AI-Powered Classification of Soils Based on Microscopic Images

 AI-Powered Classification of Soils Based on Microscopic Images

Soil classification is one of the first and most essential things we learn in civil engineering; however, in practical life, it's not as easy as it's in theory anyway. Soil can be classified in a number of ways, such as sieve analysis, Atterberg limit, and observation by the engineers themselves. It is a easy and can be more reliable method, but it may take long time, and may vary depending upon the experience of the engineer performing the experiment. Hence, slight variation in soil classification can be seen among the reports of the engineers.

To counter this issue, artificial intelligence is slowly finding its way into soil classification. One of the unique technique that can be used in soil classification is  classification of soil using microscopic images. This method uses soil samples photos and seen under a microscope , good images are then used. These images contain information such as the size of the grains and their arrangement. While soil classification can only be done through the help of people’s judgement

This will depend on parameters such as moisture content, composition, and in-situ conditions, and such parameters are not always observable in microscopic images. If the training data of the AI algorithm used is not adequate or of low quality, it will not be possible to obtain accurate results by depending solely on AI.

The application of microscopic images for classifying soils draws upon extracting features from images, which are otherwise difficult to determine in a standardized manner. Features that can be seen under a microscope include particle size, shape, roughness, angularity, and particle arrangement, which are crucial markers for determining if a particular soil contains more sand, silt, or clay. Artificial intelligence models, specifically machine learning, are trained using a large number of images that are classified, so that upon being applied to a test picture, the soil type can be deduced in a very short time period.

There are also some practical issues. Not all soil laboratories are equipped with sophisticated microscopes or facilities to process images. Engineers are also required to have proper knowledge of soil mechanics to interpret the test results. AI should help engineers, not hinder them.

Conclusion:

     AI-assisted soil classification systems using microscopic images have vast potential as a supporting technology in the field of civil engineering. Such systems can lead to increased efficiency and fewer errors when used appropriately. Nevertheless, it is essential that the AI system result be supplemented with the conventional soil test for a more accurate outcome. As civil engineering students, it is imperative that we familiarize ourselves with the AI technology while sticking to the conventional methods of soil classification.

THE BELOW PICTURES DISPLAYS THE TYPE OF SOILS