Smart Farming System: AI-Driven Precision Agriculture for Sustainable Farming

Abstract

The agricultural practice is changing at a fast pace with farmers experiencing increasing problems in terms of climate variability, soil degradation, pest outbreaks, and ineffective utilization of inputs. These problems usually result in decreased productivity and increase in operation costs particularly among small and medium farmers who do not easily access modern tools. The proposed study is a Smart Farming System, which incorporates the use of artificial intelligence, machine learning, and IoT-based sensing technologies to aid data-driven agriculture decision-making. The system collects real-time data on the moisture of soil, nutrient level, weather, and crop health as a result of distributed sensors, drones, and remote imagery. This data is analyzed using machine learning models to give personalized suggestions on irrigation, fertilization and pest management, and image processing CNN-based image processing detects the early disease indicators. Predictive analytics are also useful in estimating crop yield and predicting possible risks. The proposed system is meant to improve the efficiency of the farm, decrease the wastage of the resources, and facilitate the sustainable farming techniques. The solution could be used to close the technology divide between conventional operations and the modern precision agriculture through the provision of a scalable and user-friendly platform.