Optimizing Soil Fertility through Machine Learning: Enhancing Agricultural Productivity and Sustainability

Authors : Ayhan Arısoy, Enes Açıkgözoğlu

Pages : 124-133

Doi:10.30516/bilgesci.1532645

View : 58 | Download : 117

Publication Date : 2024-09-30

Article Type : Research Paper

Abstract :Nowadays, the sustainability of agriculture and food security have an increasing importance on soil fertility. Soil fertility is defined as the capacity of a land to grow crops and its potential crop productivity. However, factors such as increasing population, climate change, land use changes and environmental pollution threaten soil fertility. These threats can result in problems such as erosion, soil salinisation and organic matter depletion. Soil fertility is critical for the long-term health of agriculture and food security. Artificial intelligence techniques used to determine and manage soil fertility analyse the minerals present in the soil as well as other factors. These analyses assess the amount of minerals present in the soil, the availability of nutrients and important parameters such as pH. This information guides farmers in selecting the most appropriate crops. Furthermore, the integration of Internet of Things (IoT) technologies allows real-time monitoring of minerals and nutrients in the soil and optimising irrigation and fertilisation processes based on this data. These developments have the potential to improve soil fertility management and increase agricultural productivity.
Keywords : Soil Fertility, Machine Learning, Precision Agriculture, Artificial Intelligence, Sustainable Agriculture