Water is the main limiting factor in agricultural production as well as a scarce resource that needs to be optimized. The measurement of soil water with sensors is an efficient way for optimal irrigation management. However, commercial sensors are still too expensive for most farmers. This paper presents the design, development and calibration of a new capacitive low-cost soil moisture sensor that incorporates SDI-12 communication, allowing one to select the calibration equation for different soils. The sensor was calibrated in three different soils and its variability and accuracy were evaluated. Lower but cost-compensated accuracy was observed in comparing it with commercial sensors. Field tests have demonstrated the temperature influence on the sensor and its capability to efficiently detect irrigation and rainfall events.

The crop water stress index (CWSI) is one of the parameters measured in deficit irrigation and it is obtained from crop canopy temperature. However, image segmentation is required for non-leaf region exclusion in temperature measurement, as it is critical to obtain the temperature values for the calculation of the CWSI. To this end, two image-segmentation models based on support vector machine (SVM) and deep learning have been studied in this article. The models have been trained with different parameters (encoder depth, optimizer, learning rate, weight decay, validation frequency and validation patience), and several indicators (accuracy, precision, recall and F₁ score/dice coefficient), as well as prediction, training and data preparation times are discussed. The results of the F₁ score indicator are 83.11% for SVM and 86.27% for deep-learning models. More accurate results are expected for the deep-learning model by increasing the dataset, whereas the SVM model is worthwhile in terms of reduced data preparation times.

The appearance of model-driven engineering (MDE) has invigorated research on domain-specific languages (DSLs) and automatic code generation. MDE uses models to build software, thereby displacing source code as the development process's main feature. DSLs provide easy, intuitive descriptions of the system using graphic models. In this new context, DSLs facilitate work in the first design stages. In addition, MDE helps reduce DSL development costs. It therefore represents a synergistic union that can significantly improve software development.

The Coastal Ocean Observation System of Murcia Region (OOCMUR) was established in 2008 as a major scientific and technological infrastructure in Spain with the main objective of studying the impact of global climate change in the Mediterranean. The coastal lagoon of Mar Menor in southeast Spain was chosen as the first region to be monitored because it is one of the most hypersaline coastal lagoons in the Mediterranean, with a limited exchange of water with the open sea, and it is the largest in Europe. Wireless sensor networks (WSNs) offer an efficient and innovative solution for oceanographic monitoring, allowing a higher density sensor deployment, at a lower cost. This paper presents the design of an ad hoc WSN system and a control software for Mar Menor monitoring using a buoy structure with sensors, energy harvesting, and communications platform. The study focuses on the oceanographic interest of the selected marine area, details of network deployment, the custom-designed sensor nodes, and the results of system operation.