Abstract
The inland aquaculture environment is an artificial ecosystem, where the water quality is a key factor which is closely related to the economic benefits of inland aquaculture and the quality of aquatic products. Compared with marine aquaculture, inland aquaculture is normally smaller and susceptible to pollution, with poor self-purification capacity. Considering its low cost and large-scale monitoring ability, many researches have developed spectrum sensor on-board satellite platforms to allow remote monitoring of inland water surface. However, there remain many problems, such as low image resolution, poor flexible data acquisition, and anti-interference. Apart from that, the conventional forecasting model is of weak generalization ability and low accuracy. In our study, we combine unmanned aerial vehicles system (UAVs) with the wireless sensor network (WSN) to design a new ground water quality parameter and drone spectrum information acquisition approach, and to propose a novel dynamic network surgery-deep neural networks (DNS-DNNs) model based on multi-source feature fusion to forecast the distribution of dissolved oxygen (DO) and turbidity (TUB) in inland aquaculture areas. The result of using fused features, including characteristic spectrum, Gray-level co-occurrence matrix (GLCM) texture feature, and convolutional neural network (CNN) texture feature to build a model is that the characteristic spectrum+ CNN texture fusion features were the best input items for DNS-DNNs when forecasting DO, with the determination coefficient R 2 of the vertical set arriving at 0.8741, while the characteristic spectrum+ GLCM texture+ CNN texture fusion features were the best for TUB, with the R 2 reaching 0.8531. Compared with a variety of conventional models, our model had a better performance in the inversion of DO and TUB, and there was a strong correlation between predicted and real values: R 2 reached 0.8042 and 0.8346, whereas the root mean square error (RMSE) were only 0.1907 and 0.1794, separately. Our study provides a new insight about using remote sensing to rapidly monitor water quality in inland aquaculture regions.
Highlights
Inland aquaculture refers to the use of freshwater on the land surface to engage in freshwater fishery production activities such as fishing and breeding of aquatic products with economic value
The method that we are proposing can be structured according to the following steps: unmanned aerial vehicles system (UAVs) overflight with multispectral cameral and processing of spectral data; Ground wireless sensor network (WSN) water quality data collection and processing; Characteristic spectrum acquisition based on correlation analysis; training and vertification of the DNS_DNNs; and inversion of dissolved oxygen (DO) and TUB
Characteristic spectral images were extracted from the DO and TUB data samples conforming to the requirements, and 264 DO characteristic spectral images and 174 TUB characteristic spectral images were obtained
Summary
Inland aquaculture refers to the use of freshwater on the land surface to engage in freshwater fishery production activities such as fishing and breeding of aquatic products with economic value. It is one of the important components of the entire aquaculture industry [1]. With the rapid development of economy, the discharge of industrial wastewater and domestic sewage has increased greatly, which has caused environmental pollution and polluted the water quality of aquaculture. As an important research content of intelligent agriculture and agricultural internet of things, how to quickly and accurately obtain water quality information has become a concern of scholars
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