Competition for program-targeted financing for scientific, scientific and technical programs for 2021-2023 (Ministry of Agriculture of the Republic of Kazakhstan) (Smart Agriculture)

A strategically important state task, for which a program has been developed (priority direction): Sustainable development of the agro-industrial complex and the safety of agricultural products. Specialized scientific direction: Smart Agriculture.

Performers of the program: The consortium consisting: NCJSC "D. Serikbayev East Kazakhstan Technical University", LLP "Experimental farm of oilseeds" (Solnechnoye village), Peasant farm "Ernar" (Devyatka village).

The place of implementation of the program: East Kazakhstan region, Ust-Kamenogorsk, Glubokovsky district, Solnechnoye village, Borodulikhinsky district, Devyatka village.

Estimated start and end date of the program: 01.03.2021 - 31.12.2023. Duration - 34 months.

Project Supervisor:
Leading Researcher Worker
Center of Excellence Veritas D. Serikbayev EKTU, PhD
Sadenova Marzhan Anuarbekovna

Project summary

The purpose of the program is implement the concept of "smart" agriculture, including high-tech types of crop and livestock products, including on the basis of new technical solutions. The program is aimed at intensifying the use of IT-technologies in agrotechnological processes and improving the efficiency of agricultural production to ensure the country's food security. The main approaches consist in the use of remote sensing data and remote methods to identify the main types of agricultural crops (AC), yield forecasting and classification of the content of macronutrients in the soil (nitrogen, phosphorus, potassium, humus). Remote sensing methods are widely used in the agro-industrial complex of many countries in the world (USA, Canada, EU countries, India, Japan, etc.).

The main approach in the research is develop a methodology for classifying the level of macronutrients (nitrogen, phosphorus, potassium, humus) in the soil with high accuracy based on remote sensing data and remote methods, testing at experimental sites in different soil and climatic zones of Eastern Kazakhstan with the development of a module with OpenAPI. An integral part of the research is the specialized processing of space materials, including thermal channels, radar survey materials using the latest technologies on the territory of experimental farms, as well as the development of an operational land survey module and the use of UAV data to increase the accuracy and resolution of space remote sensing information. The direct expected result is the development of a mobile application for the determination of macronutrients in real time based on the Google Earth Engine system.

The purpose of the program:

The purpose of the program is to implement the concept of "smart" agriculture, including high-tech types of crop and livestock products, including on the basis of new technical solutions. The program is aimed at intensifying the use of IT-technologies in agrotechnological processes and increasing the efficiency of agricultural production to ensure the country's food security.

Program objectives

The main task: To develop scientific and methodological approaches for the identification of the main types of agricultural crops (AC), yield forecasting and classification of the content of macronutrients in the soil (nitrogen, phosphorus, potassium, humus) according to remote sensing of the Earth (remote sensing) and remote methods. To achieve the goal of the program, it is necessary to review and analyze domestic and foreign scientific, methodological and patent literature, including the experience of the USA, Ukraine, Russia and other countries to form a strategy for developing new approaches to identifying crops from satellite images, forecasting yields and determining the content of macronutrients in the soil. At the next stage, it is planned to analyze the technical characteristics of the space survey materials used in different countries and in Kazakhstan. Experimental plots of farms in the East Kazakhstan region, located in two different soil-climatic zones (Devyatka and Solnechnoe villages), were identified as the objects of field agrochemical soil surveys. According to remote sensing and remote methods, a method for classifying the level of macronutrients (nitrogen, phosphorus, potassium, humus) in the soil with high accuracy (at least 75-80%) will be developed and tested in 2 different soil-climatic zones, suitable for adaptation and subsequent replication throughout Kazakhstan. A successful example of the use of Earth observation data in agriculture can be attributed to the project "Data for the analysis of global food security" (GFSAD30) to obtain consistent and objective estimates of the volume of global food production. ESA's "Sentinel-2 Satellite in the Service of Agriculture" projects are an open system of information sources for processing images coming from the Sentinel-2 and Landsat-8 satellites, which provides numerous products, especially during the growing season for monitoring.

Foreign partner scientists

Evgeny Levin - PhD, Associate Professor of Michigan Technological University, USA. Research interests: geomatics, geospatial big data, cartography, photogrammetry, remote sensing. Advisory Board Member of the Research Institute of Earth, Planetary and Space Sciences and the Great Lakes Sciences Center. Since 2020 - Deputy editor of the journal "Geodesy and Land Informatics" (SALIS), since 2007 - member of the editorial board of the journal "GPS Solutions", Springer-Verlag, Heidelberg, Germany, since 2007 - member of the editorial board of the journal SPIE Journal of Applied Remote Sensing. The Hirsch index - 51 based on SCOPUS

Khrapov Sergey Sergeevich - Ph.D., Associate Professor of Volgograd State University (Volgograd, Russia). Research interests: geoinformation technologies, 3D technologies: modeling, computer science and programming in high-level languages. The Hirsch index is 13. The role in the project is to develop a model for predicting the yield of (grain, leguminous, oilseed, fodder) crops based on remote sensing data and remote methods

Certificate of accreditation of program executors	LLP "Experimental farm of oilseeds"