scholarly journals Sensing Technology Survey for Obstacle Detection in Vegetation

2021 ◽  
Vol 1 (3) ◽  
pp. 672-685
Author(s):  
Shreya Lohar ◽  
Lei Zhu ◽  
Stanley Young ◽  
Peter Graf ◽  
Michael Blanton
Keyword(s):  
Autonomous Vehicles ◽  
Weather Conditions ◽  
Climatic Conditions ◽  
Obstacle Detection ◽  
Agricultural Field ◽  
Thermal Camera ◽  
Agricultural Vehicles ◽  
Sensing Systems ◽  
Sensing Technology ◽  
Hyperspectral Sensing

This study reviews obstacle detection technologies in vegetation for autonomous vehicles or robots. Autonomous vehicles used in agriculture and as lawn mowers face many environmental obstacles that are difficult to recognize for the vehicle sensor. This review provides information on choosing appropriate sensors to detect obstacles through vegetation, based on experiments carried out in different agricultural fields. The experimental setup from the literature consists of sensors placed in front of obstacles, including a thermal camera; red, green, blue (RGB) camera; 360° camera; light detection and ranging (LiDAR); and radar. These sensors were used either in combination or single-handedly on agricultural vehicles to detect objects hidden inside the agricultural field. The thermal camera successfully detected hidden objects, such as barrels, human mannequins, and humans, as did LiDAR in one experiment. The RGB camera and stereo camera were less efficient at detecting hidden objects compared with protruding objects. Radar detects hidden objects easily but lacks resolution. Hyperspectral sensing systems can identify and classify objects, but they consume a lot of storage. To obtain clearer and more robust data of hidden objects in vegetation and extreme weather conditions, further experiments should be performed for various climatic conditions combining active and passive sensors.

Unsettled Topics in Obstacle Detection for Autonomous Agricultural Vehicles

2021 ◽  
Author(s):  
Stewart Moorehead ◽  
Keyword(s):  
Object Detection ◽  
Environmental Conditions ◽  
Autonomous Vehicles ◽  
Close Contact ◽  
Obstacle Detection ◽  
Agricultural Vehicles ◽  
Environmental Object ◽  
Collision Prevention ◽  
The Future ◽  
Agricultural Machines

Agricultural vehicles often drive along the same terrain day after day or year after year. Yet, they still must detect if a moveable object, such as another vehicle or an animal, happens to be on their path or if environmental conditions have caused muddy spots or washouts. Obstacle detection is one of the major missing pieces that can remove humans from highly automated agricultural machines today and enable the autonomous vehicles of the future. Unsettled Topics in Obstacle Detection for Autonomous Agricultural Vehicles examines the challenges of environmental object detection and collision prevention, including air obscurants, holes and soft spots, prior maps, vehicle geometry, standards, and close contact with large objects.

PERSONALIZED APPROACH TO THE ORGANIZATION OF MEDICAL CARE FOR PATIENTS WITH WEATHER DEPENDENCE

2020 ◽  
pp. 35-39
Author(s):  
Klepikov O.V. ◽  
Kolyagina N.M. ◽  
Berezhnova T.A. ◽  
Kulintsova Ya.V.
Keyword(s):  
Medical Care ◽  
Cardiovascular System ◽  
Performance Indicators ◽  
Weather Conditions ◽  
Climatic Conditions ◽  
Outpatient Clinics ◽  
Vulnerable Groups ◽  
Pathological Effect ◽  
Weather Forecasts ◽  
Personalized Approach

