IoT in Agriculture applications have evolved to solve several relevant problems from producers. Here, we describe a component of an IoT-enabled frost prediction system. We follow current approaches for prediction that use machine learning algorithms trained by past readings of temperature and humidity sensors to predict future temperatures. However, contrary to current approaches, we assume that the surrounding thermodynamical conditions are informative for prediction. For that, a model was developed for each location, including in its training information of sensor readings of all other locations, autonomously selecting the most relevant ones (algorithm dependent). We evaluated our approach by training regression and classification models using several machine learning algorithms, many already proposed in the literature for the frost prediction problem, over data from five meteorological stations spread along the Mendoza Province of Argentina. Given the scarcity of frost events, data was augmented using the Synthetic Minority Oversampling Technique (SMOTE). The experimental results show that selecting the most relevant neighbors and training the models with SMOTE reduces the prediction errors of both regression predictors for all five locations, increases the performance of Random Forest classification predictors for four locations while keeping it unchanged for the remaining one, and produces inconclusive results for Logistic regression predictor. These results demonstrate the main claim of these work: that thermodynamic information of neighboring locations can be informative for improving both regression and classification predictions, but also are good enough to suggest that the present approach is a valid and useful resource for decision makers and producers.

Global warming effects are seen around the world and Latin American countries are not an exception, especially for expanding drought areas. Therefore, underground water resources used in the region are incrementing exponentially. However, temporal and spatial underground water information concerning availability and quality is scarce, disabling proper decision making. In order to close that breach, we propose and embedded edge-processing Internet of Things (IoT)-based water quality monitoring system. This letter introduces the design and implementation of this solution, specifically targeted to monitor irrigation and drinking water extracted from water wells. The system is designed to be deployed in central Chile, considering the topographic conditions, which severely affect power availability and communication resources. The captured data are stored in a data lake, for further processing according to water quality models.

Agriculture continues to be one of the world’s main sources of income and provides great environmental, territorial and social value. However, frost is a recurring problem for farmers each year, representing a significant threat to agricultural production. In a matter of hours, temperatures below the freezing point can result in the loss of nearly the entire crop from a producer. In this article, we have analyzed and compared the application of a set of machine learning algorithms to predict the occurrence of frost events in the next 24 hours. The prediction process covers several challenges, such as data capture, processing, extracting each relevant parameter and finally building different prediction models to compared their performance. Furthermore, we have employed the Synthetic Minority Oversampling Technique (SMOTE) methodology to address the issue of imbalanced datasets, given the natural scarcity of frost events during the data sampling period. Our results show that among the machine learning algorithms we compared, the most efficient in terms of Recall score is K-Nearest Neighbor (KNN), while using the Area Under Curve (AUC) criteria, the highest score belongs to the Extra Trees algorithm, with 0.9909. Moreover, by applying the SMOTE balancing process, the AUC score of our models increased 13%, while the Recall score increased from 55% to 82%.

Low Power Wide Area Networks (LPWAN) are expected to enable the massive connectivity of small and constrained devices to the Internet of Things. Due to the restricted nature of both end devices and network links, LPWAN technologies employ network stacks where there is no interoperable network layer as a general case; instead, application data are usually placed directly into technology-specific two-layer frames. Besides not being able to run standard IP-based protocols at the end device, the lack of an IP layer also causes LPWAN segments to operate in an isolated manner, requiring middleboxes to interface non-IP LPWAN technologies with the IP world. The IETF has standardized a compression and fragmentation scheme, called Static Context Header Compression and Fragmentation (SCHC), which can compress and fragment IPv6 and UDP headers for LPWAN in a way that enables IP-based communications on the constrained end device. This article presents a model to determine the channel occupation efficiency based on the transmission times of SCHC messages in the upstream channel of a LoRaWAN™ link using the ACK-on-Error mode of standard SCHC. The model is compared against experimental data obtained from the transmission of packets that are fragmented using a SCHC over LoRaWAN implementation. This modeling provides a relationship between the channel occupancy efficiency, the spreading factor of LoRa™, and the probability of an error of a SCHC message. The results show that the model correctly predicts the efficiency in channel occupation for all spreading factors. Furthermore, the SCHC ACK-on-Error mode implementation for the upstream channel has been made fully available for further use by the research community.

This study aims to provide valuable insights into consumer preferences for delivery services in online shopping in Chile. The COVID-19 pandemic has accelerated the evolution of delivery and logistics services, leading to increased competition among online stores. Chile, with its highly digitally enabled population and a competitive landscape of online retailers, serves as an ideal reference case for Latin America. By analyzing key delivery attributes such as delivery time, order arrival time range, compensation policies for delivery delays, and delivery prices, we offer valuable insights into consumer behavior. These insights will, in turn, inform the formulation of effective strategies within the online shopping industry. We examine the following aspects: (a) The willingness of consumers to pay for the service attributes; (b) The relative importance assigned to these attributes by consumers; and (c) The relationship between consumer preferences and socioeconomic characteristics. Using Multinomial Logit Models and a database from a Discrete Choice Experiment, we have discovered that the most significant attributes of delivery service are the time until product arrival and the existence of compensation in case of delivery delays. Additionally, we found that consumers are willing to pay more for the same delivery service if the product is large, as large products generally have higher prices. Furthermore, we observed that delivery time preferences vary by gender and for small products, and price sensitivity varies according to educational level, household size, and socioeconomic status. To the best of our knowledge, no previous research of this kind has been conducted for Chile.