Before Sowing proper preparation of the soil is very important in crop production. Agriculture is one of the most vital activities. At the same time, this industry is highly dependent on environmental conditions.
A combination of physical, chemical and biological processes forms soil. It is a complex mixture of minerals, water and organic matter containing biotic and abiotic components that support life on our planet. Maintaining the health of the soil ensures its adequate functioning, so it is crucial to take care of this component and adequately prepare the land before planting crops. It is the main component of crop production, which determines the quantity and quality of the produce.
What Are The Steps Before Sowing
These are the steps before sowing crops.
- Mixing and loosening the soil is essential for deep root penetration before Sowing. Earthworms and numerous soil bacteria contribute to loosening and enriching the soil with nutrients and humus. Growers can prepare the soil for planting seeds using three procedures. In the future, the procedures’ effectiveness can be assessed using, among other things, the calculation of NDVI.
- Manuring is also done before the seeds are planted to increase soil fertility. It is recommended to add manure before ploughing the field for better mixing.
- Ploughing helps ensure deep root penetration into the soil, which is crucial for better aeration and firm rooting. This procedure also removes unwanted items, including weeds. Nutrients and humus are also better formed after ploughing.
- Levelling the ground improves the soil’s ability to retain moisture. Also, this procedure can significantly improve the distribution of water during irrigation.
- Farmers also use different methods and tools to plant seeds in the soil. Broadcasting is the primary method of planting seeds, which is the definition of spread. This method can mean mechanical and manual processes.
- Drilling is another method of planting seeds, continuously pouring seeds into the furrows. A special seeder is used as a tool, which ensures the desired distance between the seeds and higher accuracy.
- Dribbling is the most common method of planting seeds into holes made in the soil. For digging, a special dribbler tool is used, which drills holes of a certain depth at a certain distance from each other. In the field, conical-shaped tools are used.
Satellite Monitoring and Preparing for Sowing
Satellite technologies today play a crucial role in many industries, and agriculture is no exception. Remote sensing significantly expands the capabilities of farmers, agronomists and other agribusiness participants. Satellite-driven digital solutions can help to plan tillage, having assessed field conditions previously. Such a solution is the EOSDA Crop Monitoring, the platform created by EOS Data Analytics for precision farming. It provides ample opportunities for determining the health of crops and soil and timely detection of various problems in the fields.
Thanks to the platform, users can choose the most optimal time to start sowing by determining the water reserves, the moment of physical maturity of the soil, as well as receiving a 14-day weather forecast. Historical weather data available since 1979 makes it possible to detect important climatic patterns that affect crops, and that can threaten soil preparation and seeding.
EOSDA Crop Monitoring provides access to calculations of vegetation index values, including using the NDVI formula. It is the most crucial indicator of crop health, thanks to which agronomists track the dynamics of crop development at all stages of the growing season, plan fertilizer application and other field activities.
On February 28, 2023, EOS Data Analytics, an AI-powered satellite imagery analytics company, hosted a public webinar. The event’s topic was the application of remote-sensing-driven solutions to prepare for the sowing campaign in Ukraine. The webinar also discussed issues relevant to agriculture this year and the benefits that the EOSDA Crop Monitoring platform and other company solutions provide to industry participants.
Vasyl Cherlinka, Soil Scientist at EOS Data Analytics, who spoke at the webinar, emphasized that it is crucial to consider soil temperature and soil moisture content for proper timing of sowing. It is possible thanks to satellite data and models developed by the company. The NDVI calculation is also essential, as its values help assess the condition of winter crops after a cold period to plan the application of soil nutrients in detail.
What is NDVI and why it’s useful
The Normalized Difference Vegetation Index (NDVI) is a simple graphical indicator to determine if an area under observation contains healthy green vegetation. With this index, it is also possible to determine the presence of living green vegetation using reflected light in the visible and near-infrared ranges. NDVI is used for various purposes, including drought monitoring, yield forecasting, fire zone identification, and mapping desert encroachment.
Simply put, this index indicates the density and health of vegetation displayed at each pixel on the NDVI imagery from the satellite. It also shows the ability of the surface to photosynthesize.
