Estimating water quality parameters of freshwater aquaculture ponds using UAV-based multispectral images

• Published in: Agricultural water management Vol. 304; p. 109088
• Main Authors: Chen, Guangxin, Wang, Yancang, Gu, Xiaohe, Chen, Tianen, Liu, Xingyu, Lv, Wenxu, Zhang, Baoyuan, Tang, Ruiyin, He, Yuejun, Li, Guohong
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 01.11.2024; Elsevier
• Subjects: Freshwater Aquaculture; Machine learning; Spectral correction; Unmanned Aerial Vehicle (UAV); Water Quality; Unmanned Aerial Vehicle (UAV); Freshwater Aquaculture; Spectral correction; Machine learning; Water Quality
• ISSN: 0378-3774; 1873-2283
• DOI: 10.1016/j.agwat.2024.109088

UAV imaging technology has become one of the means to quickly monitor water quality parameters in freshwater aquaculture ponds. The change of sunlight during a long flight affects the quality of UAV images, which will reduce the accuracy of monitoring water quality. This study aims to propose a method to correct spectral variation during UAV imaging and apply it to detect dissolved organic matter (DOM) concentration and dissolved oxygen (DO) content in freshwater aquaculture ponds. Firstly, a spectral correction method was used to transform UAV-based multispectral images. The spectral data before and after correction was extracted. Secondly, 18 spectral indices before and after correction were constructed. The optimal combination of indices was identified using correlation analysis algorithm. The estimation models of water quality parameters were then constructed and compared using the Random Forest (RF), Support Vector Regression (SVR), and BP neural network (BP) methods. The results showed that the accuracy of estimating DOM concentration using corrected spectral indices was significantly improved compared to pre-correction models, with the highest improvement of 38 % (SVR), the lowest of 23 % (BP), and an average improvement of 31 %. The RF model performed best, achieving R² = 0.81, RMSE = 3.34 mg/L, and MAE = 2.17 mg/L. For DO content estimation, the accuracy of models using corrected spectral indices was also improved significantly, with the highest improvement of 97 % (RF), the lowest of 39 % (SVR), and an average improvement rate of 67 %. The Random Forest model was again optimal, with R² = 0.69, RMSE = 1.97 mg/L, and MAE = 1.47 mg/L. This study indicates that the proposed spectral correction method helps to map the concentration of DOM and DO in freshwater aquaculture ponds with high accuracy using UAV-based multispectral images. •A method was proposed to correct the impact of sunlight changes on UAV images.•Monitoring of water quality using UAV technology in freshwater aquaculture ponds.•Application of machine learning in detecting highly heterogeneous water information.