scholarly journals UAV Detection of Sinapis arvensis Infestation in Alfalfa Plots Using Simple Vegetation Indices from Conventional Digital Cameras

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 ◽  
...  
Keyword(s):  
Unmanned Aerial Vehicles ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Low Cost ◽  
Vegetation Indices ◽  
Digital Cameras ◽  
Site Specific ◽  
Aerial Vehicles ◽  
Ndvi Index ◽  
Weed Cover

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.

scholarly journals Detecting Individual Tree Attributes and Multispectral Indices Using Unmanned Aerial Vehicles: Applications in a Pine Clonal Orchard

2020 ◽  
Vol 12 (24) ◽  
pp. 4144
Author(s):  
José Luis Gallardo-Salazar ◽  
Marín Pompa-García
Keyword(s):  
Unmanned Aerial Vehicles ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Vegetation Indices ◽  
Field Measurements ◽  
Tree Level ◽  
Individual Tree ◽  
Basal Diameter ◽  
Crown Diameter ◽  
Aerial Vehicles

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.

Evaluation of a Low-cost Camera for Agricultural Applications

2019 ◽  
pp. 1-9 ◽  
Author(s):  
Abdon Francisco Aureliano Netto ◽  
Rodrigo Nogueira Martins ◽  
Guilherme Silverio Aquino De Souza ◽  
Fernando Ferreira Lima Dos Santos ◽  
Jorge Tadeu Fim Rosas
Keyword(s):  
Vegetation Index ◽  
Near Infrared ◽  
Normalized Difference Vegetation Index ◽  
Low Cost ◽  
Vegetation Indices ◽  
Paspalum Notatum ◽  
Crop Canopy ◽  
N Application ◽  
Satisfactory Accuracy ◽  
Agricultural Applications

This study aimed to modify a webcam by replacing its near-infrared (NIR) blocking filter to a low-cost red, green and blue (RGB) filter for obtaining NIR images and to evaluate its performance in two agricultural applications. First, the sensitivity of the webcam to differentiate normalized difference vegetation index (NDVI) levels through five nitrogen (N) doses applied to the Batatais grass (Paspalum notatum Flugge) was verified. Second, images from maize crops were processed using different vegetation indices, and thresholding methods with the aim of determining the best method for segmenting crop canopy from the soil. Results showed that the webcam sensor was capable of detecting the effect of N doses through different NDVI values at 7 and 21 days after N application. In the second application, the use of thresholding methods, such as Otsu, Manual, and Bayes when previously processed by vegetation indices showed satisfactory accuracy (up to 73.3%) in separating the crop canopy from the soil.

scholarly journals Digital aerial images land cover classification based on vegetation indices

2012 ◽  
Vol 31 (3) ◽  
pp. 5-23
Author(s):  
Maciej Dzieszko ◽  
Piotr Dzieszko ◽  
Sławomir Królewicz
Keyword(s):  
Land Cover ◽  
Image Classification ◽  
Geographical Information Systems ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Low Cost ◽  
Vegetation Indices ◽  
Aerial Images ◽  
Geographical Information ◽  
Wide Availability

Abstract . Knowledge of how land cover has changed over time improve assessments of the changes in the future. Wide availability of remote sensed data and relatively low cost of their acquisition make them very attractive data source for Geographical Information Systems (GIS). The main goal of this paper is to prepare, run and evaluate image classification using a block of raw aerial images obtained from Digital Mapping Camera (DMC). Classification was preceded by preparation of raw images. It contained geometric and radiometric correction of every image in block. Initial images processing lead to compensate their brightness differences. It was obtained by calculating two vegetation indices: Normalized Difference Vegetation Index (NDVI) and Green Normalized Vegetation Index (gNDVI). These vegetation indices were the foundation of image classification. PCI Geomatics Geomatica 10.2 and Microimages TNT Mips software platforms were used for this purpose.

scholarly journals Object-Based Analysis Using Unmanned Aerial Vehicles (UAVs) for Site-Specific Landslide Assessment

2020 ◽  
Vol 12 (11) ◽  
pp. 1711 ◽  
Author(s):  
Efstratios Karantanellis ◽  
Vassilis Marinos ◽  
Emmanuel Vassilakis ◽  
Basile Christaras
Keyword(s):  
High Resolution ◽  
Unmanned Aerial Vehicles ◽  
Low Cost ◽  
Mass Movement ◽  
Terrain Analysis ◽  
Site Specific ◽  
High Resolution Data ◽  
Aerial Vehicles ◽  
Object Based