Relevance. Today, in preventive medicine, climatic conditions that have a pathological effect on the functional state of a person are increasingly being updated. the occurrence of exacerbations of many diseases can be causally associated with various weather conditions. Aim: to develop the main tasks for improving the organization of medical care for weather-dependent patients with diseases of the cardiovascular system. Material and methods. The assessment of personnel, material and technical support and the main performance indicators of an outpatient clinic was carried out on the example of the Voronezh city polyclinic No. 18 to develop the main tasks for improving the organization of medical care for weather-dependent patients with diseases of the cardiovascular system. Results. The main personnel problem is the low staffing of district therapists and specialists of a narrow service. One of the priorities for reducing the burden on medical hospitals is the organization of inpatient replacement medical care on the basis of outpatient clinics. The indicators for the implementation of state guarantees for the outpatient network for 2018, which were fully implemented, are given. The analysis of the planned load performance by polyclinic specialists is presented. Cardiological and neurological services carry out measures to reduce the risk of exacerbations of diseases with cerebral atherosclerosis, hypertension, and major neurological nosologies. Conclusion. Improving the organization of medical care for weather-dependent patients with cardiovascular diseases are: informing patients about the sources of specialized medical weather forecasts in the region, organizing the work of the medical prevention office, implementing an interdepartmental approach to providing health care to the most vulnerable groups of the population.

Influence of meteorological factors on potato yield in the conditions of the Right-Bank Forest-steppe

2018 ◽  
Vol 1 (94) ◽  
pp. 55-61
Author(s):  
R.O. Myalkovsky
Keyword(s):  
Meteorological Factors ◽  
Weather Conditions ◽  
Climatic Conditions ◽  
Potato Yield ◽  
High Yield ◽  
Forest Steppe ◽  
Potato Tubers ◽  
Middle Aged ◽  
Lower Yield ◽  
The Right

Goal. The purpose of the research was to determine the influence of meteorological factors on potato yield in the conditions of the Right Bank Forest-steppe of Ukraine. Methods.Field, analytical and statistical. Results.It was established that among the mid-range varieties Divo stands out with a yield of 42.3 t/ha, Malin white – 39.8 t/ha, and Legend – 37.1 t/ ha. The most favourable weather and climatic conditions for the production of potato tubers were for the Divo 2011 variety with a yield of 45.9 t/ha and 2013 – 45.1 t/ha. For the Legenda variety 2016, the yield of potato tubers is 40.6 t/ha and 2017 – 43.2 t/ha. Malin White 2013 is 41.4 t/ha and 2017 42.1 t/ha. The average varieties of potatoes showed a slightly lower yield on average over the years of research. However, among the varieties is allocated Nadiyna – 40.3 t/ha, Slovyanka – 37.2 t/ ha and Vera 33.8 t/ha. Among the years, the most high-yielding for the Vera variety was 2016 with a yield of 36.6 t/ha and 2017 year – 37.8 t/ha. Varieties Slovyanka and Nadiyna 2011 and 2012 with yields of 42.6 and 44.3 t/ha and 46.5 and 45.3 t/ha, respectively. Characterizing the yield of potato tubers of medium-late varieties over the years of research, there was a decrease in this indicator compared with medium-early and middle-aged varieties. However, the high yield of the varieties of Dar is allocated – 40.0 t/ha, Alladin – 33.6 t/ha and Oxamit 31.3 t/ha. Among the years, the most favourable ones were: for Oxamit and Alladin – 2011 – 33.5 and 36.5 t/ha, and 2017 – 34.1 and 36.4 t/ha, respectively. Favourable years for harvesting varieties were 2011 and 2012 with yields of 45.7 and 45.8 t/ha. Thus, the highest yield of potato tubers on average over the years of studies of medium-early varieties of 41.2-43.3 t / ha were provided by weather conditions of 2011 and 2017 years, medium-ripe varieties 41.0-41.1 - 2012 and 2011, medium- late 37,6-38,5 t / ha - 2012 and 2011, respectively.

Optimal Command for Photovoltaic Systems in Real Outdoor Weather Conditions

2019 ◽  
Vol 142 (1) ◽  
Author(s):  
Hafsa Abouadane ◽  
Abderrahim Fakkar ◽  
Benyounes Oukarfi
Keyword(s):  
Maximum Power Point Tracking ◽  
Accurate Determination ◽  
Weather Conditions ◽  
Climatic Conditions ◽  
Maximum Power ◽  
Photovoltaic System ◽  
Maximum Power Point ◽  
Point Tracking ◽  
Power Point Tracking ◽  
Power Point

