During the growing season, olives progress through nine different phenological stages, starting with bud development and ending with senescence. During their lifespan, olives undergo changes in their external color and chemical properties. To tackle these properties, we used hyperspectral imaging during the growing season of the olives. The objective of this study was to develop a lightweight model capable of identifying olives in the hyperspectral images using their spectral information. To achieve this goal, we utilized the hyperspectral imaging of olives while they were still on the tree and conducted this process throughout the entire growing season directly in the field without artificial light sources. The images were taken on-site every week from 9:00 to 11:00 a.m. UTC to avoid light saturation and glitters. The data were analyzed using training and testing classifiers, including Decision Tree, Logistic Regression, Random Forest, and Support Vector Machine on labeled datasets. The Logistic Regression model showed the best balance between classification success rate, size, and inference time, achieving a 98% F1-score with less than 1 KB in parameters. A reduction in size was achieved by analyzing the wavelengths that were critical in the decision making, reducing the dimensionality of the hypercube. So, with this novel model, olives in a hyperspectral image can be identified during the season, providing data to enhance a farmer’s decision-making process through further automatic applications.

The proposal of this paper is to introduce a low-level blockchain marketplace, which is a blockchain where participants could share its power generation and demand. To achieve this implementation in a secure way for each actor in the network, we proposed to deploy it over efficient and generic low-performance devices. Thus, they are installed as IoT devices, registering measurements each fifteen minutes, and also acting as blockchain nodes for the marketplace. Nevertheless, it is necessary that blockchain is lightweight, so it is implemented as a specific consensus protocol that allows each node to have enough time and computer requirements to act both as an IoT device and a blockchain node. This marketplace will be ruled by Smart Contracts deployed inside the blockchain. With them, it is possible to make registers for power generation and demand. This low-level marketplace could be connected to other services to execute matching algorithms from the data stored in the blockchain. Finally, a real test-bed implementation of the marketplace was tested, to confirm that it is technically feasible.

Because spectral technology has exhibited benefits in food-related applications, an increasing amount of effort is being dedicated to develop new food-related spectral technologies. In recent years, the use of remote sensing or unmanned aerial vehicles for precision agriculture has increased. As spectral technology continues to improve, portable spectral devices become available in the market, offering the possibility of realising in-field monitoring. This study demonstrates hyperspectral imaging and spectral olive signatures of the Manzanilla and Gordal cultivars analysed throughout the table-olive season from May to September. The data were acquired using an in-field technique and sampled via a non-destructive approach. The olives were monitored periodically during the season using a hyperspectral camera. A white reference was used to normalise the illumination variability in the spectra. The acquired data were saved in files named raw, normalised, and processed data. The normalised data were calculated by the sensor by correcting the white and black levels using the acquired reflectance values. The olive spectral signature of the images is saved in the processed data files. The images were labelled and processed using an algorithm to retrieve the olive spectral signatures. The results were stored as a chart with 204 columns and ‘n’ rows. Each row represents the pixel of an olive in the image, and the columns contain the reflectance information at that specific band. These data provide information about two olive cultivars during the season, which can be used for various research purposes. Statistical and artificial intelligence approaches correlate spectral signatures with olive characteristics such as growth level, organoleptic properties, or even cultivar classification.

Heating, Ventilation, and Air Conditioning (HVAC) systems are crucial in the hotel sector, accounting for a significant share of energy consumption, and their unexpected failures are costly. Predictive maintenance offers an effective strategy to reduce these costs. In particular, HVAC chillers contribute substantially to overall energy consumption in hotels, and their sudden failures can result in significant expenses. This study presents a hybrid framework comprising a Fuzzy-based layer and an Artificial Neural Network-based layer, providing a practical, flexible, and interpretable solution for the predictive maintenance of hotel HVAC chillers. The framework addresses challenges related to sensor limitations, data scarcity, and system heterogeneity. The fuzzy logic (FL) layer incorporates expert knowledge through rule-based reasoning, generating meaningful outputs even without historical failure data, which is critical given the infrequent occurrence of HVAC failures. However, an FL-only system cannot adapt to the specific context of each hotel. The ANN-based layer enables continual adaptation through retraining guided by technician feedback. Each output r j ∈ [ 0 , 1 ] represents the estimated risk of an upcoming failure in a chiller component and triggers an alert when r j exceeds its threshold T j . The primary contribution of this work is the development of a flexible hybrid framework that integrates expert-driven fuzzy reasoning with data-driven ANN adaptation to estimate failure risk in hotel HVAC chillers, even under sensor limitations and scarce fault data. The system demonstrated strong performance with synthetic data and was validated in a five-star hotel in Seville, Spain.