Over the last four decades, aquaculture has become the fastest-growing agroindustry worldwide [1
]. The global production of fish continues to grow slower than the population (mainly as fishing remains almost stable, at around 80 million MT). In this context, aquaculture has contributed more day by day, with a production of around 80 million MT (110 million MT, if aquatic plants are considered) having been reported by the FAO in 2018, with an associated value of approximately 243.5 billion dollars. This has increased the availability of aquatic food for human consumption from 10 kg per capita in 1960 to more than 20.2 kg per capita in 2015 [2
]. The accelerated growth of aquaculture has made the necessary design and development of production systems with new technologies into a field known as “precision aquaculture” (or PAq). These technologies are capable of assisting aquafarmers in decision-making, including in intensified systems [3
]. PAq can be considered as the application of information technologies (software and hardware) into the field of aquaculture biology, in order to provide support to the production systems. In developing countries, it is very relevant to facilitate access to such technologies, adapting them to the regional situation at accessible costs.
One of the most important aspects to consider in intensive aquaculture is the maintenance of water quality, as it is directly related to the adequate development of the farmed organisms. Poor water quality may be responsible for creating stress conditions, which can affect feed consumption, growth, survival, and reproduction. Thresholds or adequate levels of water-quality parameters depend on the type of culture [4
]. The monitoring of physical and chemical variables, such as dissolved oxygen, temperature, and pH, in the water column is vital to maintain adequate conditions and to avoid undesirable situations which may lead to the collapse of aquaculture systems [5
]. It has been well-documented that some aquatic species are highly sensitive to drops in dissolved oxygen levels, abrupt variations in temperature, and changes of pH. For those reasons, most instrumentation systems include sensors for these parameters, not only for monitoring them, but also to have early information about potential contaminants in the water [6
]. Dissolved oxygen (DO) is a very important and limiting factor in aquaculture systems, as all aquatic organisms (except for some bacteria) need a source of DO to live and develop. For this reason, the concentration of DO in the water column is one of the most important aspects to manage for suitable aquaculture. Low levels of DO can affect the feed consumption and, in extreme cases, can cause mass mortalities of aquatic organisms [7
]. The pH in freshwater ranges around 7, while that in marine waters is around 8. For shrimp farming, the recommended pH for optimal growth is from 7.0 to 9.0 [7
]. When the pH is under 7 (acid), adverse effects on gill function and growth may result. If the level decreases under 4, acid death occurs; meanwhile, at levels over 11.0, alkaline death is possible. Temperature is another very important parameter for aquatic organisms, mainly for those which are poikilotherms, as their corporal temperature varies directly as function of environmental temperature. Wide variations in internal temperature can have significant effects on physiology and metabolism and, consequently, on feed consumption, growth, reproduction, and so on [9
For the development of instrumentation systems, platforms known as open source hardware (OSH) have become widely available, which are new tools for the implementation of electronic projects. The Arduino, for instance, is a commercial platform which is very popular in the student community, as it permits the development of a suite of automation projects and, additionally, can save time. It was created in 2005 by the Interaction Design Institute Ivrea, Italy. OSH have become increasingly more utilized, due their versatility, in applications such as robotics, automation, precision agriculture, and many others [10
]. On the other hand, wireless sensors networks (WSN) have become very useful tools to process information in an efficient manner for laboratories, saving time in analyzing samples [13
]. At present, WSNs are used as a reliable tool for the monitoring of water-quality parameters (e.g., dissolved oxygen, temperature, and pH) in real time [14
]. With the design of monitoring systems for water quality in aquaculture (including sensors for the aforementioned parameters), based on WSN with the transmission protocol ZigBee, it is possible to analyze and manage information of the environment around the organisms, which can help the farm manager to make decisions regarding aspects such as the increase of culture density (based on water-quality improvement); supplying the feed ration more adequately, or improving the feed conversion and the growth ratios (based on the interactions between consumption, temperature, and oxygen) [17
]; maintaining the water-quality parameters more effectively [18
]; reducing the eutrophication and hyper-nutrification produced by effluents; preventing the stress and diseases of farmed organisms [19
]; and reducing the need for employees [20
]. Low-cost (LC) monitoring systems for aquaculture are a priority in developing countries, as they can reach most people and provide benefits. Considering the aforementioned reasons, we decided to carry out this study. In this work, the development of a low-cost open-source hardware (LC OSH) for water-quality monitoring using simple electronics components—an RTD (resistance thermometer), pH sensor, and dissolved oxygen electrode—is studied. The present investigation is focused on the implementation and evaluation of a water-quality monitoring system using LC OSH for application in PAq, capable of making graphs and storing data for further processing and management. In such a way, this comprehensive study can allow for a simple design for a portable device fabrication.
