Abstract:This research aims to design and implement a water quality monitoring system in aquaculture that will be implemented in SME. Subang is a city which has a lot of potential fish farming in ponds, one of them is Rojo Koyo SMEs. The farmer has a problem, especially in the rainy season, mortality of fish increases and they don't know why this happens. This research aims to solve this problem by making a monitoring system to know the water quality in ponds at low cost because the system will be implemented in SME wi… Show more
“…A total of 4320 samples were collected from 22 July to 20 October 2021, with an interval of 30 min. After consulting the data, eight factors affecting the marine water quality were selected, including dissolved oxygen (Do), chlorophyll a (Chl), turbidity (Turb), blue-green algae (Bga), total dissolved solids (Tds), dissolved oxygen saturation (DoP), water temperature (Temp), and pH [ 20 , 21 , 22 , 23 , 24 , 25 ]. In order to explore the spatio-temporal characteristics of the marine water quality factors, the water quality factors were gathered from three adjacent stations.…”
In recent years, with the frequency of marine disasters, water quality has become an important environmental problem for researchers, and much effort has been put into the prediction of marine water quality. The temporal and spatial correlation of marine water quality parameters directly determines whether the marine time-series data prediction task can be completed efficiently. However, existing research has only focused on the correlation analysis of marine data in a certain area and has ignored the temporal and spatial characteristics of marine data in complex and changeable marine environments. Therefore, we constructed a spatio-temporal dynamic analysis model of marine water quality based on a cross-recurrence plot (CRP) and cross-recurrence quantitative analysis (CRQA). The time-series data of marine water quality were first mapped to high-dimensional space through phase space reconstruction, and then the dynamic relationship among various factors affecting water quality was visually displayed through CRP. Finally, their correlation was quantitatively explained by CRQA. The experimental results showed that our scheme demonstrated well the dynamic correlation of various factors affecting water quality in different locations, providing important data support for the spatio-temporal prediction of marine water quality.
“…A total of 4320 samples were collected from 22 July to 20 October 2021, with an interval of 30 min. After consulting the data, eight factors affecting the marine water quality were selected, including dissolved oxygen (Do), chlorophyll a (Chl), turbidity (Turb), blue-green algae (Bga), total dissolved solids (Tds), dissolved oxygen saturation (DoP), water temperature (Temp), and pH [ 20 , 21 , 22 , 23 , 24 , 25 ]. In order to explore the spatio-temporal characteristics of the marine water quality factors, the water quality factors were gathered from three adjacent stations.…”
In recent years, with the frequency of marine disasters, water quality has become an important environmental problem for researchers, and much effort has been put into the prediction of marine water quality. The temporal and spatial correlation of marine water quality parameters directly determines whether the marine time-series data prediction task can be completed efficiently. However, existing research has only focused on the correlation analysis of marine data in a certain area and has ignored the temporal and spatial characteristics of marine data in complex and changeable marine environments. Therefore, we constructed a spatio-temporal dynamic analysis model of marine water quality based on a cross-recurrence plot (CRP) and cross-recurrence quantitative analysis (CRQA). The time-series data of marine water quality were first mapped to high-dimensional space through phase space reconstruction, and then the dynamic relationship among various factors affecting water quality was visually displayed through CRP. Finally, their correlation was quantitatively explained by CRQA. The experimental results showed that our scheme demonstrated well the dynamic correlation of various factors affecting water quality in different locations, providing important data support for the spatio-temporal prediction of marine water quality.
“…The system also incorporates TDS sensors to continuously monitor the nutrient concentration in the hydroponic solution, ensuring real-time adjustments to maintain optimal levels. The integration process involved the careful placement of TDS sensors within the hydroponic setup to monitor the concentration of dissolved solids in the nutrient solution continuously [16]. The Arduino Uno was programmed to interpret data from the TDS sensors and make real-time adjustments to the nutrient supply based on predefined parameters.…”
Hydroponic farming has been a promising alternative to traditional farming, but providing plants with proper and consistent nutrition remains challenging. The issue in the research is the inaccuracy of the nutrients provided to the plants, resulting in growth that does not meet the expectations. This study aims to develop an automated nutrition system for hydroponic farming using Arduino Uno and total dissolved solids (TDS) sensors. In conducting this research, the study began with a preliminary exploration, followed by the design and integration process. Subsequently, testing and experiments were conducted on a small scale. Preliminary studies were conducted to understand existing hydroponic practices and challenges in manually supplying nutrients. The design of an automation system with Arduino Uno and a TDS sensor is made to control nutrition automatically. Automation systems are tested and calibrated to ensure accuracy and proper response. Implementation on a small scale was done with a test group and a control group in hydroponics. Growth and yield data were collected and analyzed. The results showed that the automated system provided precise and consistent nutrition, increasing plant growth and yields compared to manual methods. This research makes a scientific contribution to developing sustainable and efficient agriculture. In conclusion, nutrition automation using Arduino Uno and TDS sensors is an effective solution to increase the efficiency and productivity of hydroponic farming.
Keywords: hydroponics, automation, plant nutrition, Arduino Uno TDS sensor
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