جستجوی مقالات مرتبط با کلیدواژه « unmanned aerial vehicle » در نشریات گروه « کشاورزی »

Today, intelligent agriculture is developing with the advancement of electronic and information technologies. The key to access intelligent agriculture is data gathering and analysis. Therefore, data gathering is one of the fundamental actions to implement precision and intelligent agriculture. This paper aims to introduce the audience to the various data acquisition platforms such as satellite, aerial (manned and unmanned), and ground vehicle platforms and to present the features and limitations of each method based on technical and economic parameters. Technical parameters that influence decision-making include spatial resolution, spectral and temporal resolution, maneuverability, sensor variety, the extent of the covered area, and accuracy. Based on the results, all types of data acquisition technologies can be used in agriculture, and selecting the appropriate system should be based on technical and budget considerations, the availability of technology, the size of the region, and the object. By reading this paper, the audience can choose the right data acquisition system for their executive or research activities in the field of agriculture

In order to present a new, non-destructive, accurate, and fast method for estimating the nitrogen content of corn, Unmanned Aerial Vehicle (UAV) multispectral sensing technology was used.

The experiments were performed based on a randomized complete block design in four levels of nitrogen fertilizer (zero, 50, 100, and 150%) in Varamin in 2018. Sampling was carried out in two stages of fertilization (8-leaf Stage and Tasseling Stage). Multispectral aerial imaging and ground sampling was performed one week after each fertilizer application. After processing aerial imagery, vegetation indices were calculated and their correlation with the results of ground sampling was determined.

Based on the results obtained from the correlation coefficients (r) and best subsets regression, among the spectral vegetation indices, Normalized Difference Vegetation Index (NDVI), Nitrogen Reflectance Index (NIR), and Modified Triangular Vegetation Index2 (MTVI2) indices in both eight leaf collar (V8) and tasseling (VT) of maize growth stage was identified as the best indicator to estimate the nitrogen content of forage maize. At VT, a positive and significant relationship was obtained between NDVI (R2= 0.86, P≤0.001), NRI (R2= 0.70, P≤0.001) and MTVI2 (R2= 0.46, P≤0.01) indices with maize nitrogen content.

It can be concluded that UAV multispectral imaging provides acceptable accuracy in determining the nitrogen content of maize. This technology can help farmers to determine the appropriate time of fertilization.

In the present century, the need to provide food security for the growing population of the country, the importance of maintaining human health and the environment, as well as the need to continuously increase the productivity of inputs, has made the use of new technologies in the production of agricultural products more necessary. One of the new technologies is Intelligent agriculture. This paper has tried to present the importance of remote sensing technology as one of the most important parts of intelligent agriculture, namely data collection and recording. Also, the types of platforms and sensors that have been introduced in remote sensing studies and their applications in agriculture, as well as the advantages and limitations of each method are described. In the present century, the need to provide food security for the growing population of the country, the importance of maintaining human health and the environment, as well as the need to continuously increase the productivity of inputs, has made the use of new technologies in the production of agricultural products more necessary. One of the new technologies is Intelligent agriculture. This paper has tried to present the importance of remote sensing technology as one of the most important parts of intelligent agriculture, namely data collection and recording. Also, the types of platforms and sensors that have been introduced in remote sensing studies and their applications in agriculture, as well as the advantages and limitations of each method are described.

The Internet of Things is a new technology that is conceived as a global network of machines and devices that can interact with each other. The Internet of Things is also known as one of the most important fields of technology of the future and has been considered by many industries. This system mimics the characteristics of emotion, intelligence, and the ability to think, memorize, make decisions, and react to the physical environment from the human brain. Also, from three parts of different types of sensors, the communication network of information processing units and intelligent and data management and processing units have been formed. The study, conducted using a large library study and using credible international information, traces the IoT from the background to applications and examines all the small to large components of this on-farm approach. Then the application of this concept in irrigation, especially in automation and intelligent irrigation systems in the field was investigated. Finally, common tools such as in-field sensors, wireless sensor networks, satellites, and the use of an unmanned aerial vehicle as a means to achieve the goal of communication between the Internet of Things and intelligent farm irrigation systems, were introduced. Finally, the benefits, applications, and challenges of the present and future of the Internet of Things in irrigation were outlined.

