AGRIS - 

2023

Shigimaga, V.A. | Fajzullin, R.A. | Osokina, A.S.

There is given the substantiation of the main parameters of an unmanned automated herd monitoring system on a pasture by automating the measurement of current physiological indicators of animals to improve the efficiency and rate of supervision over them in free grazing conditions. The studies were based on the theory of radio communication about the propagation of radio waves, and also there was used a graphoanalytic method for calculating the parameters of component elements as parts of an unmanned system for automated monitoring of animals on pasture. The DJI Phantom 4 Advanced quadcopter was used to measure the flight time of an unmanned aerial vehicle (UAV) over the real pasture. An experimental field with an area of 200 hectares, located in the Kharkov region, was used as a pasture. The research was carried out in summer of 2021. According to the results of well-known studies of the processes, methods, and facilities of monitoring the physiological state of animals on pasture, it has been established that the advanced facilities including air-launch vehicles should be used for remote monitoring. Among them are helicopter-type UAVs, as well as elements of the radio telemetry system (RTM), individual tags and sensors of the physiological parameters of the animal. At the same time, an unmanned automated monitoring system in combination with RTM elements is able to provide the transmission of physiological data from any sensors located on the animal's body or inside it. The data are transmitted to the main reading sites for processing on a PC and giving recommendations to specialists (veterinarians, zootechnicians, etc.). The power of the relay equipment on board the UAV is calculated to be at least 60 mW and the communication range with the animal transponder of not more than 800 m. The main parameters of the UAV flight over a pasture of 200 ha have been experimentally established: the height is 20 m, the speed is 8.7 km/h, the time is 27.5 min, and the work load is 350 g. | Приведено обоснование основных параметров беспилотной системы автоматизированного мониторинга стада на пастбище за счет автоматизации измерения текущих физиологических показателей животных для повышения эффективности и оперативности надзора за ними в условиях свободного выпаса. Исследования основывались на теории радиосвязи о распространении радиоволн, а также применен графоаналитический метод для расчета параметров элементов в составе беспилотной системы автоматизированного мониторинга животных на пастбище. Для измерения времени полета беспилотного летательного аппарата (БПЛА) над реальным пастбищем был применен квадрокоптер типа DJI Phantom 4 Advanced. В качестве пастбища использовали опытное поле площадью 200 га, находящееся в Харьковской области. Исследования выполнены летом 2021 г. На основе известных исследований процессов, способов и технических средств мониторинга физиологического состояния животных на пастбище установлено, что для дистанционного мониторинга целесообразно применять новейшие технические средства, включая аппараты воздушного базирования. Среди них БПЛА вертолетного типа, а также элементы системы радиотелеметрии (РТМ), индивидуальные бирки и датчики физиологических параметров животного. Беспилотная система автоматизированного мониторинга в сочетании с элементами РТМ сможет обеспечить передачу физиологических данных с любых датчиков, находящихся на теле животного или внутри него. Данные передаются на основной пункт приема информации с целью обработки на компьютере и выдачи рекомендаций специалистам (ветеринарам, зоотехникам и т.п.). Рассчитаны мощность аппаратуры ретрансляции на борту БПЛА (не менее 60 мВт) и дальность связи с транспондером животного (не более 0,8 км). Экспериментально установлены основные параметры полета БПЛА над пастбищем площадью 200 га: высота 20 м, скорость 8,7 км/ч, время 27,5 мин, полезная нагрузка 350 г.

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Central Scientific Agricultural Library - Russian Federation