The photovoltaic panel is characterized by a unique point called the maximum power point (MPP) where the panel produces its maximum power. However, this point is highly influenced by the weather conditions and the fluctuation of load which drop the efficiency of the photovoltaic system. Therefore, the insertion of the maximum power point tracking (MPPT) is compulsory to track the maximum power of the panel. The approach adopted in this paper is based on combining the strengths of two maximum power point tracking techniques. As a result, an efficient maximum power point tracking method is obtained. It leads to an accurate determination of the MPP during different situations of climatic conditions and load. To validate the effectiveness of the proposed MPPT method, it has been simulated in matlab/simulink under different conditions.

scholarly journals Time- and Resource-Efficient Time-to-Collision Forecasting for Indoor Pedestrian Obstacles Avoidance

2021 ◽  
Vol 7 (4) ◽  
pp. 61
Author(s):  
David Urban ◽  
Alice Caplier
Keyword(s):  
Neural Network ◽  
Autonomous Vehicles ◽  
Depth Estimation ◽  
Video Camera ◽  
Obstacle Detection ◽  
Navigation Systems ◽  
Time To Collision ◽  
Static Data ◽  
Monocular Depth ◽  
Fully Connected

As difficult vision-based tasks like object detection and monocular depth estimation are making their way in real-time applications and as more light weighted solutions for autonomous vehicles navigation systems are emerging, obstacle detection and collision prediction are two very challenging tasks for small embedded devices like drones. We propose a novel light weighted and time-efficient vision-based solution to predict Time-to-Collision from a monocular video camera embedded in a smartglasses device as a module of a navigation system for visually impaired pedestrians. It consists of two modules: a static data extractor made of a convolutional neural network to predict the obstacle position and distance and a dynamic data extractor that stacks the obstacle data from multiple frames and predicts the Time-to-Collision with a simple fully connected neural network. This paper focuses on the Time-to-Collision network’s ability to adapt to new sceneries with different types of obstacles with supervised learning.

Selection of drought-resistant crops for South Steppe of Ukraine

2021 ◽  
Vol 5 (1) ◽  
pp. 13-22
Author(s):  
V. V. Gamayunova ◽  
L. H. Khonenko ◽  
M. I. Fedorchuk ◽  
O. A. Kovalenko
Keyword(s):  
Humus Content ◽  
Soil Layer ◽  
Weather Conditions ◽  
Growing Season ◽  
Grain Sorghum ◽  
Climatic Conditions ◽  
Mineral Fertilizers ◽  
Chemical Application ◽  
Sowing Dates ◽  
Drought Resistant

The cultivation expediency of more drought-resistant crops, in particular sorghum, millet, false flax, safflower and others, instead of sunflower in the area of the Southern Steppe of Ukraine is substantiated. This is, first of all, required by climate change both in Ukraine and in the world. Since 2004, researches of field crops were carried out in the conditions of the Educational and Scientific Practical Center of the Mykolaiv National Agrarian University. Soil phase is the southern chernozem with humus content in the 0–30 cm soil layer which consist of 2.96–3.21 %, with medium and high level of availability of mobile phosphorus and potassium and low – mobile nitrogen. Experiments with soriz (Oksamyt hybrid) were conducted during 2004–2006, millet (Tavriiske, Kostantynivske, Skhidnevarieties) in 2008–2010, grain sorghum (Stepovyi 5 hybrid) in 2014–2016, safflower dye (Lahidnyi variety) in 2017–2019. The years of research differed significantly in temperature and even more in the amount of precipitation before sowing and during the growing season of crops. However, the weather conditions were typical of the Southern Steppe zone of Ukraine. It is established that all studied drought-resistant crops respond positively to nutrition optimization – the level of yield and quality of grain or seeds increases. It was found that the soriz productivity depending on the application of fertilizers and sowing dates increased by 37.6–39.2 %, millet –by 33.3–41.6 %, grain sorghum depending on the background of nutrition and growing conditions – by 8.2–33.2 %, dye safflower – by 11.1–64.6 %. It was determined that the optimization of nutrition of cultivated crops allows to increase their resistance to adverse conditions and productivity in the case of application of low doses of the mineral fertilizers before sowing, pre-sowing treatment of seeds, and growth-regulating chemical application of plants on the main stages of the growing season. Key words: drought-resistant plants, climatic conditions, nutrition optimization, yield, crop quality, varieties, sowing dates.

