In this work, an exhaustive revision is given of the literature associated with advanced information and communication technologies in agriculture within a window of 25 years using bibliometric tools enabled to detect of the main actors, structure, and dynamics in the scientific papers. The main findings are a trend of growth in the dynamics of publications associated with advanced information and communication technologies in agriculture productivity. Another assertion is that countries, like the USA, China, and Brazil, stand out in many publications due to allocating more resources to research, development, and agricultural productivity. In addition, the collaboration networks between countries are frequently in regions with closer cultural and idiomatic ties; additionally, terms’ occurrence are obtained with Louvain algorithm predominating four clusters: precision agriculture, smart agriculture, remote sensing, and climate smart agriculture. Finally, the thematic-map characterization with Callon’s density and centrality is applied in three periods. The first period of thematic analysis shows a transition in detecting the variability of a nutrient, such as nitrogen, through the help of immature georeferenced techniques, towards greater remote sensing involvement. In the transition from the second to the third stage, the maturation of technologies, such as unmanned aerial vehicles, wireless sensor networks, and the machine learning area, is observed.

This study examines the potential of machine learning (ML) and deep learning (DL) techniques for classifying microplastics using Fourier-transform infrared (FTIR) spectroscopy. Six commonly used industrial plastics (PET, HDPE, PVC, LDPE, PP, and PS) were analyzed. A significant contribution of this research is the use of broader and more varied spectral ranges than those typically reported in the state of the art. Furthermore, the impact of different normalization techniques (Min-Max, Max-Abs, Sum of Squares, and Z-Score) on classification accuracy was evaluated. The study assessed the performance of ML algorithms, such as k-nearest neighbors (k-NN), support vector machines (SVM), naive Bayes (NB), random forest (RF), and artificial neural networks architectures (including convolutional neural networks (CNNs) and multilayer perceptrons (MLPs)). Models were trained and validated using the FTIR-PLASTIC-c4 dataset with a 10-fold cross-validation approach to ensure robustness. The results showed that Z-score normalization significantly improved stability and generalization across most models, with CNN, MLP, and RF achieving near-perfect values in accuracy, precision, recall, and F1-score. In contrast, the sum of squares normalization was less effective, particularly for CNNs, due to its sensitivity to scale and data distribution. Notably, naive Bayes consistently underperformed because of its limitations in analyzing complex spectral data. The findings highlight the effectiveness of FTIR spectra with broad and variable ranges for the automated classification of microplastics using ML techniques, along with appropriate normalization methods.

This paper presents an optimization modeling approach to support strategic planning for designing hydrogen supply chain (HSC) networks. The energy source for hydrogen production is proposed to be electricity generated at Mexican sugar factories. This study considers the utilization of existing infrastructure in strategic areas of the country, which brings several advantages in terms of possible solutions. This study aims to evaluate the economic and environmental implications of using biomass wastes for energy generation, and its integration to the national energy grid, where the problem is addressed as a mixed-integer linear program (MILP), adopting maximization of annual profit, and minimization of greenhouse gas emissions as optimization criteria. Input data is provided by sugar companies and the national transport and energy information platform, and were represented by probability distributions to consider variability in key parameters. Independent solutions show similarities in terms of resource utilization, while also significant differences regarding economic and environmental indicators. Multi-objective optimization was performed by a genetic algorithm (GA). The optimal HSC network configuration is selected using a multi-criteria decision technique, i.e., TOPSIS. An uncertainty analysis is performed, and main economic indicators are estimated by investment assessment. Main results show the trade-off interactions between the HSC elements and optimization criteria. The average internal rate of return (IRR) is estimated to be 21.5% and average payback period is 5.02 years.

The areas of application for augmented reality technology are heterogeneous but the content creation tools available are usually single-user desktop applications. Moreover, there is no online development tool that enables the creation of such digital content. This paper presents a framework for the creation of Cultural Entertainment Systems and Augmented Reality, employing cloud-based technologies and the interaction of heterogeneous mobile technology in real time in the field of mobile tourism. The proposed system allows players to carry out a series of games and challenges that will improve their tourism experience. The system has been evaluated in a real scenario, obtaining promising results.