained from an unmanned aerial vehicle (UAV) equipped with an RGB camera. The developed method follows a two-stage approach. In the first stage, images are processed using ImageJ software, while, in the second phase, Python is used to implement morphological filters and the Hough transform for rust identification. The

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Psiroukis, Vasilis, George Papadopoulos, Aikaterini Kasimati, Nikos Tsoulias, and Spyros Fountas. "Cotton Growth Modelling Using UAS-Derived DSM and RGB Imagery." Remote Sensing 15, no. 5 (2023): 1214. http://dx.doi.org/10.3390/rs15051214.

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Modeling cotton plant growth is an important aspect of improving cotton yields and fiber quality and optimizing land management strategies. High-throughput phenotyping (HTP) systems, including those using high-resolution imagery from unmanned aerial systems (UAS) combined with sensor technologies, can accurately measure and characterize phenotypic traits such as plant height, canopy cover, and vegetation indices. However, manual assessment of plant characteristics is still widely used in practice. It is time-consuming, labor-intensive, and prone to human error. In this study, we investigated t

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Agapiou, Athos. "Vegetation Extraction Using Visible-Bands from Openly Licensed Unmanned Aerial Vehicle Imagery." Drones 4, no. 2 (2020): 27. http://dx.doi.org/10.3390/drones4020027.

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Red–green–blue (RGB) cameras which are attached in commercial unmanned aerial vehicles (UAVs) can support remote-observation small-scale campaigns, by mapping, within a few centimeter’s accuracy, an area of interest. Vegetated areas need to be identified either for masking purposes (e.g., to exclude vegetated areas for the production of a digital elevation model (DEM) or for monitoring vegetation anomalies, especially for precision agriculture applications. However, while detection of vegetated areas is of great importance for several UAV remote sensing applications, this type of processing ca

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Zhang, Heng, Can Yang, and Xijian Fan. "MTCDNet: Multimodal Feature Fusion-Based Tree Crown Detection Network Using UAV-Acquired Optical Imagery and LiDAR Data." Remote Sensing 17, no. 12 (2025): 1996. https://doi.org/10.3390/rs17121996.

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Accurate detection of individual tree crowns is a critical prerequisite for precisely extracting forest structural parameters, which is vital for forestry resources monitoring. While unmanned aerial vehicle (UAV)-acquired RGB imagery, combined with deep learning-based networks, has demonstrated considerable potential, existing methods often rely exclusively on RGB data, rendering them susceptible to shadows caused by varying illumination and suboptimal performance in dense forest stands. In this paper, we propose integrating LiDAR-derived Canopy Height Model (CHM) with RGB imagery as complemen

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Surový, P., N. A Ribeiro, A. C Oliveira, and Ľ. Scheer. "Discrimination of vegetation from the background in high resolution colour remote sensed imagery." Journal of Forest Science 50, No. 4 (2012): 161–70. http://dx.doi.org/10.17221/4611-jfs.

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Different transformations of RGB colour space were compared to develop the best method for discrimination of vegetation from the background in open pure cork oak stands in southern Portugal in high-resolution colour imagery. Normalised difference index, i1i2i3 colour space and other indices developed for classic band imagery were recalculated for near infrared imagery and tested. A new method for fully automated thresholding was developed and tested. The newly developed index shows the equal accuracy performance but provides the smallest overestimation error and retains the largest scale of gr

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Wang, Jiali, Ming Chen, Weidong Zhu, Liting Hu, and Yasong Wang. "A Combined Approach for Retrieving Bathymetry from Aerial Stereo RGB Imagery." Remote Sensing 14, no. 3 (2022): 760. http://dx.doi.org/10.3390/rs14030760.

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Shallow water bathymetry is critical in understanding and managing marine ecosystems. Bathymetric inversion models using airborne/satellite multispectral data are an efficient way to retrieve shallow bathymetry due to the affordable cost of airborne/satellite images and less field work required. With the increasing availability and popularity of unmanned aerial vehicle (UAV) imagery, this paper explores a new approach to obtain bathymetry using UAV visual-band (RGB) images. A combined approach is therefore proposed for retrieving bathymetry from aerial stereo RGB imagery, which is the combinat

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Chandel, Narendra S., Yogesh A. Rajwade, Kumkum Dubey, Abhilash K. Chandel, A. Subeesh, and Mukesh K. Tiwari. "Water Stress Identification of Winter Wheat Crop with State-of-the-Art AI Techniques and High-Resolution Thermal-RGB Imagery." Plants 11, no. 23 (2022): 3344. http://dx.doi.org/10.3390/plants11233344.

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Timely crop water stress detection can help precision irrigation management and minimize yield loss. A two-year study was conducted on non-invasive winter wheat water stress monitoring using state-of-the-art computer vision and thermal-RGB imagery inputs. Field treatment plots were irrigated using two irrigation systems (flood and sprinkler) at four rates (100, 75, 50, and 25% of crop evapotranspiration [ETc]). A total of 3200 images under different treatments were captured at critical growth stages, that is, 20, 35, 70, 95, and 108 days after sowing using a custom-developed thermal-RGB imagin

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Martínez Movilla, Andrea, Juan Luis Rodríguez Somoza, and Joaquín Martínez Sánchez. "Machine learning classification of intertidal macroalgae using UAV imagery and topographical indexes." International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLVIII-4/W11-2024 (June 27, 2024): 73–80. http://dx.doi.org/10.5194/isprs-archives-xlviii-4-w11-2024-73-2024.

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Abstract. Intertidal macroalgae play a vital role in marine ecosystems, necessitating effective monitoring of their coverage and diversity. Traditional monitoring methods are labour-intensive and costly, prompting exploration of the use of unmanned aerial vehicles (UAVs) to characterize intertidal ecosystems. We propose an alternative process integrating UAV red-green-blue (RGB) imagery and topographic indexes to classify complex intertidal macroalgae assemblages automatically. We studied two intertidal areas capturing eight flights between May and September 2023. Orthoimages and Digital Eleva

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Zhang, Jiuyuan, Jingshan Lu, Qiuyan Zhang, et al. "Estimation of Garden Chrysanthemum Crown Diameter Using Unmanned Aerial Vehicle (UAV)-Based RGB Imagery." Agronomy 14, no. 2 (2024): 337. http://dx.doi.org/10.3390/agronomy14020337.

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Crown diameter is one of the crucial indicators for evaluating the adaptability, growth quality, and ornamental value of garden chrysanthemums. To accurately obtain crown diameter, this study employed an unmanned aerial vehicle (UAV) equipped with a RGB camera to capture orthorectified canopy images of 64 varieties of garden chrysanthemums at different growth stages. Three methods, namely RGB color space, hue-saturation-value (HSV) color space, and the mask region-based convolutional neural network (Mask R-CNN), were employed to estimate the crown diameter of garden chrysanthemums. The results

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