Artificial Intelligence-based Green Technologies for Efficient Agriculture

Modern agriculture poses an important challenge. It affects food quality, environmental conditions and the agricultural sector's capacity to adapt to climate change. Traditional fertility management methods have significant drawbacks. They sometimes use too many chemicals, lack real-time, location-specific data, and dissipate resources. These problems lead to soil weakening, lower crop yields, and a greater environmental impact. Due to high implementation costs, a lack of Machine Learning model awareness, poor worldwide standards, and variable soil data availability, it is difficult to make data-driven decisions in soil management. This study explores the growing significance in Technologies like Artificial Intelligence (AI) and Machine Learning (ML) in sustainable farming systems by focusing on distant sensing methods, sensor networks with the Internet of Things (IoT), robotics, and data-driven decision support systems. The current state of these technologies and their increasing use in more precise diagnoses and effective soil management are also addressed.