Harnessing microbiomes and machine learning for sustainability efforts

Shah, Mahi (2025) Harnessing microbiomes and machine learning for sustainability efforts. International Journal of Science and Research Archive, 14 (2). pp. 1476-1481. ISSN 2582-8185

The Malthusian theory suggests that exponential population growth will eventually surpass the linear growth of food supply, leading to widespread land scarcity for urbanization. However, Malthus could not have anticipated the advancements in scientific knowledge and technology that now have the potential to revolutionize food production. With the global population projected to reach 9 billion by 2050 [4], ensuring sustainable food systems while minimizing environmental harm has become a critical challenge. Recent research reveals the role of the soil microbiome—complex communities of bacteria, fungi, and other microorganisms in enhancing plant growth. Notably, the correlation between soil bacteria and the growth of barley provides a method for identifying the most productive land. This study tests the machine learning capability to analyze soil microbiome data to predict crop yields based on the role of bacteria. A dataset of 627 bacterial strain features from over 1,340 farms was analyzed using Python to build regression models, uncovering patterns that optimize smart farming practices and soil conservation [5]. By integrating artificial intelligence with microbiology, the research highlights innovative approaches to advancing sustainable agriculture by identifying the most suitable areas for crop cultivation. The paper demonstrates the effectiveness of KNN, Decision Tree, and MLP Regressor models in accurately predicting crop yields, outperforming random chances or any existing lottery-based systems.