Method of Vegetation Detection Using RGB Images Made by Unmanned Aerial Vehicles on the Basis of Colour and Texture Analysis

2019 ◽  
pp. 55-62
Author(s):  
Michael Y. Kataev ◽  
Maria M. Dadonova

The article describes the capability of application of RGB?1 images made by digital cameras for detection of Earth surface (vegetation) types. A set of measures necessary to be taken for processing of the images made by unmanned aerial vehicles (UAV) in real-time is described. Application of analogue of vegetation index allows detecting vegetation on an RGB image, which increases the probability of correct detection of surface (vegetation) type. Methods of preliminary and thematic processing of images required for positive detection of surface types are considered. Texture analysis is applied for detection of vegetation type. The results of the processing of real images are provided.

2020 ◽  
Vol 2 (2) ◽  
pp. 206-212 ◽  
Author(s):  
Luis Fernando Sánchez-Sastre ◽  
Mª Auxiliadora Casterad ◽  
Mónica Guillén ◽  
Norlan Miguel Ruiz-Potosme ◽  
Nuno M. S. Alte da Veiga ◽  
...  

Unmanned Aerial Vehicles (UAVs) offer excellent survey capabilities at low cost to provide farmers with information about the type and distribution of weeds in their fields. In this study, the problem of detecting the infestation of a typical weed (charlock mustard) in an alfalfa crop has been addressed using conventional digital cameras installed on a lightweight UAV to compare RGB-based indices with the widely used Normalized Difference Vegetation Index (NDVI) index. The simple (R−B)/(R+B) and (R−B)/(R+B+G) vegetation indices allowed one to easily discern the yellow weed from the green crop. Moreover, they avoided the potential confusion of weeds with soil observed for the NDVI index. The small overestimation detected in the weed identification when the RGB indices were used could be easily reduced by using them in conjunction with NDVI. The proposed methodology may be used in the generation of weed cover maps for alfalfa, which may then be translated into site-specific herbicide treatment maps.


Author(s):  
Михаил Катаев ◽  
Mikhail Kataev ◽  
Мария Дадонова ◽  
Maria Dadonova

To date, flying unmanned aerial vehicles (UAVs), with digital cameras on board, have become commonplace. The resulting images are collected in orthophotomaps and more used for visual analysis. A numerical analysis of the content of images is still poorly developed. One of the areas of analysis is the allocation of vegetation in the image and the determination of types. There are many ways to highlight plants in an image, such as texture, color, or index analysis. In this paper, we set the task of processing the image obtained from the UAV, isolating the vegetation in the image, selecting the desired plants from the set, and estimating the area occupied by this plant in the image based on texture analysis.


2020 ◽  
Vol 12 (24) ◽  
pp. 4144
Author(s):  
José Luis Gallardo-Salazar ◽  
Marín Pompa-García

Modern forestry poses new challenges that space technologies can solve thanks to the advent of unmanned aerial vehicles (UAVs). This study proposes a methodology to extract tree-level characteristics using UAVs in a spatially distributed area of pine trees on a regular basis. Analysis included different vegetation indices estimated with a high-resolution orthomosaic. Statistically reliable results were found through a three-phase workflow consisting of image acquisition, canopy analysis, and validation with field measurements. Of the 117 trees in the field, 112 (95%) were detected by the algorithm, while height, area, and crown diameter were underestimated by 1.78 m, 7.58 m2, and 1.21 m, respectively. Individual tree attributes obtained from the UAV, such as total height (H) and the crown diameter (CD), made it possible to generate good allometric equations to infer the basal diameter (BD) and diameter at breast height (DBH), with R2 of 0.76 and 0.79, respectively. Multispectral indices were useful as tree vigor parameters, although the normalized-difference vegetation index (NDVI) was highlighted as the best proxy to monitor the phytosanitary condition of the orchard. Spatial variation in individual tree productivity suggests the differential management of ramets. The consistency of the results allows for its application in the field, including the complementation of spectral information that can be generated; the increase in accuracy and efficiency poses a path to modern inventories. However, the limitation for its application in forests of more complex structures is identified; therefore, further research is recommended.


2019 ◽  
Vol 11 (22) ◽  
pp. 2667 ◽  
Author(s):  
Jiang ◽  
Cai ◽  
Zheng ◽  
Cheng ◽  
Tian ◽  
...  

