Organization of retrospective monitoring of the soil cover in Azov district of Rostov oblast

Bryzzhev, A. V.; Рухович, Д. И.; Королева, П. В.; Калинина, Н. В.; Vil’chevskaya, E. V.; Dolinina, E. A.; Rukhovich, S. V.

doi:10.1134/s1064229315100014

Cited by 26 publications

(15 citation statements)

References 9 publications

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“…Собирая больше информации, храня ее как можно дольше, можно своевременно прогнозировать возникновение различных проблем и вовремя их устранять. Технический и технологический прогресс позволяет осуществлять информационное сопровождение сельскохозяйственного производства в целом, добиваясь многокритериального оптимума [7].…”

Section: автоматизация и информатикаunclassified

Automation facilities for agricultural machinery control

Измайлов¹,

Khoroshenkov²,

Kolesnikova³

et al. 2017

S-h. maš. tehnol.

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Исследована возможность применения средств автоматизации для управления сельхозтехникой. Предложены решения по созданию централизованной унифицированной автоматизированной информационной системы управления мобильными агрегатами. Отмечено, что исходя из современных требований эта система должна быть открытой, интегрированной в общую схему управления сельскохозяйственным предприятием. Она должна реализовать идею применения стандартных аппаратных, программных и коммуникационных средств в задачах контроля и управления. Поэтому схема должна строиться на унифицированных модулях и тех стандартах, что освоены в России. Показано, что, базируясь на блочно-модульном построении, комплексная многомерная унифицированная автоматизированная система управления различными объектами сельскохозяйственного назначения должна соответствовать следующим принципам: высокая надежность, простота обслуживания, низкие издержки при эксплуатации, быстрая окупаемость, связанная с увеличением урожайности, сниженные потери при уборке, послеуборочной обработке и хранении, улучшенные энергетические показатели. Установлено, что управление технологическими процессами в сельхозпроизводстве осуществляется в основном с обратной связью. Пример без обратной связиэто программное управление температурой в хранилище при режиме «охлаждение». Обратная связь в управлении технологическими процессами сельхозпроизводства позволяет оптимально решить проблему рационального распределения функций в человеко-распределенных системах и сформировать интеллектуальные эргономические интерфейсы, согласованные с профессиональными представлениями лиц, принимающих решения. Отрицательная обратная связь, создаваемая устройством управления, позволяет автоматически поддерживать показатель качества технологического процесса на заданном уровне. Количественный анализ производственной ситуации опирается на глубоко формализованную базу вычислительной техники, что способствует выработке оптимального решения. Показано, что применение информационной автоматизированной системы управления увеличивает производительность труда на 40 процентов, уменьшает энергетические затраты на 25 процентов. Повышение качества выполняемых технологических операций позволит увеличить урожайность в 1,2-1,3 раза. Ключевые слова: средства автоматизации, автоматизированный контроль и управление, технологические процессы, сельскохозяйственная продукция.

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Section: автоматизация и информатикаunclassified

Automation facilities for agricultural machinery control

Измайлов¹,

Khoroshenkov²,

Kolesnikova³

et al. 2017

S-h. maš. tehnol.

View full text Add to dashboard Cite

show abstract

“…Degradation can be detected based on the analysis of big satellite data in the manual interpretation mode [14][15][16][17][18]. Retrospective monitoring of land use and soil cover allows to identify areas of formation of degraded lands for a period of 50 years or more [19]. In this case, the accuracy of detection and the accuracy of determination of the boundaries of degraded areas are higher than those during ground-based surveys [18][19][20].…”

Section: Introductionmentioning

confidence: 99%

“…Retrospective monitoring of land use and soil cover allows to identify areas of formation of degraded lands for a period of 50 years or more [19]. In this case, the accuracy of detection and the accuracy of determination of the boundaries of degraded areas are higher than those during ground-based surveys [18][19][20]. Retrospective monitoring allows to exactly determine the boundaries of agricultural fields and to trace their history [21].…”