2023

Vladimir M. Dubyanskiy | Daria A. Prislegina | Alexander E. Platonov

Introduction. The study focuses on methods providing mathematical substantiation of discrepancies between actual incidence rates of Astrakhan rickettsial fever (ARF) and Crimean-Congo hemorrhagic fever (CCHF) and predicted rates due to the indirect impact of weather conditions during the current epidemic season. The purpose of the study was to develop explanatory models for ARF and CCHF incidence using satellite monitoring (remote sensing) data and to present the results of their practical evaluation in the Stavropol Territory and Astrakhan Region. Materials and methods. The materials included climate data provided by the Space Research Institute of the Russian Academy of Sciences as well as epidemiological data on CCHF and ARF incidence from 2005 to 2021. The explanatory models incorporated the Bayes theorem and Wald sequential analysis. All the calculations were completed using the Microsoft Excel 2010-based program developed by the authors. Results. It has been found that the greatest indirect effect on development of the CCHF epidemiological situation is produced by the normalized difference vegetation index and relative air humidity in June-July in the Stavropol Territory and by the maximum, minimum and average air temperature in October as well as the minimum air temperature in July in the Astrakhan Region. ARF incidence rates depend on the indirect effect of the annual average and average annual maximum temperature, maximum temperature and the normalized difference vegetation index in April-July. The match between explanatory model-based results and prediction model-based results ranged within 46.2-100%. Discussion. In addition to projecting incidence rates, which could be reached with the observed values of climatic factors in the current year, the explanatory models can be used for indirect verification of prediction models and for identification of factors causing differences in results. Conclusion. The practical evaluation of explanatory models confirms the prospects and benefits of the study that should be continued, involving other regions highly endemic for tick-borne infections.

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Directory of Open Access Journals - Sweden

2023

Karimov, B. | Karimova, G. | Amankulova, N.

Computer processing and analysis of satellite data is an urgent task of the science of remote sensing of the earth. Such processing can range from adjusting the contrast and brightness of the images of an amateur photographer to a group of scientists using neural network classification to determine the types of minerals in a hyperspectral satellite image. This article implements a method of satellite data fusion, which improves the digital image interpretation and image quality for further analysis. For fusion, a multispectral image with a resolution of 30 m Landsat 5 with 6 channels was taken, with three more significant and informative in their composition were used, as well as a panchromatic (monochrome) image with a resolution of 15 m. To evaluate the resolution of the images and the resulting images before and after the image fusion algorithm, image slices along a straight line and intersecting buildings, green mass, roads and industrial areas presented. For testing, test territories taken from Google Earth and the field work results.

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Publishing center Science and Practice - Russian Federation

2023

Gulyanov, Yu.A. | Polyakov, D.G.

An express evaluation of the development level of field agrocenoses phytomass and agrophysical soil indicators is necessary for rapid technological optimization of the growth conditions for arable crops in order to realize their productive potential, as well as to preserve and reproduce the soil fertility. The research was carried out in 2020–2022 at the experimental field of the Orenburg State Agrarian University. The research objects were the normalized difference vegetation index (NDVI) of agrocenoses of winter and spring wheat (Triticum aestivum L.) or oats (Avena sativa L.) cultivated under minimum tillage technology, and soil agrophysical parameters (hardness, moisture content, density) at the experimental site. NDVI was determined using Earth's remote sensing (ERS) and a hand-held sensor Green Seekеr Handheld Crop Sensor, Model HCS–100. The penetrologger Eijkelkamp 06.15.SA was used to determine the hardness of the soil; the density of the arable soil layer was determined by N.A. Kachinsky drilling method; soil moisture was determined by the thermostatic-weight method. Digital material was processed using the conventional methods of statistical analysis. There has been established the acceptability of modern instrumental methods using portable devices for determining phytometric parameters of crops and agrophysical properties of soils, their ease of use and efficiency. The high convergence between the results of NDVI determination using satellite images and those obtained by ground scanning was confirmed. The dependence of phytometric parameters of oats agrocenosis (according to NDVI) on agrophysical indicators of the soil was revealed. The most pronounced feedback (r = –0.52) was observed between NDVI and the density of the upper soil horizon (5–10 cm), as well as between NDVI and the hardness of the soil layers: 5-10 cm (r = –0.43) and 25–30 cm (r = –0.52) most probably associated with the transition to direct sowing in modern agricultural technologies and the legacy of deep tillage in retrospect, which led to a compacted soil layer in these horizons. | Экспресс-оценка уровня развития фитомассы полевых агроценозов и агрофизических показателей почвы необходима для оперативной технологической оптимизации условий роста полевых культур, реализации их урожайного потенциала, сохранения и воспроизводства плодородия почв. Исследования проводили в 2020–2022 гг. в производственных посевах учебно-опытного поля Оренбургского государственного аграрного университета. Объектом исследований выступали нормализованный разностный вегетационный индекс (NDVI) агроценозов озимой и яровой пшеницы (Triticum aestivum L.), овса посевного (Avena sativa L.), возделываемых по минимальной технологии, и агрофизические параметры (твёрдость, влажность, плотность) почвы экспериментального участка. Определение NDVI проводили по данным дистанционного зондирования Земли (ДЗЗ) и ручным сенсором Green Seeker Handheld Crop Sensor, Model HCS - 100. Для определения твёрдости почвы использовали пенетрологгер Eijkelkamp 06.15.SA, плотность пахотного слоя почвы определяли буровым методом по Н. А. Качинскому, влажность почвы – термостатно-весовым методом. При обработке цифрового материала применяли общепринятые методы статистического анализа. Установлена приемлемость современных инструментальных методов определения фитометрических параметров посевов и агрофизических свойств почвы с помощью портативных устройств, их простота и оперативность. Подтверждена высокая сходимость результатов определения NDVI по спутниковым снимкам и полученным в процессе наземного сканирования. Выявлена зависимость фитометрических параметров агроценоза овса (по NDVI) от агрофизических параметров почвы. Отмечена наиболее выраженная обратная связь (r = –0,52) NDVI с плотностью верхнего (5–10 см) почвенного горизонта и твёрдостью слоёв почвы 5–10 см (r = –0,43) и 25–30 см (r = –0,52), наиболее вероятно связанная с переходом на прямой посев в современных агротехнологиях и наследием глубоких обработок в ретроспективе, приведших к уплотнению почвы в этих горизонтах.

