REENGINEERING OF BUSINESS PROCESSES  OF AGROINDUSTRIAL  ENTERPRISES IN CONDITIONS  OF THROUGH DIGITAL TRANSFORMATION

The article describes the high dynamism and uncertainty of the external and internal envi-ronment, which actualize the implementation of innovative technologies in the management of business processes in the agro-industrial complex (AIC). Attention is focused on the strategic nature of the transformation of agricultural production within the digital ecosystem. The perspective technologies of collection and processing of remote sensing data obtained from various satellite sensors, unmanned vehicles, weather stations; geographic information systems; global positioning systems are considered. The fundamental elements of reengineering of business processes in the context of digital transformation, the creation of control systems to control the development of agricultural crops using streaming processing of remote sensing data are considered. The factors of restraining and catalyzing production processes in AIC are substantiated, the features of the elements of the organization of the digital spatial environment are revealed, which largely determine the transition to a unified information support system for an agro-industrial enterprise. A structural and functional model of reengineering of business processes is proposed, aimed at ensuring sustainability in making managerial decisions. As part of the reengineering process, it is planned to create an information environment for an agricultural enterprise, consisting of interconnected procedures for merging information of its component functional systems: an automated monitoring system, a system for automated recognition of the specifics of the state of plant surface elements and an automated analytical decision support system for selecting agrotechnological techniques. Reengineering of business processes according to the proposed model will reduce risks in terms of compliance with time factors, increase production volumes and profitability of an agricultural enterprise due to the transition to digital technologies for automated collection and processing of big data, the ability to make decisions based on automated analytical systems and the ability to store in the knowledge base the generated chains of agro-technological operations for the needs of future periods

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An Efficient Decision Support System for Flood Inundation Management Using Intermittent Remote-Sensing Data

Remote Sensing ◽

10.3390/rs13142818 ◽

2021 ◽

Vol 13 (14) ◽

pp. 2818

Author(s):

Hai Sun ◽

Xiaoyi Dai ◽

Wenchi Shou ◽

Jun Wang ◽

Xuejing Ruan

Keyword(s):

Remote Sensing ◽

Decision Support ◽

Decision Support System ◽

Support System ◽

Water Body ◽

Remote Sensing Data ◽

Flood Inundation ◽

Remote Sensing Images ◽

Sensing Data ◽

Tracking Model

Timely acquisition of spatial flood distribution is an essential basis for flood-disaster monitoring and management. Remote-sensing data have been widely used in water-body surveys. However, due to the cloudy weather and complex geomorphic environment, the inability to receive remote-sensing images throughout the day has resulted in some data being missing and unable to provide dynamic and continuous flood inundation process data. To fully and effectively use remote-sensing data, we developed a new decision support system for integrated flood inundation management based on limited and intermittent remote-sensing data. Firstly, we established a new multi-scale water-extraction convolutional neural network named DEU-Net to extract water from remote-sensing images automatically. A specific datasets training method was created for typical region types to separate the water body from the confusing surface features more accurately. Secondly, we built a waterfront contour active tracking model to implicitly describe the flood movement interface. In this way, the flooding process was converted into the numerical solution of the partial differential equation of the boundary function. Space upwind difference format and the time Euler difference format were used to perform the numerical solution. Finally, we established seven indicators that considered regional characteristics and flood-inundation attributes to evaluate flood-disaster losses. The cloud model using the entropy weight method was introduced to account for uncertainties in various parameters. In the end, a decision support system realizing the flood losses risk visualization was developed by using the ArcGIS application programming interface (API). To verify the effectiveness of the model constructed in this paper, we conducted numerical experiments on the model's performance through comparative experiments based on a laboratory scale and actual scale, respectively. The results were as follows: (1) The DEU-Net method had a better capability to accurately extract various water bodies, such as urban water bodies, open-air ponds, plateau lakes etc., than the other comparison methods. (2) The simulation results of the active tracking model had good temporal and spatial consistency with the image extraction results and actual statistical data compared with the synthetic observation data. (3) The application results showed that the system has high computational efficiency and noticeable visualization effects. The research results may provide a scientific basis for the emergency-response decision-making of flood disasters, especially in data-sparse regions.

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Learning-Based Algal Bloom Event Recognition for Oceanographic Decision Support System Using Remote Sensing Data

Remote Sensing ◽

10.3390/rs71013564 ◽

2015 ◽

Vol 7 (10) ◽

pp. 13564-13585 ◽

Cited By ~ 11

Author(s):

Weilong Song ◽

John Dolan ◽

Danelle Cline ◽

Guangming Xiong

Keyword(s):

Remote Sensing ◽

Decision Support ◽

Decision Support System ◽

Support System ◽

Algal Bloom ◽

Remote Sensing Data ◽

Event Recognition ◽

Sensing Data

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Possibility of determining the nitrogen content in winter wheat plants during the earing phase using remote sensing data

Proceedings of the International conference “InterCarto/InterGIS” ◽

10.35595/2414-9179-2020-3-26-199-209 ◽

2020 ◽

Vol 26 (3) ◽

pp. 199-209

Author(s):

Fedor Eroshenko ◽

Irina Storchak ◽

Irina Engovatova ◽

Andrey Likhovid

Keyword(s):

Remote Sensing ◽

Winter Wheat ◽

Nitrogen Content ◽

Growth And Development ◽

Vegetation Index ◽

Local Level ◽

Remote Sensing Data ◽

Reflection Index ◽

Sensing Data ◽

Agricultural Enterprise

The study examined the possibility of using remote sensing Data (RED, NIR, NDVI) for monitoring winter wheat crops in production conditions for nitrogen content in plants. This work is divided into two stages: 1) analysis of the correlation between NDVI indicators and nitrogen content on production crops of the North-Caucasian FNAC; 2) comparative analysis of the correlation between nitrogen content and remote sensing data in the conditions of the “Rodina” agricultural enterprise in the Shpakovsky district of the Stavropol territory. Selection of plant samples (sheaf material) was carried out according to the generally accepted method. Repeatability — 4-fold. The chemical composition of plant organs was determined using the method of V.T. Kurkaev and co-authors, and the chlorophyll content was determined by Y.I. Milaeva and N.P. Primak. We used the earth remote sensing data provided by the Terra satellite and obtained by the Modis scanning Spectroradiometer. At the first stage, the relationships between the nitrogen content in winter wheat plants and the values of the normalized difference vegetation index (NDVI) were studied. At the early stages of growth and development of winter wheat plants, high correlation coefficients between these indicators were obtained. Thus, the correlation coefficient on average for the fields in 2012 was equal to -0.89, and in 2013 and 2014 — -0.82. In later phases of growth and development of winter wheat plants, this relationship was not observed. At the second stage, it was found that it is advisable to use the red reflection index to assess the nitrogen content at the local level (a separate agricultural enterprise) in the earing phase. In this case, there is a stable inverse correlation — the average for three years of research was -0.71. When other remote sensing indicators (NDVI and NIR) are used in the analysis, the links are either absent or less apparent.

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