The data of the validation test showed no significant differences in any of the measured variables. The mean temperature of the reference equipment was 24.02 °C, while that in the proposed system was 24.05 °C, giving a non-significant difference of 0.03 °C. In the case of dissolved oxygen, the trend was similar, with a mean value of 7.14 mg L−1
in the reference equipment and 7.13 mgL−1
in the proposed system. For pH, the difference was a little greater (0.06), with a mean of 8.18 in the reference equipment and 8.24 in the proposed system. Table 1
shows the values recorded for each variable in the reference equipment and the proposed system. The highest coefficient of variation was observed for DO, with 17.72% to 18.19% of variation, which indicates that those sensors were a little more unstable, due to the water characteristics and the effect of temperature on this parameter. The pH standard deviation in the proposed system was ± 0.11; a similar value of ± 0.21 when using a wireless water-quality monitor has been reported [25
shows the results of repetitive tests; no statistically significant differences were found between them (P
A list of the utilized parts is presented in Table 3
. It was fulfilled with the aim of developing a system with a low budget. The proposed system had an overall cost of 455€, in comparison to 1030€ with a similar system on the market (commercial price from personal communication with a local store); this represents a difference of 56% (580€). With the proposed system, there is a cost of approximate 150€ per variable measured. The proposed water-quality monitoring system, therefore, meets the requirement of a low-cost system, due to its price. Furthermore, it enables data logging. As can be seen from a comparison with commercially available equipment, its resolution and accuracy are sufficient for qualitative water-quality monitoring. In general, there is no other related equipment to compare with that presented in this study; however, it could be compared to other recent low-cost systems made with similar hardware, where costs can be compared [26
Information stored in the database can be processed and displayed as a graph, from which the user can understand the status of an aquaculture system in a better way, and can correct problems or make decisions as soon as possible. Figure 6
shows these results in a graphical way, showing that the application of both systems in aquaculture is feasible and gives reliable information. The comparison between the reference equipment against the proposed LC OSH system follows the same pattern: the differences in the median values between groups are not great enough to exclude the possibility that the difference is due to random sampling variability and statistically significant differences (P
< 0.05) were determined by Student’s t-tests.
For Figure 7
= 38 samples were taken completely at random and a positive linear correlation was obtained with the reference equipment, where it can be seen that the measurement of dissolved oxygen (Figure 7
a) showed very good agreement, obtaining r2
= 0.81; the LC-OSH system average was 4.84 mg L−1
, while that for the reference equipment was 4.82 mg L−1
, representing a difference of only 0.5%. In a similar way, the measurement of pH obtained r2
= 0.72, with an average of 8.26 for the low-cost proposed system and 8.13 for the reference equipment, a difference of 1.5% between their averages. In the case of temperature, r2
= 0.97 was obtained, with an average temperature of 26.13 °C for the low-cost proposed system and 26.09 °C for the reference equipment; thus, their difference was 0.2%.
