Exploring Opportunities of Generative Artificial Intelligence for Sustainable Soil Analytics in Agriculture

Generative Artificial Intelligence (GenAI) integration in agriculture, especially in soil analytics, intends to cause a potential technological revolution. This work intends to experiment with the potentiality of GenAI and its models like Generative Adversarial Networks, Transformers, Autoencoders, Autoregressions models, etc. GenAI could significantly help in analyzing and understanding soil data to find trends and patterns, subsequently predicting the yield of crops, among others. It can also help optimize fertilizers and other inputs, reduce water use, and improve soil health. GenAI has the potential to make the agriculture sector sustainable and productive. The research identifies the fact that it will enhance precision nutrient management. It provides for soil health improvement strategies and environmentally friendly farming practices through the analysis of multi-sources of data that give rise to better development of fertilization strategies. In doing so, it identifies present realms in which GenAI is applied to the benefit of agricultural developments, building a conceptual framework that should be implemented in agriculture, pinpointing challenges, opportunities, futuristic insights, and—last but not least—value for research, stakeholders, and users.