Piping erosion is an influential landform of intensified water erosion that interact with each other in the loess-derived soilsof the Province of Golestan (NE Iran). This study attempts to emphasize the use of high-precision data through field surveys and UAV images, and then to establish a threshold to study the factors that affect piping erosion. The present study was carried out using photogrammetric drones in an area of ​​approximately 2700 hectares in the loess lands in the east of the Province of Golestan and identified areas affected by piping erosion. A total of 833 pipes were recorded using the GPS and the orthophoto images from the UAV. Topographic, hydrological, and biological factors were treated as independent variables and piping erosion as dependent variables through statistical analyzes. The topographic factors indicated that the maximum density of piping occurred at altitudes of between 300–350 m, at maximum slope of more than 30%, at slope length less than 5m, and in concave tracts and profile curvatures. Regarding the hydrological factors, the maximum piping density occurred at the maximum numerical value of the topographic wetness index (more than 12), and at the closest distance to waterways (less than 100m). Biological factors indicated that the maximum density of pipes is in the rangelands and where this erosional facies is closest to the roads. Therefore, it can be suggested that an accurate identification of the piping erosion is facilated through ground observations aided by high-precision areal photography and understanding causative factors developing them.

Understanding the ecological and geomorphological processes of the spatial distribution of loess deposits helps us to find their interactions in the arid and semi-arid regions. The Iranian loess plateau with a unique landscape and complex topography also located in steppe vegetation with semi-arid climate. The aims of present study are point pattern analysis of hillside, stream, and their interactions using different summary statistics in the particular part of Iranian loess plateau. As there is a small distance between hillslope and complex topography in the study area, unmanned aerial vehicle (UAV) imagery used to prepare precise colored aerial photos for statistical analyses. Further, regarding to the mentioned purposes, this research has enough novelty compare with previous studies, and thus, it is a new step in studying the spatial pattern of loess facies; In other words, this study attempts to find the effective relationship between the hillside and streams in terms of the expansion of channel erosion in the study region.

The study area has dry Xeric soil moisture and Thermic soil temperature regimes. The UAV technique used to prepare the precise colorful images with highly spatial/temporal resolution to model the spatial patterns of hillside and streams density. The topographic attributes (primary and secondary) obtained from digital elevation model (DEM) applied with a spatial resolution of 20×20 cm. The univariate and bivariate point analyses (modelling) used for variable analyses in Spatstat package in R and Progammita software. Finally, Mark correlation function (MCF) used to investigate the question of reducing the size of density dependent.

The results of univariate g(r) and O-ring (r) showed that different hillside facing i.e. flat, north, south, east, and west have the aggregated pattern in all distances in the study area. These implied that the same directions are distributed more closely next to each other and their arrangements follow the special pattern. In addition, the interaction between streams and north-face using the bivariate g12(r) and O12(r) confirmed the positive interaction between streams and north-face in all distances in the study area. The MCF analysis also showed that the slope as the effective factor has positive interaction with streams, and steep slopes are more aggregated compare with the low slopes. This indicates which the steeper slopes are more likely to form streams than flat lands.

Generally, the streams probability form in the steep slopes more than flat areas due to their shear energy, which leads to more soil losses. Further, the soil loss on the steep slopes is more than flat terrain. Finally, UAV technologies are recommended in the study of spatial pattern of deposits for detailed observations, highly accurate data, and deciding natural resource managers to reduce soil erosion.

