Open AccessUsing the results of remote sensing of land for programmed agricultural production in arid conditions
Author(s) -
Elena Melikhova
Publication year - 2021
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1801/1/012044
Subject(s) - agricultural engineering , environmental science , agriculture , land reclamation , irrigation , agricultural productivity , agrochemical , remote sensing , production (economics) , computer science , geography , engineering , agronomy , macroeconomics , archaeology , economics , biology
The article considers methodological approaches to substantiating the main tasks solved by the developed software package for managing agricultural production in the programmed cultivation of agricultural crops. There are characteristic differences in the reflection spectrum of agricultural plants due to the influence of water vapor absorption. The total reflected radiation in the optical and near-infrared range, recorded by remote sensing, depends on the biological and varietal characteristics of plants, the development of the leaf surface, their growth phases, the chemical composition of the soil, and other factors. The set of vegetation indices NDVI and its modifications is justified. Problems of mathematical modeling of irrigation technologies in arid conditions based on differential equations are revealed. The model takes into account agrochemical features of soils, biological properties of crops, agroclimatic conditions, as well as technological parameters of irrigation regimes. The necessity and expediency of using digital information technologies in agricultural production, including irrigation reclamation, for operational management and control of agrotechnological parameters of crop cultivation are justified. A conceptual and logical database model for programmed crop cultivation has been developed. The developed software package will allow systemically aggregating and accumulating agrophysical and hydrometeorological information to justify land reclamation measures.