The increased development of computer vision technology combined with the increased availability of innovative platforms with ultra-high-resolution sensors, has generated new opportunities and fields for investigation in the engineering geology domain in general and landslide identification and characterization in particular. During the last decade, the so-called Unmanned Aerial Vehicles (UAVs) have been evaluated for diverse applications such as 3D terrain analysis, slope stability, mass movement hazard and risk management. Their advantages of detailed data acquisition at a low cost and effective performance identifies them as leading platforms for site-specific 3D modelling. In this study, the proposed methodology has been developed based on Object-Based Image Analysis (OBIA) and fusion of multivariate data resulted from UAV photogrammetry processing in order to take full advantage of the produced data. Two landslide case studies within the territory of Greece, with different geological and geomorphological characteristics, have been investigated in order to assess the developed landslide detection and characterization algorithm performance in distinct scenarios. The methodology outputs demonstrate the potential for an accurate characterization of individual landslide objects within this natural process based on ultra high-resolution data from close range photogrammetry and OBIA techniques for landslide conceptualization. This proposed study shows that UAV-based landslide modelling on the specific case sites provides a detailed characterization of local scale events in an automated sense with high adaptability on the specific case site.

scholarly journals ULTRA-HIGH SPATIAL RESOLUTION UAV-BASED IMAGERY TO PREDICT BIOMASS IN TEMPERATE GRASSLANDS

2019 ◽  
Vol XLII-2/W13 ◽  
pp. 443-447 ◽  
Author(s):  
U. Lussem ◽  
A. Bolten ◽  
J. Menne ◽  
M. L. Gnyp ◽  
G. Bareth
Keyword(s):  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Biomass Yield ◽  
Low Cost ◽  
Rapid Assessment ◽  
Vegetation Indices ◽  
Multispectral Data ◽  
Efficient Data ◽  
Spatio Temporal ◽  
Cost Efficient

<p><strong>Abstract.</strong> Monitoring biomass yield in grassland is of key importance to support sustainable management decisions. Especially the high spatio-temporal variety in grasslands requires rapid and cost-efficient data acquisition with a high spatial and temporal resolution. Therefore, this study aims to evaluate the comparability of UAV-based simultaneously acquired vegetation indices from a consumer-grade RGB-camera (Sony Alpha 6000) and a well-calibrated narrow-band multispectral camera (MicaSense RedEdge-M) to estimate dry matter biomass yield. The study site is an experimental grassland field in Germany with four nitrogen fertilizer levels. Biomass yield and UAV-based data for the first cut in May 2018 was analysed in this study. From the RGB-data the Plant Pigment Ratio Index (PPR) and the Normalized Green Red Difference Index (NGRDI) and from the multispectral data the Normalized Difference Vegetation Index (NDVI) are calculated as predictors for dry biomass yield. The NGRDI and NDVI perform moderately well with cross-validation R<sup>2</sup> of 0.57 and 0.63 respectively, while the PPR performs better with an R<sup>2</sup> of 0.70. These results indicate the potential of low-cost UAV-based methods for rapid assessment of grasslands.</p>

scholarly journals Actual Evapotranspiration and Biomass of Maize from a Red–Green-Near-Infrared (RGNIR) Sensor on Board an Unmanned Aerial Vehicle (UAV)

Water ◽  
2020 ◽  
Vol 12 (9) ◽  
pp. 2359 ◽  
Author(s):  
Robson Argolo dos Santos ◽  
Everardo Chartuni Mantovani ◽  
Roberto Filgueiras ◽  
Elpídio Inácio Fernandes-Filho ◽  
Adelaide Cristielle Barbosa da Silva ◽  
...  
Keyword(s):  
Decision Making ◽  
Precision Agriculture ◽  
Vegetation Index ◽  
Near Infrared ◽  
Normalized Difference Vegetation Index ◽  
Low Cost ◽  
Vegetation Indices ◽  
Maize Crop ◽  
Evaporative Fraction ◽  
Dry Biomass

