A deep learning CNN architecture applied in smart near-infrared analysis of water pollution for agricultural irrigation resources

By Huazhou Chen, An Chen, Lili Xu, Hai Xie, Hanli Qiao, Qinyong Lin, Ken Cai. 2020

302 citations recorded by the source corpus.

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.

Subfields: Industrial and Manufacturing Engineering, Plant Science, Water Science and Technology

View Paper: https://www.semanticscholar.org/paper/5f56e6e5971a23e5e0dfe2fdcbaf48aba54efe9e

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Industrial and Manufacturing Engineering

1. Ali Asghar Heidari
2. Huiling Chen
3. Iman Ahmadianfar
4. Amir H. Gandomi

In Water Science and Technology

1. Mengyuan Zhu
2. Amir H. Gandomi
3. Hongqiang Ren
4. Jiawei Wang
5. Xiao Yang
6. Lin Ye
7. Bin Wu
8. Linyu Zhang

Citations in the corpus

• RUN beyond the metaphor: An efficient optimization algorithm based on Runge Kutta method (2021, background): “Real-world problems are faced every day, and we need to develop solvers for deep learning applications (Chen, Chen, et al., 2020; Li, et al., 2019; Qiu, et al., 2019), decision-making procedures (Liu, et al., 2016; Liu, et al.; Wu, et al., 2020), optimal resource allocation (Yan, et al., 2020),…”
• A review of the application of machine learning in water quality evaluation (2022, background): “Do-mestic and industrial wastewater contains various pollutants, calling for an evaluation of water quality before treatment [75].”