Agricultural information is spatial in nature, so the most effective tool for organizing, analyzing, and managing such information is geographic information systems (GIS).
The problem is to use remote sensing materials (aerial or satellite images) as an operational source of geographical information for such systems, since decades of world experience convincingly confirm that space imagery allows significantly improving methods of operational crop condition monitoring and crop forecasting, improving agricultural statistics collection, increasing accuracy, homogeneity, objectivity, and frequency of observations.
Database management systems and spatial analysis tools embedded in GIS systems enable to reveal of hidden data patterns. Such analysis tools can be used to analyze the impact of relief, soil characteristics, hydrological regime, fertilizer application, etc. on farmland of any level. That is why geospatial data analysis has found such wide application in agriculture.
Types of Technology Applications in Agriculture
What is geospatial data analysis? Let’s start from the beginning. The huge area of fields, the large number of vehicles, and the large number of people engaged in agriculture have determined the need to develop qualitatively new methods of land management and agricultural production.
One of the most promising areas for improving the efficiency of agricultural production management is the use of geographic information systems based on geoinformation technology.
You can find more information by the link to see how such systems work. Mostly, they allow us to solve the following tasks:
- Information support for decision-making
- Planning of agricultural operations
- Monitoring of agrotechnical operations and crop condition
- Forecasting of crop yields and assessment of losses
- Planning, monitoring, and analysis of machinery use.
All of these benefits can be achieved with the help of different technologies that leverage the power of geospatial data analytics. Here are some of the main ones that are widely used by farmers today.
Satellite Imagery
Satellite monitoring of crops is one of the most effective and widespread technology implementation examples in farming. Basically, this method relies on spectral analysis of satellite images with the goal of generating crop maps depending on the measures of different vegetation indices.
This technology refers to precision farming methods because the difference in vegetation index dynamics informs about the disproportion in development within a crop or field. And this, in turn, makes it possible to make an effective decision on the need for additional agricultural work.
Of course, geospatial data analysis in agriculture, including satellite monitoring of crops is necessary for agronomists and company management to control crop growth and development, forecast yield, and optimize management decisions.
For business owners it’s an opportunity for assessment of the state and prospects of the business, making management decisions, and deciding on additional investments.
Smart Irrigation Systems: Ground Sensors and Weather Monitoring
The main purpose of introducing smart irrigation systems is to increase productivity and make production cheaper by saving resources (water, energy).
The systems help the farmer to control water consumption through monitoring and control of soil water availability, crop growth, and development conditions.
For example, soil sensors can be used to determine the amount of moisture in the soil, its distribution, and availability to the root system of the plant.
These types of sensors are most commonly used in smart irrigation systems. Additionally, weather sensors can be used to determine the lack of water availability by measuring air humidity, monitoring the amount of precipitation, and evaporation of moisture from the soil surface.
Automated Farm Machinery and Robotics
Technologies providing autonomous machinery appeared in agriculture together with digital technology. The desire to make the process of controlling machines precise and accurate led to the emergence of auto steering.
This function makes it possible to set the necessary parameters for the machinery, according to which its autonomous movement will be carried out.
At this stage of development of this technology along with the growth of the geospatial data analytics market there is a possibility of autonomous control of several machines at once and even in the absence of a GPS signal.
The main advantage of robots is automation. Robots perform their tasks without the intervention of the farmer and the data obtained from them can help make effective decisions.
With the help of available online databases, weather forecasts, and data from internal and external sensors, robots will know more about what is happening on the farm than the farmer could know by performing automated actions himself.
The robots can also be powered by electric motors and can reduce energy consumption and carbon emissions on the farm. Some of them will soon possibly be powered by solar panels. Robotization of agriculture is an opportunity to make it less harmful to the environment.
