Vegetation index detection using neuromorphic systems for spiking neuron execution

The continuous advancement of technology has led to a significant increase in the number of processors in integrated circuits. This progress has enabled faster and more efficient computing, driving innovations in artificial intelligence, machine learning, and real-time data processing. However, as computational complexity increases, conventional digital architectures face significant challenges, including high power consumption, memory bottlenecks, and latency limitations. These limitations have motivated researchers to explore alternative computing paradigms, resulting in the emergence of neuromorphic systems. Neuromorphic computing is an innovative technology inspired by the human brain's functionality. At their core, neuromorphic systems are based on spike neural networks (SNNs), a biologically plausible approach to artificial intelligence that mimics the way neurons communicate using discrete pulses, or spikes. Unlike traditional artificial neural networks (ANNs), which process information in continuous value activations, SNNs use asynchronous event-based computing. This mechanism enables energy-efficient processing, reduced memory requirements, and improved adaptability for real-time applications. One of the fundamental aspects of neuromorphic hardware design is the efficient implementation of synaptic connectivity. As biological neurons form intricate networks through synaptic links, SNNs require optimized interconnections to facilitate effective learning and decision-making. Advances in neuromorphic chips, such as Intel's Loihi and IBM's TrueNorth, have demonstrated the viability of large-scale SNN-based computing, which offers promising applications in robotics, edge computing, and sensory data analytics. In environmental monitoring and remote sensing, neuromorphic computing presents an innovative approach to vegetation index detection. Traditional methods for calculating vegetation indices, such as the Normalized Difference Vegetation Index (NDVI) and the Improved Vegetation Index (IVI), rely on highperformance computer systems to process satellite and aerial imagery. These conventional techniques often require significant computational resources, making real-time and energy-efficient analysis challenging, especially in edge or remote environments. By leveraging neuromorphic processing, the calculation of the vegetation index can be performed with lower energy consumption and maintain high accuracy. This study explores the integration of neuromorphic systems for detecting vegetation indices using pecking neural networks. By coding spectral reflectance data into peak trains and processing it through an optimized SNN, we aim to demonstrate the feasibility of real-time, low-power vegetation analysis. Our research examines the efficiency, accuracy, and scalability of neuromorphic computing in comparison to traditional deep learning models, highlighting the potential of this emerging technology in environmental and agricultural applications.