Development of deep learning quantization framework for remote sensing edge device to estimate inland water quality...

Recent achievements in the fields of deep learning and remote sensing have led to their application in monitoring river water quality. One of the most researched methods is the estimation of total suspended solid (TSS) concentrations using multispectral imagery and convolutional neural network (CNN) models. Owing to the sorption capacity of other pollutants, TSS monitoring is essential. However, despite recent advances in deep learning, the application of contemporary technologies in water quality monitoring has not yet been fully explored. This study aims to develop a framework for on-device AI that can be applied to edge devices through quantization using a lightweight deep learning model. Lightweight CNN models were identified using neural architecture search (NAS) in conjunction with Pareto optimization, achieving high performance (0.806 of Nash-Sutcliffe efficiency (NSE)) while minimizing computational burden (8.118 MB). The model sizes were further compressed (0.736 MB) through the application of post-training quantization (PTQ) and quantization aware training (QAT), ensuring that accuracy (0.831 of NSE) was preserved. This provides a scalable approach for real-time TSS monitoring, bridging the gap between advanced deep learning techniques and practical environmental applications. These applications indicate that it is possible to estimate other water quality indices using multispectral imagery. It enables the tracing of the source of contamination and facilitates rapid responses by identifying changes in real time.