Application of Artificial Intelligence in Soil Contamination Analysis: Green Sustainable Practices

Soil nutrients have a significant contribution in the health of soil and in productivity of crops. However, traditional methods of analysis of different parameters of soil such as electrical conductivity (EC), pH, organic carbon, moisture content, nitrogen, C/N ratio, phosphorus and potassium (NPK) and soil temperature are time consuming and exhaustive methods. However, with advancement in artificial intelligence, various advanced and digital based techniques can be explored for handling the large number of soil parameters data for better and efficient results. A set of AI techniques including Deep Learning (DL) and Machine Learning (ML) have great potential in advanced technology based agriculture systems. Farmers and soil scientists can identify the amount of nutrients present in agricultural soil by exploiting AI methods particularly ML and DL techniques. These techniques are accurate and adaptable techniques. Accuracy of results can be achieved by ML technique. Generative AI, a subset of AI is playing an important role to enhance the productivity in agriculture domain. In this paper, the authors have shown the application of GAN, a machine learning framework, to generate data that imitates real soil contamination data. This synthetic data can help to augment training datasets for machine learning models.