shows the results of the operation continuity test (reads of DO, temperature, and pH over 5 days), which demonstrate the sturdiness of the proposed LC OSH system, as the equipment worked continuously over 30 days without any problems. This is a very important finding as, in aquaculture, the production cycles are very long (up to one year), and the equipment must work without interruption. The maximum value recorded for dissolved oxygen was 8.3 mgL−1
and the minimum was 8.0 mgL−1
; therefore, it can be noted that the dissolved oxygen was always in a ± 0.3 (maximum–minimum) range, where the oxygen source (blower) was never shut down. It can be noted that, as the temperature increases, oxygen decreases, and vice versa; such variation is due to the solubility of dissolved oxygen being inversely proportional to the temperature [29
Regarding the repeatability test, the statistical analysis showed no significant differences among repetitions in any of the three parameters measured by the proposed LC OSH system. The mean temperatures were 23.66, 23.72, and 23.86 °C for repetitions 1, 2, and 3, respectively. For dissolved oxygen, the means were 6.44, 6.95, and 6.85 mg L−1 for the three repetitions; while, for pH, the mean values were 8.05, 8.37, and 7.94.
For the reliability test (precision, accuracy, and sensibility), the results showed a precision of 0.106 mg L−1
and a coefficient of variation (C.V.) of 0.42% for DO. For pH, the precision was 0.242 and the C.V. was 2.6%. In the case of temperature, the precision was 0.106 °C and the C.V. was 0.421%. Table 4
lists the results for the three parameters.
The above results indicate that the designed equipment can be used as a reliable tool in aquaculture, as the reads were very near to those considered as the true accepted value.
The novelty of the proposed system mainly consists of the application of open-source hardware, which provides new opportunities for the research of in situ sensing in aquaculture systems with high precision and the behavior of cultured organisms (e.g., crustaceans, fish, and shellfish). The system allows for continuous recording and storing of water physicochemical variables for metrological traceability in precision aquaculture applications. The designed low-cost equipment can provide useful information, including under harsh aquaculture environments; therefore, more consideration in care and handling are required. A growing tendency in the development of instruments such as the proposed LC system to evaluate water quality has been observed worldwide, due to the problems faced by modern aquaculture; mainly those related to hygiene and environmental care. In developing countries, as is the case in Latin America, the innovation or development of different monitoring systems using open platforms (software or hardware) is of great interest, as they are cheaper [31
] and can represent savings between 30–60% [26
]; in addition, they have great potential to advance food security [33
There are currently several ways to feed aquaculture crops, one of which considers the physicochemical variables of water—especially temperature and dissolved oxygen—as they have an effect on the food intake of organisms [34
]. For this reason, different platforms have been developed to supply feed based on sensors [35
] and so the proposed LC OSH monitoring system could be very useful for the development of automatic feeders. There are some very important challenges for the improvement of such systems, one of them being calibration: the programming codes could be made even more robust, such that the system can detect patterns associated with the need for re-calibration. The energy used for the evaluation of the proposed LC OSH system was provided by a local electricity supplier; however, some farms in the region do not have access to the electrical grid, due to their geographical location (i.e., difficulty of access or being a large distance from cities) [38
] and, so, it would be appropriate to consider, in the future, powering these systems with solar energy or rechargeable batteries using solar energy. Another challenge is access to the Internet, as mentioned above. The locations of farms make Internet connection complicated although, due to technological advances in communications, there are now more and more tools available for connecting to the Internet [39
], which would be excellent, for example, for adding the proposed LC OSH system to the IoT.
Some feasible improvements to the proposed LC OSH system could be: (1) the inclusion of a monitor of water-quality index, a tool which provides a general overview of the state of contamination of the water column, that can positively help to manage the farm for a better production response [4
]; (2) the incorporation of more water-quality electrodes, such as conductivity, turbidity, and ion-selective electrodes; (3) furthermore, the addition of ion-selective electrodes (e.g., Mg, K, Ca, P, and Na) can be very useful in low-salinity cultures where the ionic balance is a key aspect—for example, some physiological and metabolic functions of shrimp, such as osmoregulation, have been directly related to Mg and K ions [44
]; and (4) the inclusion of a module for the generation of early alerts, which are essential for farmers.