AI in Agriculture: Crop Yield Estimation
Digital technologies in agriculture help to reduce risks, eliminate the human factor, reduce costs and increase crop yields. The main objective of digitalization is to reduce the cost of production and improve its quality and competitiveness through the efficient use of resources.
Improving the efficiency of agricultural enterprise management through digitalization technologies contributes to the preservation of competitiveness in the market. To work without digitalization means to lose in the global competition. To make the right management decisions we need information and data which can be collected by such technologies as satellite monitoring, high-tech sensors, GPS systems, etc.
Global experience of countries with developed agri sectors shows that the introduction of IT in production allows for reducing unplanned expenses via using innovative crop yield prediction software, consolidating the data received from ground sensors, drones, satellites and other external applications to make smart decisions. New technologies make it possible to trace the entire path of a product from the field to the consumer, which guarantees its quality and ensures the needs of customers are met.
Modern entrepreneurs need to produce more food while using fewer resources, so a significant breakthrough in agricultural production technology is needed to achieve high crop yields in a sustainable way.
AI in Agriculture
A large number of processes in the agricultural industry are now being automated. Artificial intelligence helps farmers make their work more cost-effective. This is done by reducing expenditures on inputs and increasing yields. A huge number of systems are now helping the farmer to make a decision to do any work on the farm: weather stations, moisture sensors, satellites providing images of the area, etc.
Various herbicides and chemicals are now ubiquitous in agriculture. The use of these substances is generally accepted even at the legislative level, but this does not negate their harmfulness despite the benefits they offer. By reducing the number of pesticides used, it is possible to reduce financial costs and improve the condition of the land with a further increase in yields.
Modern technology makes it easy to diagnose plant disease, choose a treatment, and calculate the estimated damage. And with that information, it becomes much easier to perform crop yield prediction, which is crucial for a farming business.
Crop Yield Estimation
Today, when making decisions, the agricultural producer has previously unavailable sources of information: satellite and UAV images, readings of moisture sensors, ground-based weather stations, etc. At the same time, new monitoring and control systems are constantly appearing on the market, which offers individual, more accurate analysis and forecasting, including ones that enable crop yield estimation using remote sensing.
Among all parameters of crop production, a special place is occupied by crop yields. The issue of yield forecasting is of great scientific and practical interest, and a large number of works are devoted to it.
One of the tasks of artificial intelligence applications in agriculture is the generalization, analysis and processing of data from various monitoring tools. On the basis of data analysis, the areas of crops with depressed growth are identified, plant diseases and pest problems are detected, and the provision of plants with nutrients, potential yields, etc. are determined. All of this data combined enables farmers to estimate future yield.
Yield Prediction with EOSDA
Satellite monitoring is one of the most effective crop yield forecasting methods, and software tools like EOSDA Crop Monitoring take it to the next step. This precision farming platform allows users to access all the important fields’ data in one place, including information on crops’ health at different growth stages, enabling growers to make the most reliable and timely decisions.
Analyzing satellite imagery with the help of AI, the tool helps detect crop issues early on, adjust fertilizers application, manage water consumption, and save fuel due to a smaller number of field trips. Effective, remote, and early detection of any changes speeds up the decision-making process, which means growers save time, cut costs, and eventually get higher yields.
EOSDA also offers a custom solution for advanced yield forecasting. The company’s team of scientists and engineers use remote sensing and machine learning models to ensure maximum efficiency and accuracy of crop yield prediction. For this purpose, two prediction models are used: biophysical and statistical. Combined together, they allow for achieving an accuracy of 95%.
Accurate yield prediction is vital for an agribusiness, and modern technology, including satellite monitoring, makes it much easier thanks to a variety of available and affordable ways of field data collection and analysis.
Learn More
- What is Salinity
- Farming States in the United States of America
- Agriculture Artificial Intelligence. Practical Applications and Challenges of Using AI in Agriculture
- Farmers and Modern Agriculture
- Drone technology in Agriculture
- FAO: Food and Agriculture Organization of the United Nations
