Abstract Water is a natural resource for agricultural irrigation. Recycling use of water is important in terms of resource conservation and is good for sustainable development of the ecological environment. The wastewater from daily living and industrial production contains various chemicals that are supposed as pollutants leading to the decline of water quality. For the demand of water protection and recycling, the assessment of water pollution level should be evaluated. An effective scientific technique is required for rapid detection of water pollution. Near-infrared (NIR) spectroscopy is a modern technology suitable for rapid detection of agricultural targets. For monitoring the agricultural water resource, the NIR modeling methods are required to be smart and artificially controlled to solve the issues when we confront a considerable number of data or a dynamic situation. In this study, an improved convolutional neural network (CNN) architecture was designed for a deep calibration on the NIR data. The architecture is shallow, simply constructed with one convolution layer and one pooling layer. The decision tree algorithm was employed in the pooling layer for extracting the informative features in a data driven manner. The CNN architecture was trained by combined tuning of multiple parameters in different layers. The convolution filters, the decision tree branches and the hidden neurons in the fully connected layer were automatically adaptive with fidelity to the measured data. A CNN calibration model for NIR quantitatively determination of water pollution level was then established and optimized in deep learning mode, and eventually improved the NIR prediction accuracy. Prospectively, the designed shallow CNN architecture is feasible to be used for establishing intelligent spectroscopic models for evaluating the level of water pollution, and is expected to provide smart technical support in dealing with the issues of water recycling and conservation for agricultural cultivation.

中文翻译：

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深度学习CNN架构应用于农业灌溉资源水污染智能近红外分析

摘要 水是农业灌溉的自然资源。水资源的循环利用对节约资源具有重要意义，有利于生态环境的可持续发展。日常生活和工业生产废水中含有各种化学物质，被认为是导致水质下降的污染物。针对水资源保护和循环利用的需求，应评估水污染程度。快速检测水污染需要有效的科学技术。近红外（NIR）光谱是一种适用于农业目标快速检测的现代技术。为监测农业水资源，当我们面对大量数据或动态情况时，NIR建模方法需要智能和人为控制才能解决问题。在这项研究中，设计了一种改进的卷积神经网络 (CNN) 架构，用于对 NIR 数据进行深度校准。该架构很浅，简单地由一个卷积层和一个池化层构成。在池化层中采用决策树算法以数据驱动的方式提取信息特征。CNN 架构是通过对不同层中的多个参数进行组合调整来训练的。全连接层中的卷积滤波器、决策树分支和隐藏神经元自动适应测量数据的保真度。然后建立了用于NIR定量测定水污染水平的CNN校准模型，并在深度学习模式下进行了优化，最终提高了NIR预测精度。展望未来，所设计的浅层CNN架构可用于建立评估水污染水平的智能光谱模型，有望为解决农业用水循环利用和节约问题提供智能技术支持。