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Central Scientific Agricultural Library - Russian Federation

2023

Gulyanov, Yu.A.

The steppe regions of Russia are the best developed in agricultural terms. They provide with 57.8% of planted acreage (46975.4 thousand ha on average for 2000-2021), more than 60.0% (60107.8 thousand t) of gross yield of cereal and leguminous crops, which forms the basis of the country's food security. With the increasing anthropogenic degradation of natural agricultural landscapes, there is a need to optimize land use and ecologize agriculture in the region. The object of the study was the data of Earth's remote sensing (ERS) and spatial heterogeneities of phytomass of field crops in agrocenoses of steppe regions of Russia. The data on the planting acreage and outputs of cereals and leguminous crops in these regions for 2000-2021 have been studied. The causes of the occurrence are systematized and the degree of influence of the spatial heterogeneity of phytomass on the realization of the bioclimatic potential of field crops is estimated. The experience of using satellite monitoring data to identify the plant heterogeneity of field agrocenoses is summarized, the results of its reliability in the field are presented and the prospects for using ERS to control the development of field crops are evaluated. The low degree of sustainability of agriculture and the lack of guarantees of food security in the case of decommissioning of developed and unstable agricultural lands have been revealed. In order to stabilize and preserve the production of plant raw materials, increase the yield capacity of field crops on the lands remaining in cultivation, it is necessary to introduce knowledge-intensive agricultural technologies more widely, including those based on ERS data. | Степные регионы России наиболее хорошо освоены в сельскохозяйственном отношении. Они обеспечивают 57,8% посевных площадей (46975,4 тыс. га в среднем за 2000-2021 гг.), более 60,0% (60107,8 тыс. т) валового урожая зерновых и зернобобовых культур и составляющего основу продовольственной безопасности страны. При нарастающей антропогенной деградации природных агроландшафтов возникает потребность в оптимизации использования земель и экологизации земледелия в регионе. Объектом исследования служили данные дистанционного зондирования Земли (ДЗЗ) и пространственная неоднородности фитомассы полевых культур в агроценозах степных регионов России. Изучены сведения о площадях посевов и объемах производства зерновых и зернобобовых культур в этих регионах за 2000-2021 гг. Систематизированы причины возникновения и оценена степень влияния пространственной неоднородности фитомассы на реализацию биоклиматического потенциала полевых культур. Обобщён опыт использования данных спутникового мониторинга для выявления растительной неоднородности полевых агроценозов, представлены результаты его достоверности в полевых условиях и оценены перспективы использования данных ДЗЗ для управления развитием полевых культур. Выявлены низкая степень устойчивости земледелия и отсутствие гарантий продовольственной безопасности в случае выведения из оборота выработанных и неустойчивых с.-х. угодий. С целью стабилизации и сохранения объёмов производства растительного сырья, повышения продуктивности полевых культур на остающихся в обработке землях необходимо шире внедрять наукоемкие агротехнологии, в том числе основанные на данных ДЗЗ.