scholarly journals Surface Changes of Selected Hardwoods Due to Weather Conditions

Forests ◽  
2018 ◽  
Vol 9 (9) ◽  
pp. 557 ◽  
Author(s):  
Ivan Kubovský ◽  
Eliška Oberhofnerová ◽  
František Kačík ◽  
Miloš Pánek
Keyword(s):  
Laser Scanning ◽  
Weather Conditions ◽  
Degradation Process ◽  
Climatic Conditions ◽  
Laser Scanning Microscopy ◽  
Dust Particles ◽  
Confocal Laser ◽  
Chemical Changes ◽  
Colour Changes ◽  
Surface Changes

The study is focused on the surface changes of five hardwoods (oak, black locust, poplar, alder and maple) that were exposed to natural weathering for 24 months in the climatic conditions of Central Europe. Colour, roughness, visual and chemical changes of exposed surface structures were examined. The lowest total colour changes (ΔE*) were found for oak (23.77), the highest being recorded for maple (34.19). Roughness differences after 24-month exposure (ΔRa) showed minimal changes in poplar wood (9.41); the highest changes in roughness were found on the surface of alder (22.18). The presence of mould and blue stains was found on the surface of maple, alder and poplar. Chemical changes were characterized by lignin and hemicelluloses degradation. Decreases of both methoxy and carbonyl groups, cleavage of bonds in lignin and hemicelluloses, oxidation reaction and formation of new chromophores were observed. In the initial phases of the degradation process, the discoloration was related to chemical changes; in the longer period, the greying due to settling of dust particles and action of mould influenced the wood colour. The data were confirmed by confocal laser scanning microscopy. The obtained results revealed degradation processes of tested hardwood surfaces exposed to external environmental factors.

scholarly journals AGRO-CLIMATIC SUITABILITY AND WATER REQUIREMENT FOR TANZANIA GUINEAGRASS CULTIVATION IN THE STATE OF CEARÁ

2017 ◽  
Vol 30 (4) ◽  
pp. 1028-1038 ◽  
Author(s):  
NAILSON LIMA SANTOS LEMOS ◽  
ANA CLARA RODRIGUES CAVALCANTE ◽  
THIERES GEORGE FREIRE DA SILVA ◽  
JOSÉ RICARDO MACEDO PEZZOPANE ◽  
PATRÍCIA MENEZES SANTOS ◽  
...  
Keyword(s):  
Sandy Soils ◽  
Weather Conditions ◽  
Climatic Conditions ◽  
Water Requirement ◽  
The State ◽  
Climatic Suitability ◽  
Irrigation Water Requirement ◽  
Productive Potential ◽  
Shallow Soils ◽  
Water Holding

ABSTRACT This study aimed to define areas suitable, and the irrigation water requirement for, cultivation of Tanzania guineagrass in the state of Ceará, Brazil. Tanzania guineagrass yield was estimated by a mathematical model, which considers the crop actual evapotranspiration, resulting from the crop climatological water balance. The water requirement throughout the year was estimated for soils with a water holding capacity of 20 (shallow soils), 40 (sandy soils), 60 (soils with medium texture) and 100 mm (clay soils). The relative frequency of occurrence of monthly productions greater than 2,750 kg DM ha-1 month-1 was obtained for different areas in Ceará, representative of most of the state's economic mesoregions. Tanzania guineagrass annual yields in the state of Ceará were between 20,000-30,000 kg DM ha-1 year-1. During the rainy season, the productive potential varies with the economic mesoregion, which presents different climatic conditions. The state of Ceará is only suitable for the rainfed production of Tanzania guineagrass for 4 months each year, predominantly from February to May, while weather conditions do not favor the development of this grass in the remaining months.

scholarly journals Estimating evaporation with thermal UAV data and two-source energy balance models

