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.

In this study, a novel ten-port waveguide microwave sensor is designed, implemented, calibrated and tested in order to obtain the reflection coefficient magnitude and phase. This reflectometer is based on the well known six-port structure but the number of detectors has been increased to eight in order to improve the sampling procedure of the standing wave present within the waveguide. In addition, a learning method based on neural networks' usage has been implemented for autonomous calibration from the data collected by a vector network analyzer. An automated procedure consisting of a moving sample within a multimode cavity has enabled different reflection coefficients to be obtained. Neural networks have been employed in order to learn the relationship between the actual reflection parameter and the acquired signals from eight power detectors. This novel device has been calibrated with a neural architecture based on radial basis functions and the error of device measurements has been analyzed. This new design and the incorporated neural network calibration allow one to avoid problems caused by fault or nonlinearity of the detectors, and to get robustness, flexibility and adaptability characteristics for the presented device.

The application of deficit irrigation techniques is essential in arid or semi-arid areas of the southeast of Spain, where water is a scarce and very costly resource. However, to apply these techniques, it is necessary to carry out preliminary tests on the specific crop in order to develop the models that allow the optimization of water use while achieving acceptable yields. The system proposed in this article demonstrates the feasibility of using wireless technologies available in most facilities (Wireless Fidelity) to deploy a high-density network of nodes with a variety of heterogeneous sensors to collect data from the soil, plant, and atmosphere. The data are sent and stored in a cloud server for real-time visualization from any mobile device and further analysis. The nodes have been developed using low-cost processors and are equipped with batteries and solar panels, allowing their autonomy to be virtually unlimited, as shown by the consumption studies and tests carried out.

Perishable product losses can occur throughout postharvest handling. Proper monitoring of key environmental conditions during this period is essential for predicting quality losses throughout their shelf life. This paper presents the design and testing of a portable and compact datalogger for the real-time monitoring of environmental conditions throughout the food supply chain. The device developed incorporates high-precision sensors to measure temperature, relative humidity, CO₂ concentration, luminosity and vibrations, as well as wireless communication capabilities for data transmission, simplifying real-time monitoring over existing multi-component systems while keeping costs affordable. Strategies to optimize power consumption allow a month of battery life, being able to cover entire periods of transport and storage, according to the results of the autonomy test performed on the device. The datalogger uses NB-IoT and relies on other wireless communication protocols if not available to send sensor data to a cloud platform. Comparative testing with commercial dataloggers has been carried out to verify correct device measurements, and field testing has validated successful real-time data transmission along an entire refrigerated transport route. The functionality and autonomy of the proposed device meet the needs of live remote monitoring to help reduce food losses.