Knowing the soil moisture available to plants is important for adequate management of water use in agricultural farms, with automated methods being the most accurate. However, acquisition costs are high and most of the commercially available irrigation controllers still work using pre-set times. This study aimed to develop and calibrate a low-cost automated tensiometer with high efficiency in irrigation control, based on real-time monitoring. The research was conducted at the Laboratories of Hydraulics and of Water Soil Plant and Atmosphere Relationship, which belong to the Federal University of Grande Dourados (UFGD), in Dourados, MS, Brazil, with soil classified as an Oxisol. Pressure transducers and a microcontroller were used to assimilate the pressure inside tensiometers and transform it into readings of soil water matric potential (Ψm). Thus, the calibration was carried out by comparing the different readings of the transducer and digital tension meter. Different tensions were applied to obtain a soil moisture curve, starting from the most humid point (saturated) to the driest one (oven-dried soil), collecting 20 valid points. Subsequently, the data were subjected to the normality test, with subsequent statistical analysis and regression curve models. Linear adjustments with a high coefficient of determination (R2 = 0.99) were observed, with the automated system built in this study being capable of monitoring soil water tension in real-time.
A common scenario in the developing countries is the low income and less education of producers. Thus, the tools used for irrigation management must be cheap and easy to handle. In this work, an autonomous and low-cost network of micro-weather stations has been developed for irrigation management. Simulations were performed to evaluate the ability of intelligent systems to compute evapotranspiration with noisy and insufficient data. The network of micro-weather stations was then applied to autonomous irrigation management of a crop of bell peppers. Statistical analysis was performed on data from the developed system and a standard weather station. The results show no statistical difference between the values of evapotranspiration calculated with data from these two sources. The developed system performed with a coefficient of determination of 0.968, mean absolute error of 0.055 mm day −1 , and root mean square error of 0.063 mm day −1 . The study shows that low-cost intelligent systems can be used as viable tools for efficient irrigation management.
Surface runoff monitoring is important for the sustainable management of global water resources. Obtaining a practical and inexpensive method for collecting data in the field can help to better understand surface runoff and its effects, necessary for the management of watersheds. This study sought to elaborate the calibration curves of the ultrasonic sensor due to temperature variability, verifying the inaccuracy of the distance between objects and the sensor, and determining the feasibility of using low-cost sensors in an in-loco experiment installed on Parshall flumes. The experiment was conducted on the Experimental Farm of the Federal University of Grande Dourados, Dourados, MS, Brazil. The data were collected by twelve HC-SR04 ultrasonic distance sensors , which were coupled to a data acquisition system composed of an expansion board connected to a Raspberry minicomputer. Sensor calibration using temperature data resulted in the error correction of ± 8.0 mm of distance reading. On the other hand, the R2 of the comparison curves between sensor and control system (laser distance meter and ruler in the flume) resulted in high values (above 0.95), showing the feasibility of its use and meeting the specifications for use in the field subject to weather conditions. This study demonstrates the performance of ultrasonic sensors as a potential for new application to evaluate surface runoff aiming to propose new runoff coefficients.
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