Rural communities usually settle in territories where crop self-consumption is the main source of sustenance. In this context, climate change has made these environments of crop control susceptible to water shortages, impacting crop yields. The implementation of greenhouses has been proposed to address these problems, together with strategies to optimize water and energy consumption. In this study, an energy–water management system based on a model predictive control strategy is proposed. This control strategy consists of a fuzzy optimizer used to determine the optimal consumption from isolated microgrids considering the local resources available. The proposed controller is implemented on two timescales. First, medium-term optimization over one month is used to estimate the necessary water demand required to support crop growth and a high yield. Second, short-term optimization is used to determine the optimal climate conditions inside the greenhouse for managing crop irrigation, refilling the reserve water tank, and providing ventilation. Experiments were conducted to test this approach using a case study of an isolated community. For such a case, energy consumption was reduced, and the irrigation process was optimized. The results indicated that the proposed controller is a viable alternative for implementing intelligent management systems for greenhouses.

This article proposes a monitoring system that allows to track transitions between different stages in the berry harvesting process (berry picking, waiting for transport, transport and arrival at the packing site) solely using information from temperature and vibration sensors located in the basket. The monitoring system assumes a characterization of the process based on hidden Markov models and uses the Viterbi algorithm to perform inferences and estimate the most likely state trajectory. The obtained state trajectory estimate is then used to compute a potential damage indicator in real time. The proposed methodology does not require information about the weight of the basket to identify each of the different stages, which makes it effective and more efficient than other alternatives available in the industry.

In some important berry-producing countries, such as Chile, the fruit is harvested manually. The markets for these products are generally very distant, and any damage caused to the fruit during harvesting will be expressed in its shelf life. The first step to understanding the harvesting process is to identify what happens to the harvest baskets in each stage (picking, wait-full, transport-full, freezing tunnel, emptying and transport-empty), allowing variables that can affect the shelf life to be identified. This article proposes the use of Smartbins, intelligent harvest baskets with sensors to collect weight, temperature, and vibration data. Combined analysis of the variables collected, using machine learning algorithms, allows the system to estimate which stage the basket is at with an accuracy of 80%, and to assess whether the fruit has been exposed to situations that could affect its shelf life. Due to imbalance characteristics of the data collected, the best results were obtained in longer stages (picking and wait-full stages with 89% and 86% respectively).

In order to ascertain the spatial and temporal changes in the air quality in Temuco City, Chile, we created and installed a network of inexpensive sensors to detect PM2.5 particulate matter. The 21 measurement points deployed were based on a low-cost Sensiron SPS30 sensor, complemented with temperature and humidity sensors, an Esp32 microcontroller card with LoRa and WiFi wireless communication interface, and a solar charging unit. The units were calibrated using an airtight combustion chamber with a Grimm 11-E as a reference unit. The calibration procedure fits the parameters of a calibration model to map the raw low-cost particle-material measurements into reliable calibrated values. The measurements showed that the concentrations of fine particulate material recorded in Temuco present a high temporal and spatial variability. In critical contamination episodes, pollution reaches values as high as 354 µg/m${}^3$, and at the same time, it reaches 50 µg/m${}^3$ in other parts of the city. The contamination episodes show a similar trend around the city, and the peaks are in the time interval from 07:00 PM to 1:00 AM. In the winter, this time of day coincides with when families are usually home and there are low temperatures outside.

There is a disparity in access to new technologies between urban and rural areas, being more noticeable in indigenous communities. This work shows the participatory development of IoT greenhouses in two rural elementary schools. We developed participatory workshops to involve the school students in co-designing and implementing the technological IoT greenhouses. The topics of the workshops were water, energy, and IoT sensors. The early involvement of the children allows us to create IoT kits composed of wireless sensors and actuators and deploy them into the greenhouses. The resulting kits are commanded via a web server, in which the student can access dashboards with real-time data, including ground temperatures and humidity in different greenhouse sectors, ambient temperature and humidity, command the irrigation, and view the total amount of water used. The resulting outcome has a significant impact in the long term because of the transforming experience for the school students.