Commercially available digital cameras can be mounted on an unmanned aerial vehicle (UAV) for crop growth monitoring in open-air fields as a low-cost, highly effective observation system. However, few studies have investigated their potential for nitrogen (N) status monitoring, and the performance of camera-derived vegetation indices (VIs) under different conditions remains poorly understood. In this study, five commonly used VIs derived from normal color (RGB) images and two typical VIs derived from color near-infrared (CIR) images were used to estimate leaf N concentration (LNC). To explore the potential of digital cameras for monitoring LNC at all crop growth stages, two new VIs were proposed, namely, the true color vegetation index (TCVI) from RGB images and the false color vegetation index (FCVI) from CIR images. The relationships between LNC and the different VIs varied at different stages. The commonly used VIs performed well at some stages, but the newly proposed TCVI and FCVI had the best performance at all stages. The performances of the VIs with red (or near-infrared) and green bands as the numerator were limited by saturation at intermediate to high LNCs (LNC > 3.0%), but the TCVI and FCVI had the ability to mitigate the saturation. The results of model validations further supported the superiority of the TCVI and FCVI for LNC estimation. Compared to the other VIs derived using RGB cameras, the relative root mean square errors (RRMSEs) of the TCVI were improved by 8.6% on average. For the CIR images, the best-performing VI for LNC was the FCVI (R2 = 0.756, RRMSE = 14.18%). The LNC–TCVI and LNC–FCVI were stable under different cultivars, N application rates, and planting densities. The results confirmed the applicability of UAV-based RGB and CIR cameras for crop N status monitoring under different conditions, which should assist the precision management of N fertilizers in agronomic practices.


2021 ◽  
Vol 918 (1) ◽  
pp. 012011
Author(s):  
H S Aprilianti ◽  
R A Ari ◽  
A Ranti ◽  
M F Aslam

Abstract Understanding the threshold value classification from various vegetation types may help distinguish spectral reflectance differences in detailed land use studies. However, conducting all of the processes requires relatively large resources regarding manual computation, which could be surpassed by cloud computing. Unfortunately, in Bogor Regency, there is still a lack of research that studies the threshold value of various vegetation types related to forestry and plantation sectors. Land use categories were classified, and threshold values were determined, especially for selected vegetation types including teak, oil palm, rubber, pine, bamboo, and tea based on several vegetation indices in Bogor Regency using the Cloud-Computing platform. The data source was retrieved from 10-meters Sentinel-2 Satellite median imagery of January 2019 - June 2021. Land use maps were generated using Random Forest Algorithm from composite images. Meanwhile, the threshold value of each vegetation type was calculated from the average and standard deviation of NDVI, SAVI, EVI, ARVI, SLAVI, and GNDVI index. The result of the study showed forest and plantation area covers about 158,168.13 ha or 48.92 % of the study area. NDVI was found suitable to identify teak, SLAVI for rubber and pine, EVI for bamboo and tea, and GNDVI for oil palm vegetation.


2019 ◽  
Vol 8 ◽  
pp. 279-285
Author(s):  
Stanislav Arbuzov ◽  
Evgenij Gritskevich ◽  
Darja Michaylova ◽  
Anna Selezneva

Monitoring of the environment with the help of unmanned aerial vehicles is currently one of the most developing branches of optoelectronic instrument-making. Digital cameras installed on these devices make it possible to survey the underlying surface in order to select the its features. The use of unmanned aerial vehicles for the control of agricultural lands is a very perspective case of such monitoring. The technique of measuring the spectral reflection coefficients of surfaces is developed for identification of the vegetation state observed in the field of view of multispectral digital camera. The method allows determining the spectral reflectance of the calibration surfaces using the reference ones and after that to find the parameters of working surfaces using the calibration ones. The obtained results are applied under the analysis and processing of images obtained in the course of the unmanned aviation system that monitors agricultural lands.