Surface reflectance data acquisition by unmanned aerial vehicles (UAVs) are an important tool for assisting precision agriculture, mainly in medium and small agricultural properties. Vegetation indices, calculated from these data, allow one to estimate the water consumption of crops and predict dry biomass and crop yield, thereby enabling a priori decision-making. Thus, the present study aimed to estimate, using the vegetation indices, the evapotranspiration (ET) and aboveground dry biomass (AGB) of the maize crop using a red–green-near-infrared (RGNIR) sensor onboard a UAV. For this process, 15 sets of images were captured over 61 days of maize crop monitoring. The images of each set were mosaiced and subsequently subjected to geometric correction and conversion from a digital number to reflectance to compute the vegetation indices and basal crop coefficients (Kcb). To evaluate the models statistically, 54 plants were collected in the field and evaluated for their AGB values, which were compared through statistical metrics to the data estimated by the models. The Kcb values derived from the Soil-Adjusted Vegetation Index (SAVI) were higher than the Kcb values derived from the Normalized Difference Vegetation Index (NDVI), possibly due to the linearity of this model. A good agreement (R2 = 0.74) was observed between the actual transpiration of the crop estimated by the Kcb derived from SAVI and the observed AGB, while the transpiration derived from the NDVI had an R2 of 0.69. The AGB estimated using the evaporative fraction with the SAVI model showed, in relation to the observed AGB, an RMSE of 0.092 kg m−2 and an R2 of 0.76, whereas when using the evaporative fraction obtained through the NDVI, the RMSE was 0.104 kg m−2, and the R2 was 0.74. An RGNIR sensor onboard a UAV proved to be satisfactory to estimate the water demand and AGB of the maize crop by using empirical models of the Kcb derived from the vegetation indices, which are an important source of spatialized and low-cost information for decision-making related to water management in agriculture.

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
Keyword(s):  
Unmanned Aerial Vehicles ◽  
Texture Analysis ◽  
Vegetation Index ◽  
Vegetation Type ◽  
Digital Cameras ◽  
Vegetation Types ◽  
Aerial Vehicles ◽  
Rgb Images ◽  
Thematic Processing ◽  
Surface Vegetation

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.

scholarly journals Comparison of Sentinel-2 and UAV Multispectral Data for Use in Precision Agriculture: An Application from Northern Greece

Drones ◽  
2021 ◽  
Vol 5 (2) ◽  
pp. 35
Author(s):  
Nikolaos Bollas ◽  
Eleni Kokinou ◽  
Vassilios Polychronos
Keyword(s):  
Unmanned Aerial Vehicles ◽  
Precision Agriculture ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Northern Greece ◽  
Multispectral Data ◽  
Aerial Vehicles ◽  
The Difference ◽  
Uav Images ◽  
Sentinel 2

The scope of this work is to compare Sentinel-2 and unmanned aerial vehicles (UAV) imagery from northern Greece for use in precision agriculture by implementing statistical analysis and 2D visualization. Surveys took place on five dates with a difference between the sensing dates for the two techniques ranging from 1 to 4 days. Using the acquired images, we initially computed the maps of the Normalized Difference Vegetation Index (NDVI), then the values of this index for fifteen points and four polygons (areas). The UAV images were not resampled, aiming to compare both techniques based on their initial standards, as they are used by the farmers. Similarities between the two techniques are depicted on the trend of the NDVI means for both satellite and UAV techniques, considering the points and the polygons. The differences are in the a) mean NDVI values of the points and b) range of the NDVI values of the polygons probably because of the difference in the spatial resolution of the two techniques. The correlation coefficient of the NDVI values, considering both points and polygons, ranges between 83.5% and 98.26%. In conclusion, both techniques provide important information in precision agriculture depending on the spatial extent, resolution, and cost, as well as the requirements of the survey.

HEALTHINESS OF OIL PALM PLANTATION TOWARDS SUSTAINABILITY OF ENVIRONMENT

2020 ◽  
Vol 7 (1) ◽  
pp. 21
Author(s):  
Faradina Marzukhi ◽  
Nur Nadhirah Rusyda Rosnan ◽  
Md Azlin Md Said
Keyword(s):  
Oil Palm ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Satellite Image ◽  
Vegetation Indices ◽  
Soil Nutrient ◽  
Oil Palm Plantation ◽  
Sustainable Environment ◽  
Moisture Analysis ◽  
The Relationship

The aim of this study is to analyse the relationship between vegetation indices of Normalized Difference Vegetation Index (NDVI) and soil nutrient of oil palm plantation at Felcra Nasaruddin Bota in Perak for future sustainable environment. The satellite image was used and processed in the research. By Using NDVI, the vegetation index was obtained which varies from -1 to +1. Then, the soil sample and soil moisture analysis were carried in order to identify the nutrient values of Nitrogen (N), Phosphorus (P) and Potassium (K). A total of seven soil samples were acquired within the oil palm plantation area. A regression model was then made between physical condition of the oil palms and soil nutrients for determining the strength of the relationship. It is hoped that the risk map of oil palm healthiness can be produced for various applications which are related to agricultural plantation.

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