XI INTERNATIONAL CONFERENCE
“INFORMATION TECHNOLOGY AND IMPLEMENTATION” (IT&I-2024)
Crop monitoring system based on earth image analysis to predict crop yields in fields
Stepan Bilan, Oksana Vlasenko, Oleksandr Honcharuk, Yevhenii Roiko
Problem and Necessity
The agricultural sector in people's lives is a necessary component of the survival and normal development of society.
The development of information technology has made it possible to increase the productivity of the agricultural sector.
The introduction of modern information technologies automates many processes in agriculture and also increases the yield based on the analysis of previous harvests.
One of the widely used approaches is continuous monitoring of crops by analyzing images obtained from various aircraft.
Problem and Necessity
To solve the tasks set, there are already methods that ensure high reliability of the analysis. However, they are quite complex and require large computational resources. Therefore, there is a constant search for new, more advanced methods that can be used to solve many problems in processing images of crop fields.
One of the approaches to the implementation of these methods are methods using cellular automata technologies, edge pixel analysis and the Radon transform, which in such a combination make it possible to simplify the analysis and preliminary preparation of images, which increases the accuracy.
This paper discusses methods for analyzing and identifying atmospheric distortions that affect crop yields, as well as methods for assessing the condition of corn crops based on crop density analysis.
Monitoring of atmospheric distortions in the analysis of images of the earth's surface of agricultural land
In modern conditions, when analyzing images of agricultural crops obtained from aircraft, problems often arise due to the influence of atmospheric distortions, such as clouds, light scattering, etc.
The presence of various types of distortions reduces the ability to clearly analyze the image of fields with crops, which leads to false results when making decisions and forecasting the harvest.
To improve the quality of field image analysis, there is a need to identify atmospheric distortions in satellite images or images obtained from various aircraft.
Existing methods do not provide high accuracy in determining the presence of clouds, which distort the analyzed areas of the field with crops with a certain degree of transparency. It is also difficult to determine the degree of distortion in high humidity, smoke and other distortions in a cloudless atmosphere of the earth.
To improve the accuracy of image analysis, the work uses a combination of methods for selecting the most informative pixels of a raster image using cellular automata technologies.
Examples of images of fields recorded from aircraft
For example, it is possible to obtain such photos using the EOSDA Crop Monitoring program.
Selecting the inner area of a field plot
Result
All pixels located outside the outline take code 0 and become background pixels,
Pixels located inside the outline retain the original code
Atmospheric distortion detection
An area of land is allocated in accordance with the method described above.
The average value of the pixel codes located within the area is determined.
Confidence interval values are set to identify groups of pixels that have the same color and brightness within the boundaries of the confidence intervals.
To do this, determine the value of D according to the formula for each pixel of the selected area
An example of selecting groups of pixels on an image of a selected area for different atmospheric distortions
An example of highlighted groups of pixels indicating the presence of clouds
An example of the formation of six Radon projections at angles 00, 300, 600, 900, 1200 and 1500
Method for assessing the condition of corn crops based on the analysis of seedling density
Drone image of corn crop in early stages of growth
Method for assessing the condition of corn crops based on the analysis of seedling density
Image of corn crop after threshold treatment
The result of forming the Radon projection in the 00 direction
Example image of a highlighted corn row
An example of generated Radon projections for one row of corn
An example of a pulse sequence that determines the number of plants in a row
Conclusion
The paper presents the results of combining edge pixel extraction methods, cellular automata technologies and Radon transform for efficient monitoring of the state of agricultural land areas recorded on raster images. The proposed methods make it possible to select any area in the image without distorting the codes of pixels located inside the area. This allowed for efficient analysis of atmospheric distortions during monitoring of the condition of a selected agricultural area on a raster image. Using edge detection operators and Radon projection analysis for a selected area, the degree of shading of an agricultural area by clouds and the impact on the quality of display of the area of the earth's surface under different weather conditions are determined. The use of the Radon transform made it possible to determine with high accuracy areas in fields shaded by clouds. The use of threshold processing in combination with cellular automata and Radon transform technologies allows us to estimate the yield of agricultural plants based on the analysis of crop density. Using the example of analyzing the density of corn crops, the effectiveness of the proposed method was proven, which provides 99% accuracy in automatically calculating the number of seedlings and determining the density of crops, which made it possible to predict the harvest with high accuracy. For the analysis of crop density of agricultural plants, high demands are not placed on image quality. The combination of the described methods automatically indicates the location of weeds, which makes it possible to analyze their impact on crop yield. The use of threshold processing implements additional functions of the system, which are aimed at analyzing the soil to determine the content of various chemicals, the amount of fertilizer in each section of the field and other components used in the agricultural sector.
In further studies, the authors plan to use analysis of the impact of weeds present in an image to predict crop yields.