Author(s):  
L. M. González-de Santos ◽  
J. Martínez-Sánchez ◽  
H. González-Jorge ◽  
A. Novo ◽  
P. Arias

<p><strong>Abstract.</strong> Many inspection tasks of structures are already carried out by unmanned aerial vehicles (UAV). Most of these inspections consist of using payloads for close range remote sensing purposes (i.e. digital cameras, thermal or LiDAR sensors). In all these inspection tasks the UAV system does not need to be close to the structure and typically the GPS coverage is good to perform mission navigation. In this paper, a smart payload developed for navigation in the neighbourhood of structures is presented. With this payload the UAV system is able to control the distance to a structure and the angle formed by the UAV and the structure in the horizontal plane. This payload has been calibrated in order to determine the calibration curve and measure the accuracy of the payload. The system has been tested in an indoor environment (GPS-denied). Good position and angular results has been obtained.</p>


2008 ◽  
Vol 47 (2) ◽  
pp. 411-424 ◽  
Author(s):  
Young-Kwon Lim ◽  
Ming Cai ◽  
Eugenia Kalnay ◽  
Liming Zhou

Abstract The impact of different surface vegetations on long-term surface temperature change is estimated by subtracting reanalysis trends in monthly surface temperature anomalies from observation trends over the last four decades. This is done using two reanalyses, namely, the 40-yr ECMWF (ERA-40) and NCEP–NCAR I (NNR), and two observation datasets, namely, Climatic Research Unit (CRU) and Global Historical Climate Network (GHCN). The basis of the observation minus reanalysis (OMR) approach is that the NNR reanalysis surface fields, and to a lesser extent the ERA-40, are insensitive to surface processes associated with different vegetation types and their changes because the NNR does not use surface observations over land, whereas ERA-40 only uses surface temperature observations indirectly, in order to initialize soil temperature and moisture. As a result, the OMR trends can provide an estimate of surface effects on the observed temperature trends missing in the reanalyses. The OMR trends obtained from observation minus NNR show a strong and coherent sensitivity to the independently estimated surface vegetation from normalized difference vegetation index (NDVI). The correlation between the OMR trend and the NDVI indicates that the OMR trend decreases with surface vegetation, with a correlation &lt; −0.5, indicating that there is a stronger surface response to global warming in arid regions, whereas the OMR response is reduced in highly vegetated areas. The OMR trend averaged over the desert areas (0 &lt; NDVI &lt; 0.1) shows a much larger increase of temperature (∼0.4°C decade−1) than over tropical forest areas (NDVI &gt; 0.4) where the OMR trend is nearly zero. Areas of intermediate vegetation (0.1 &lt; NDVI &lt; 0.4), which are mostly found over midlatitudes, reveal moderate OMR trends (approximately 0.1°–0.3°C decade−1). The OMR trends are also very sensitive to the seasonal vegetation change. While the OMR trends have little seasonal dependence over deserts and tropical forests, whose vegetation state remains rather constant throughout the year, the OMR trends over the midlatitudes, in particular Europe and North America, exhibit strong seasonal variation in response to the NDVI fluctuations associated with deciduous vegetation. The OMR trend rises up approximately to 0.2°–0.3°C decade−1 in winter and early spring when the vegetation cover is low, and is only 0.1°C decade−1 in summer and early autumn with high vegetation. However, the Asian inlands (Russia, northern China with Tibet, and Mongolia) do not show this strong OMR variation despite their midlatitude location, because of the relatively permanent aridity of these regions.


2020 ◽  
Vol 12 (17) ◽  
pp. 2863 ◽  
Author(s):  
L. Minh Dang ◽  
Hanxiang Wang ◽  
Yanfen Li ◽  
Kyungbok Min ◽  
Jin Tae Kwak ◽  
...  

The radish is a delicious, healthy vegetable and an important ingredient to many side dishes and main recipes. However, climate change, pollinator decline, and especially Fusarium wilt cause a significant reduction in the cultivation area and the quality of the radish yield. Previous studies on plant disease identification have relied heavily on extracting features manually from images, which is time-consuming and inefficient. In addition to Red-Green-Blue (RGB) images, the development of near-infrared (NIR) sensors has enabled a more effective way to monitor the diseases and evaluate plant health based on multispectral imagery. Thus, this study compares two distinct approaches in detecting radish wilt using RGB images and NIR images taken by unmanned aerial vehicles (UAV). The main research contributions include (1) a high-resolution RGB and NIR radish field dataset captured by drone from low to high altitudes, which can serve several research purposes; (2) implementation of a superpixel segmentation method to segment captured radish field images into separated segments; (3) a customized deep learning-based radish identification framework for the extracted segmented images, which achieved remarkable performance in terms of accuracy and robustness with the highest accuracy of 96%; (4) the proposal for a disease severity analysis that can detect different stages of the wilt disease; (5) showing that the approach based on NIR images is more straightforward and effective in detecting wilt disease than the learning approach based on the RGB dataset.


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