Section: Introductionmentioning

confidence: 99%

The Use of Deep Machine Learning for the Automated Selection of Remote Sensing Data for the Determination of Areas of Arable Land Degradation Processes Distribution

et al. 2021

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Soil degradation processes are widespread on agricultural land. Ground-based methods for detecting degradation require a lot of labor and time. Remote methods based on the analysis of vegetation indices can significantly reduce the volume of ground surveys. Currently, machine learning methods are increasingly being used to analyze remote sensing data. In this paper, the task is set to apply deep machine learning methods and methods of vegetation indices calculation to automate the detection of areas of soil degradation development on arable land. In the course of the work, a method was developed for determining the location of degraded areas of soil cover on arable fields. The method is based on the use of multi-temporal remote sensing data. The selection of suitable remote sensing data scenes is based on deep machine learning. Deep machine learning was based on an analysis of 1028 scenes of Landsats 4, 5, 7 and 8 on 530 agricultural fields. Landsat data from 1984 to 2019 was analyzed. Dataset was created manually for each pair of “Landsat scene”/“agricultural field number”(for each agricultural field, the suitability of each Landsat scene was assessed). Areas of soil degradation were calculated based on the frequency of occurrence of low NDVI values over 35 years. Low NDVI values were calculated separately for each suitable fragment of the satellite image within the boundaries of each agricultural field. NDVI values of one-third of the field area and lower than the other two-thirds were considered low. During testing, the method gave 12.5% of type I errors (false positive) and 3.8% of type II errors (false negative). Independent verification of the method was carried out on six agricultural fields on an area of 713.3 hectares. Humus content and thickness of the humus horizon were determined in 42 ground-based points. In arable land degradation areas identified by the proposed method, the probability of detecting soil degradation by field methods was 87.5%. The probability of detecting soil degradation by ground-based methods outside the predicted regions was 3.8%. The results indicate that deep machine learning is feasible for remote sensing data selection based on a binary dataset. This eliminates the need for intermediate filtering systems in the selection of satellite imagery (determination of clouds, shadows from clouds, open soil surface, etc.). Direct selection of Landsat scenes suitable for calculations has been made. It allows automating the process of constructing soil degradation maps.

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“…From 1990 to 2000, 10,355 ha (5.35 %) had changed, from 2000 to 2010, 5450 ha (3.5 %) changed. Bryzzhev and Ruhovich (2013) showed that despite the relative homogeneity of the district in climatic, geological, and topographical terms, for 20 years (from 1990 to 2010), more than 8 % of the territory ware affected by land-use change.…”

Section: Identification Of the Causes And Extent Of Land Degradation mentioning

confidence: 99%

“…The time interval for the study was identified by the availability and accuracy of the data used. Multi-temporal maps were made to assess the variability of land use in the area (Bryzzhev and Ruhovich 2013). Figure 18.13 shows the plots where at least once during the analyzed period (1990-2010) the agricultural processing was not performed, was difficult, or did not give the intended outcome (harvest).…”

Section: Identification Of the Causes And Extent Of Land Degradation mentioning

confidence: 99%

The Economics of Land Degradation in Russia

Sorokin

Bryzzhev

Strokov

et al. 2015

Economics of Land Degradation and Improvement – A Global Assessment for Sustainable Development

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The analysis of the costs for action versus inaction in addressing land degradation at national and sub-national (regional and district) levels in Russia showed that the total annual costs of land degradation due to land use and cover change only are about 189 billion USD in 2009 as compared with 2001, i.e. about 23.6 billion USD annually, or about 2 % of Russia's Gross Domestic Product (GDP) in 2010. These land use and land cover changes occurred in the area of 130 million ha. The area of arable lands decreased by 25 % during the period of the economic reforms between 1990 and 2009. The total economic value of ecosystem goods and services is estimated to equal about 3700 billion USD in Russia, exceeding the conventional GDP by 3 times. The costs of action against land degradation are lower than the costs of inaction in Russia by 5-6 times over a 30-year horizon. Almost 92 % of the costs of action are made up of the opportunity costs of action. The methodology of the economics of land degradation can be successfully applied in peculiar socioeconomic conditions of Russia, but the lack of harmonization of methods and indicators brings uncertainty to quantitative assessments.

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Organization of retrospective monitoring of the soil cover in Azov district of Rostov oblast

Cited by 26 publications

References 9 publications

Automation facilities for agricultural machinery control

Automation facilities for agricultural machinery control

The Use of Deep Machine Learning for the Automated Selection of Remote Sensing Data for the Determination of Areas of Arable Land Degradation Processes Distribution

The Economics of Land Degradation in Russia

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