AI Based Leaf Age and Maturity Identification Systems: A Comprehensive Review of Microcontroller Integration, Techniques and Applications

The development of artificial intelligence combined with microcontroller-based systems proves to be a breakthrough method in the precision farming. It allows smart decision-making and optimizes the usage of resources with real-time processing capability. This review describes the current system of AI-based systems in leaf age and maturity identification in most detail, with special focus on microcontroller inclusion, computing difficulties, and use cases. The review systematically examines many Artificial Intelligence methods such as convolutional Artificial Intelligence, machine learning algorithms, and edge computing strategies in solving leaf classification problems. Key advantages of microcontroller-based AI systems include cost-effectiveness 60–70% reduction compared to server-based systems low power consumption <50 mW, real-time decision-making capabilities >90% latency reduction, and cloud independence > 95% uptime in poor connectivity areas. Nevertheless, there are still serious issues such as the lack of computing resources, changes in the environment, and special skills. A combination of IoT, blockchain and enhanced sensor networks should provide the opportunities to improve the data security, the positioning the supply chain and deploy it at scale. The future research directions improve hybrid AI architectures, federated learning, and optimizing/training with a quantum advantage to address the computational bottleneck challenges favouring ecosustainability and efficiency in AI. The review presents a guideline to other researchers and practitioners as to how they can develop their own independent data-driven agricultural systems to be able to attain higher productivity, with reduced damage to the environment.