2016 ◽  
Vol 20 (2) ◽  
pp. 697-713 ◽  
Author(s):  
H. Hoffmann ◽  
H. Nieto ◽  
R. Jensen ◽  
R. Guzinski ◽  
P. Zarco-Tejada ◽  
...  
Keyword(s):  
Heat Flux ◽  
Energy Balance ◽  
High Resolution ◽  
Eddy Covariance ◽  
Land Surface ◽  
Weather Conditions ◽  
Heat Fluxes ◽  
Thermal Camera ◽  
Model Input ◽  
Eddy Covariance Measurements

Abstract. Estimating evaporation is important when managing water resources and cultivating crops. Evaporation can be estimated using land surface heat flux models and remotely sensed land surface temperatures (LST), which have recently become obtainable in very high resolution using lightweight thermal cameras and Unmanned Aerial Vehicles (UAVs). In this study a thermal camera was mounted on a UAV and applied into the field of heat fluxes and hydrology by concatenating thermal images into mosaics of LST and using these as input for the two-source energy balance (TSEB) modelling scheme. Thermal images are obtained with a fixed-wing UAV overflying a barley field in western Denmark during the growing season of 2014 and a spatial resolution of 0.20 m is obtained in final LST mosaics. Two models are used: the original TSEB model (TSEB-PT) and a dual-temperature-difference (DTD) model. In contrast to the TSEB-PT model, the DTD model accounts for the bias that is likely present in remotely sensed LST. TSEB-PT and DTD have already been well tested, however only during sunny weather conditions and with satellite images serving as thermal input. The aim of this study is to assess whether a lightweight thermal camera mounted on a UAV is able to provide data of sufficient quality to constitute as model input and thus attain accurate and high spatial and temporal resolution surface energy heat fluxes, with special focus on latent heat flux (evaporation). Furthermore, this study evaluates the performance of the TSEB scheme during cloudy and overcast weather conditions, which is feasible due to the low data retrieval altitude (due to low UAV flying altitude) compared to satellite thermal data that are only available during clear-sky conditions. TSEB-PT and DTD fluxes are compared and validated against eddy covariance measurements and the comparison shows that both TSEB-PT and DTD simulations are in good agreement with eddy covariance measurements, with DTD obtaining the best results. The DTD model provides results comparable to studies estimating evaporation with similar experimental setups, but with LST retrieved from satellites instead of a UAV. Further, systematic irrigation patterns on the barley field provide confidence in the veracity of the spatially distributed evaporation revealed by model output maps. Lastly, this study outlines and discusses the thermal UAV image processing that results in mosaics suited for model input. This study shows that the UAV platform and the lightweight thermal camera provide high spatial and temporal resolution data valid for model input and for other potential applications requiring high-resolution and consistent LST.

scholarly journals CREATING MULTI-TEMPORAL MAPS OF URBAN ENVIRONMENTS FOR IMPROVED LOCALIZATION OF AUTONOMOUS VEHICLES

2020 ◽  
Vol XLIII-B2-2020 ◽  
pp. 317-323
Author(s):  
J. Schachtschneider ◽  
C. Brenner
Keyword(s):  
Urban Areas ◽  
Autonomous Vehicles ◽  
Weather Conditions ◽  
Point Clouds ◽  
Urban Environments ◽  
Seasonal Effects ◽  
Data Set ◽  
Multi Temporal ◽  
One Year

Abstract. The development of automated and autonomous vehicles requires highly accurate long-term maps of the environment. Urban areas contain a large number of dynamic objects which change over time. Since a permanent observation of the environment is impossible and there will always be a first time visit of an unknown or changed area, a map of an urban environment needs to model such dynamics.In this work, we use LiDAR point clouds from a large long term measurement campaign to investigate temporal changes. The data set was recorded along a 20 km route in Hannover, Germany with a Mobile Mapping System over a period of one year in bi-weekly measurements. The data set covers a variety of different urban objects and areas, weather conditions and seasons. Based on this data set, we show how scene and seasonal effects influence the measurement likelihood, and that multi-temporal maps lead to the best positioning results.

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