Deep Learning CNN-Based Architecture Applied to Intelligent Near-Infrared Analysis of Water Pollution from Agricultural Irrigation Resources

Agricultural irrigation is a key component of agricultural production; however, inappropriate water management may lead to water pollution, affecting crop growth and ecological environment. The aim of this study is to use the advanced technique of deep learning convolutional neural network (CNN) in combination with near-infrared (NIR) spectroscopy to achieve intelligent monitoring and analysis of water quality in agricultural irrigation resources. We collected water samples from different agricultural fields and measured the NIR spectral data of these samples using a NIR spectrometer. These data were used to train and validate our designed CNN model. Our CNN model has multiple convolutional and pooling layers to efficiently capture feature information in the spectra. This study provides a new and efficient approach for water resource management and pollution monitoring in agricultural production, which can help to achieve the goals of sustainable agricultural development and ecosystem protection. Future research could further optimise the model performance and consider its application to water quality monitoring systems in real agricultural fields.