Artificial Intelligence in Agricultural Transformation for Enhancing Food Security through Precision Farming

Growing worldwide consumption of food security has laid the foundation to make farming methods more effective and sustainable. Machine learning (ML), which falls under the broader AI framework, has become an innovative enabler for precision farming—we facilitate the continuous analysis of data, the use of this data for modelling Outlooks to support farming activity, and manage resources to improve farming yields. This paper discusses the Implication of AI in precision farming as a concept that enhances crop yield forecast, resource utilisation, and the mitigations of farming hurt on the environment. The proposed AI models also use IoT sensors, remote sensing, and machine learning to decide on the use of water, fertilizers, and pesticides. The simulations reveal quite encouraging gains to the precision of yield accuracies, optimization of resources, and reduction of costs. The investigated AI applications in farming demonstrate a significant capacity to cut the farming manifold and environmental emissions, thereby indicating an ability to support the longer-term health and growth of farming and food security for the globe. However, issues like data quality, the accuracy of models used and declaration or adoption have become issues yet to settle. The study indicates that there is hope for the modern agriculture as being revolutionized by AI and points to the ways of improving the use of AI further.