Intelligent monitoring method of water quality based on image processing and RVFL‐GMDH model

Abstract

The water quality, contaminant migration characteristics, and emissions quantity of pollutants in the basin would have a great impact on aquatic creatures, agricultural irrigation, human life, and so on. In the aquaculture industry, because water colour can reflect the species and number of phytoplankton in the water, the water quality type can be obtained by analysing the colour of the aquaculture water using image processing techniques. Therefore, this study proposes an intelligent monitoring approach for water quality. The critical features of water colour images are extracted, and then using the machine learning methods, an intelligent system for water quality monitoring is established based on the fused random vector functional link network (RVFL) and group method of data handling (GMDH) model. The proposed approach presents a superior performance relative to other state‐of‐the‐art methods, and it achieves an average predicting accuracy of 96.19% on the feature dataset. Experimental findings demonstrate the validity of the proposed approach, and it is accomplished efficiently for the monitoring of water quality.

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Study on Water Quality Change Trend and Its Influencing Factors from 2001 to 2021 in Zuli River Basin in the Northwestern Part of the Loess Plateau, China

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A CNN-based image detector for plant leaf diseases classification

“…As you can see in Table 1 , the state-of-the-art plant diseases models achieve a very high classification accuracy, requiring a large number of parameters and higher computation cost, which prohibit their usage in embedded devices. In recent years the models have achieved very high accuracies, exploring different methods: transfer learning applied to existing architectures in literature [4] , [21] , [8] , [23] , [27] , existing DNN models combined with different features extraction methods [11] , [17] , modified versions of existing networks [31] , [33] , [41] , [36] , novel network architectures [25] , [29] , [34] , [38] , [39] , [40] . Such a higher accuracy, in most cases, has been reached using complexed architectures that require a high number of parameters.…”