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Central Scientific Agricultural Library - Russian Federation

2023

Stepanov, A.S. | Dubrovin, K.N.

The research aimed to build models of the vegetation development for soybean crops cultivated in the Far East based on data from remote sensing of the Earth from space. To achieve this goal, we considered optical and radar images obtained from the Sentinel-1, Sentinel-2 satellites, as well as the MODIS spectroradiometer for arable lands of the Khabarovsk Territory (Khabarovsk District) and the Amur Region (Arkharinsk, Ivanovsky and Oktyabrsky Districts) in 2021. For individual fields with soybean, pixel by pixel, time series of Normalized Difference Vegetation Index (NDVI) and Dual polarimetric Radar Vegetation Index (DpRVI) were formed (from April to October 2021), and their characteristics were assessed depending on the fields' geographic location. Soybean NDVI seasonal time series fitting study was performed using Fourier transform, double logistic function, Gaussian function, double Gaussian function and double sine function. The average approximation error ranged from 7 to 15%, depending on the type of function. The average values of extremes and the average statistical day of the onset of the maximum of the approximated time series were calculated. The seasonal patterns of NDVI and DpRVI for soybeans were similar in different regions of the southern part of the Far East. At the same time, they differ significantly from the seasonal patterns of other agricultural crops in the macroregion. The generated time series can be used to identify, clarify the boundaries of individual fields, control the soybean crop rotation. To approximate the NDVI seasonal variation curves, the best results were obtained by using the Fourier transform and the double logistic function, the error was 6.99 and 7.61%, respectively. | Исследования проводили с целью построения моделей вегетационного развития сои на Дальнем Востоке на основе данных дистанционного зондирования Земли из космоса. Для достижения поставленной цели рассматривали оптические и радарные изображения, полученные со спутников Sentinel-1, Sentinel-2, а также спектрорадиометра MODIS для пахотных земель Хабаровского края (Хабаровский район) и Амурской области (Архаринский, Ивановский и Октябрьский районы) в 2021 г. Для отдельных полей с соей, попиксельно, были сформированы временные ряды Normalized Difference Vegetation Index (NDVI) и Dual polarimetric Radar Vegetation Index (DpRVI) (в период с апреля по октябрь 2021 г.), а также проведена оценка их характеристик в зависимости от географического расположения полей. Исследование по аппроксимации временных рядов сезонного хода NDVI для сои проводили с использованием преобразования Фурье, двойной логистической функции, функции Гаусса, двойной функции Гаусса и двойной синусоидальной функции. Средняя ошибка аппроксимации находилась в диапазоне от 7 до 15% в зависимости от типа функции. Рассчитывали средние значения экстремумов и среднестатистический день наступления максимума аппроксимированных временных рядов. Графики сезонного хода NDVI и DpRVI для сои были схожими в разных районах южной части Дальнего Востока. При этом они существенно отличаются от графиков сезонного хода прочих сельхозкультур макрорегиона. Сформированные временные ряды могут быть использованы для идентификации, уточнения границ отдельных полей, контролирования севооборота сои. Для аппроксимации кривых сезонного хода NDVI наилучшие результаты обеспечило использование преобразования Фурье и двойной логистической функции – ошибка составила соответственно 6,99 и 7,61%.

