The amount of water required to irrigate, is essential in agricultural planning. In Mexico the water required for irrigation is generally not estimated when it is carried out despite several estimation methods being available (direct and indirect). However, some methods can be very expensive, requiring preparation time to use them or time to obtain the results. One of the methods involves using sensors based on relative permittivity. This method have been widely used in agriculture because they show the percentage of water contained in the substrate. However, this value helps the producer know the soil moisture status of their crop in percentage units but does not tell them how much water needs to be added to each plant in liters. Knowing this value could help reduce water losses due to infiltration, thereby increasing the crop area. Therefore, it was developed a device capable of recommending the amount of water in v/v (volume of water/volume of soil) required to irrigate a crop. The prototype device was based on the BGT-SEC Z2TM sensor and the ATMEGA 2560TM microcontroller. The obtained device was calibrated and a specific model was developed for two types of soil: sandy (with an RMSE of 0.0107) and loamy (with an RMSE of 0.00556). With factory calibration, a RMSE value of 0.0339 was found for the loamy soil and 0.0278 for the sandy soil. In addition, the sensor was tested on strawberry plants with pots covered with and without plastic mulch (using loamy soil). The results on the strawberry plants, indicated that water consumption was best explained by the specific calibration equation for loamy soil covered with plastic mulch (67.8 mL RMSE) and without plastic mulch (82.8 mL RMSE). Finally, it was found that at least two measurements are required to obtain soil moisture average in plastic mulch strawberry pots and 6 measurements in pots without plastic mulch. With the above, it is concluded that the device developed in this study performed adequately during experiments and the sensor worked continuously without failing.
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Quevedo-Nolasco et al. (2024) studied this question.
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