Leaf area index (LAI) is an important indicator of plant growth and yield. Therefore, monitoring the spatial and temporal distribution of LAI at agricultural farms could be a significant predictor of how well the various elements of farm management strategies such as irrigation scheduling and uniformity have been implemented. The purpose of this study is to outline how pictures taken by a modified digital camera can be used for estimating the LAI of a corn silage field. It focuses on how to utilize a combination of a simple digital photographic cameras with spectral filters, designed to provide multispectral images in the visible and near-infrared domains, to estimate LAI. In order to remove the sources of errors in the resulting images, procedures to perform image vignetting corrections, geometric distortions corrections, and elimination of radiometric bidirectional effects are suggested. Due to high spatial resolution of this imaging system (at the level of a few centimeters), separation of surfaces with and without plant cover was accomplished well. This separation process was also useful in determination of percentage of vegetation cover (crop density). The leaf area index had the highest correlation with the vegetation cover percentage (R2 = 0/919), and the NIR spectral band (R2 = 0/741). There was a high correlation between the two spectra of red and green with the NIR spectral band. This correlation indicates that with the presence of the NIR spectral band, the effect of red and green spectral bands on estimating leaf area index is insignificant. Therefore, a multivariable regression model was generated to estimate leaf area index as a function of only two parameters, namely vegetation cover percentage and spectral band NIR. The performance of the developed model was evaluated by comparing its predicted values of LAI with corresponding measured values. The adjusted coefficient of determination of this comparison was 96.6%, which indicates that 96.6% of the variation in the estimated leaf area index values is explained by the two variables (vegetation cover percentage and NIR spectral band) incorporated into the model.

Crown cover of trees in arid and semi-arid regions is of great importance as the parts of ground under tree canopies are favourable environments for regeneration establishment and survival of other organisms. Therefore, it is essential to be aware of crown cover status of trees and shrubs, monitor their changes and assess their health. Remote sensing data obtained by most of satellites do not make unbiased measurement of crown area of single trees possible as their spatial resolution is not suitable for this purpose. On the other hand, these data are not available at the time researchers need, and if available, they are expensive. Continuous progress in remote sensing results in access of researchers to unmanned aerial vehicle (UAV) that imagery taken by this device have not only very high spatial resolution for precise study of biometric characteristics of single trees, but also availability of images at favourable time for researchers. Considering this issue, this study was aimed to evaluate UAV imagery and corresponding digital surface model (DSM) resulted from stereo images to estimate crown area of wild pistachio single trees in Zagros vegetation zone. Moreover, it was also aimed to investigate the impact of spatial resolution of DSM on accuracy and precision of estimating crown area of the trees.
A part of Baneh Research Site with area of 45 ha was selected that was purely cover by wild pistachio. In October 2016, the study area was covered by 1076 aerial images with 3 cm spatial resolution taken by a UAV flown at 70 m above the study area. The images were geo-referenced using 12 ground control points collected by Leica Viva GS15 three-frequency global positioning system. The DSM extracted using bundle adjustment method was then resampled to three different spatial resolutions of 3, 50, and 100 cm. An object-based processing method was proposed in order to automatically extract the crown boundaries from DSM. Number of 100 wild pistachio trees were randomly selected that their spatial position were registered and their crown areas were measured before. The mean crown area obtained by DSM of UAV imagery and the observed mean were compared by paired sample t-test. In addition, three indices of root mean squared error (RMSE), model efficiency (ME), and bias score (BS) were applied to assess the precision of results.
The orhtophoto mosaic of the study area was obtained with RMSE of 8 cm. Among 100 wild pistachio trees randomly selected for this study, 100, 89, and 80 trees were recognized on DSMs with spatial resolution of 3, 50, and 100 cm, respectively. Although there was no significant difference between observed mean crown area of 80 trees (51.3 m2) recognized on all DSMs and mean crown area estimated on DSMs with 3 (42.6 m2), 50 (44.5 m2), and 100 cm (39.2 m2) (α=0.05), but their correlation decreased. Moreover, RMSE increased and ME and BS decreased with decreasing spatial resolution of DSM.
In general, it was concluded that DSM of UAV imagery is an appropriate means to recognize and measure crown area of wild pistachio single trees in the study area that obviously separated tree crowns from their shadow and other objects. Moreover, it was revealed that with decreasing spatial resolution of DSM, data processing became easier and there was no significant difference between observations and measurements but the precision of results decreased.