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Central Scientific Agricultural Library - Russian Federation

2022

V. M. Dubyanskiy | D. A. Prislegina | A. E. Platonov

Introduction. With the epidemiological situation for Crimean-Congo hemorrhagic fever (CCHF) remaining tense in many countries worldwide, special attention should be focused on development and improvement of risk-based epidemiological prediction methods.The aim of the study was to build a prediction model for CCHF incidence dynamics (based on the Stavropol Territory) using satellite monitoring (remote sensing) data.Materials and methods. We analyzed the climate data obtained from the Space Research Institute of the Russian Academy of Sciences as well as the data of public statistics reports on CCHF incidence from 2005 to 2021. The prediction model incorporated the Bayes theorem and Wald sequential analysis. The information content of the factors was assessed using the Kullback method.Results. Predictions for each of 26 districts were made stepwise (compared to threshold levels) to predict whether there will be at least one case of CCHF, whether the relative incidence per 100,000 population will exceed the median level (0.9 cases) or the average rate (3.5 cases) or the third quartile rate (4.7 cases). The highest values of information coefficients were obtained for soil temperature and moisture content (at depths of 10 and 40 cm), normalized relative vegetation index, relative humidity, maximum and average air temperature, relative air humidity. During the testing of the model in 2021, false-negative (erroneous) prediction was made for 2 districts.Discussion. The model proved to be most efficient in prediction of occurrence or absence of cases. More accurate quantitative prediction may be difficult due to subjective factors (including misdiagnosing CCHF cases without hemorrhagic manifestations and administering treatment for other conditions with similar symptoms).Conclusion. The tests of the model demonstrate its potential. The practical application of the prediction will make healthcare workers more alert when screening and detecting CCHF cases.

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Directory of Open Access Journals - Sweden

2022

Yaroslava V. Pavlyuk | Olga M. Sablina | Stanislava B. Smirnova | Kristina A. Gladkaya

Significant areas of the country’s territories are suffering erosion processes due to the increased anthropogenic load. Belgorod region territories are included. This fact makes the reason to the present study for the spatial and temporal patterns analysis of the linear erosion development. The analysis is needed to access the potential territory resistance to the erosion processes within the framework of the basin concept in the South of the Central Russian Upland. The spatial distribution analysis of erosional forms in the study area makes it possible to identify patterns of distribution and assess the possible development of erosion processes. Geoinformation modeling is used to maintain the study in conjunction with natural methods for studying the linear development of the erosion network.During the research, the methods of thematic processing of space images, geospatial analysis, GIS modeling, comparative geographic, the method of analogies, landscape planning, as well as methods of statistical and mathematical data processing were used.The study has revealed the fact, that territories with a high erosion density and a low share of the river density is characterized with the high ravine network density with a low density of ravines. Though, territories with low erosion density and a more developed river network are characterized by a decrease in the ravine network density; territories with a high erosion density are characterized with a weak development of upper link and the predominance of lower link of watercourses in the river network structure. Then, most basins with low ravine network density are significantly dissected in the upper and middle links of the river network. Establishing the regularity of the erosion forms distribution made it possible to assess the territory resistance of the erosion processes, as well as predict and analyze the need for anti-erosion measures.

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Directory of Open Access Journals - Sweden

2021

М М. Kharytonov | V. T. Pashova | S. M. Lemyshko | G. P. Yevgrashkina | O. V. Titarenko

