Digital agriculture mapping (Crop monitoring)

Compliance of the actual use with the intended purpose and authorized use of land plots, as well as changes in the condition of lands of all categories due to the impact of natural and anthropogenic processes shall be established based on the results of monitoring of land use. Maps of land use shall reflect the relationship of land plots with natural conditions, which is necessary for scientific forecasting of rational use of lands.

Cartographic materials:
- Land management plans and schemes in scales ranging from 1:5,000 to 1:200,000.
- Forestry management plans.
- Topographic maps up to a scale of 1:300,000.
- Land use maps, which serve as the base for all other agricultural maps.

Statistical materials:
- Aggregate data for large regions and countries, including summaries for districts, regions, and federal subjects.
- Annual reports from individual agricultural enterprises and agronomists.
- Land reports and agricultural census data.

Remote sensing materials:
- High-resolution satellite images used for creating maps of agricultural land, cadastral maps, and land use photoplans at scales up to 1:50,000 and 1:25,000.

Field research:
- Primarily used for regional agricultural mapping, involving route and key surveys of farms following a specially developed program.

They appeared in the middle of the XIX century in the "Economic and Statistical Atlas of European Russia" (1851) and characterized the location of the main branches and crops of agriculture. At the end of XIX - beginning of XX centuries a number of interesting agricultural maps and atlases were published: "Map of the most important branches of productivity of European Russia" (1872), which shows the areas of bread growing, flax growing, beet growing, tobacco growing, silk growing; the famous "Atlas of Asian Russia" (1914). The famous "Atlas of Asian Russia" (1914), complex in its content, but oriented to display the conditions of agricultural development of the Asian part of Russia in the early 20th century; the album "Agricultural Trades in Russia" (1914), which is a collection of statistical maps that characterize the country's agriculture by large territorial units - provinces. Of the numerous agricultural maps, the "Map of Agriculture of the USSR" (1926), compiled under the direction of N. I. Vavilov using the point method, is of particular interest. This method was further developed and improved when drawing agricultural maps by BSAM. The "Atlas of Agriculture of the USSR" (1960) and the "Atlas of the Development of National Economy and Culture" (1967) belong to the overview agricultural mapping.

Lands of agricultural purpose are recognized as lands located outside of populated areas, provided for agricultural needs, and intended for such purposes.

Within the lands of agricultural purpose, the following are distinguished:

• Agricultural lands;
• Lands occupied by intra-farm roads, communications, protective afforestation for land improvement, water bodies (including ponds formed by water retention structures on watercourses and used for pond aquaculture purposes);
• Objects of capital construction and non-capital structures used for the production, storage, and primary processing of agricultural products, as provided by federal laws;
• Non-stationary retail facilities;
• Residential houses, construction, reconstruction, and operation of which are allowed on land plots used by peasant (farm) households for their activities, or on land plots intended for citizens' gardening for personal use (Article 77 of the Land Code of the Russian Federation).

Digital agriculture mapping utilizes remote sensing data to monitor crops by providing information on vegetation health, growth patterns, and crop conditions. Specific data obtained includes Normalized Difference Vegetation Index (NDVI), chlorophyll content, and land surface temperature, enabling precision farming practices.

UAVs in digital agriculture mapping offer high-resolution and timely data collection capabilities. They provide detailed imagery, multispectral data, and 3D terrain models, allowing for more accurate and frequent crop monitoring compared to traditional methods. UAVs enhance precision agriculture practices by enabling targeted interventions based on real-time information.

Remote sensing data aids in detecting crop diseases, pests, and nutrient deficiencies by capturing spectral signatures associated with stressed vegetation. This information allows for early intervention by farmers, enabling targeted treatments, optimizing resource use, and minimizing crop losses.

Digital agriculture mapping supports yield estimation and forecasting by analyzing crop health, density, and growth patterns. Farmers can use this information to make informed decisions about irrigation, fertilization, and harvesting schedules, optimizing yield and resource efficiency.

Machine learning and data analytics enhance digital agriculture mapping by enabling the automated analysis of large datasets. These technologies can identify complex patterns in remote sensing data, allowing for the development of predictive models for crop monitoring. This integration enhances the accuracy of monitoring, enabling farmers to make data-driven decisions for crop management.