The discharge of highly mineralized mine waters of the Western Donbass negativelyaffected the Samara Bay area, which is of great fishery importance. Mine water storage pondswere built in deep erosional cuts in the territories of mine fields in the gullies of Kosminnaya,Taranova, Glinyana and Svidovok. The screening of the bottoms was not performed in any pond.The soils that make up the bottom are not aquicludes, which contributes to intensive filtration ofmine waters and pollution of aquifers. Artificial ponds built without waterproofing of bottoms,rock dumps, mine water discharge routes and other sources of pollution are actively involved inthe zone of influence of mine drainage and worsen the quality of drinking water, the reserves ofwhich in the Western Donbass are limited. There is flooding and flooding by groundwater, as wellas increasing (compared to the period before the violations) areas of land temporarily floodedduring floods on rivers in the valleys of the rivers Samara, Velyka Ternivka and such large beamsas Svydovok, Taranova, Kosminna due to subsidence of the earth’s surface. Differences in theconditions of formation of mine waters determine the nature and degree of their impact on theenvironment. The main difficulties making complicated accurate comparison between calculatedand field data in Samara river floodplain are following: a) changes in parameters of mining andpumping rates of water used for local needs; b) hydrological changes including formation ofnew channels, bed deformation; c) transformation of the monitoring network; d) increasing leakagethrough the clayey bottom of the ponds. The main objective is to provide a comprehensivegeo-ecological assessment of the state of the Samara river floodplain in the area of coal miningin the Western Donbass. Multispectral imagery of Sentinel-2 satellite system was used for remoteassessment within the study area. Geomorphologic assessment of the studied area was performedusing Sentinel-1 satellite radar interferometry. Flooding of the territory is observed due to minedrainage and subsidence of the earth’s surface. The risk of salinization of soils under the conditionsof water use from the beam “Glynyana” and from the Samara river is estimated as insignificant,from the beam “Kosminna” ‒ average. The SAR value of mine waters in “Taranova” and“Svydovok” beams corresponds to a high level of salinity. Samara river waters belong to the 3rdclass and are characterized as highly mineralized, sometimes unsuitable for irrigation. Intensivenatural overgrowth of the mine dumps that have passed the stage of mining reclamation has beenrecorded near the “Heroyiv of Space”, “Pavlogradska” and “Samarska” mines. The rate of selfgrowthof the land cover around the mine “Ternovska” and “Blagodatna” is estimated as average.The self-healing levels of the “Ternovska” and “Blagodatna West Donbasska” mines are ratedfrom low to high. The greatest risk of salinization of soils under conditions of use of water forirrigation is possible at a fence from artificial ponds located in Taranov’s and Svidovok’s beams.The conturs of “Verbsky”, “Ternivsky”, “Bogdanovsky” and “Boguslavskiy” piscicultural pondscan be corrected with map of remote sensing of Samara river floodplain geomorphology. The dataobtained can be useful during development of econetwork of promising ecological corridors inthe floodplain part of the Samara River as well..

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Directory of Open Access Journals - Sweden

2021

P. V. Lykhovyd

Spatial crop monitoring using vegetation indices is one of the most promising technologies for crop mapping and remote phenological observations. The aim of the study was to determine the patterns of seasonal dynamics of the spatial normalized difference vegetation index for the main crops grown in the south of Ukraine and to connect it to their phenology. Remote sensing data provided by the OneSoil AI platform, which uses Sentinel-1 and Sentinel-2 imagery as a basis, was used to derive the monthly index values for the 2016–2021 growing season for nine selected crops grown in the experimental fields at the NAAS Institute of Irrigated Agriculture, Kherson, Ukraine. The fallow field was also included in the study to determine the cutoff values of the vegetation index, which are not representative of any healthy vegetation. It was determined that each crop has its unique pattern of the dynamics of the vegetation index, except for winter wheat and winter barley, which demonstrated quite similar models. The peak values of the vegetation index were observed in May for winter crops (wheat, barley, rapeseed) and early-spring crops (chickpea, peas), while the late-spring crops (grain corn, grain sorghum, soybeans, sunflower) reached the peak values in July. It is possible to suggest that the highest demand for mineral nutrition and watering will fall in the mentioned time periods of late spring and midsummer. Phenological monitoring revealed that the highest values of the spatial normalized difference vegetation index were observed in the following stages of crop growth, namely: winter wheat, winter barley – stem elongation; winter rapeseed – flowering; chickpea – branching; peas – budding and flowering; sunflower – stem growth; soybeans - pod formation; grain sorghum – panicle ejection and flowering; grain corn – panicle ejection and flowering. The results provide novel information for further implementation in the mathematical models for automation of crops recognition, mapping, and phenological observations based on the remote sensing data. Further scientific research in this direction will be aimed at increasing the spectrum of crops studied and a detailed investigation of the relationship between the value of the normalized difference vegetation index and their phenology.

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