One of the main concerns in precision agriculture (PA) is the growth of weeds within a crop field. Currently, to prevent the spread of weeds, automatic techniques and computational tools are used to help to identify, classify, and detect the different types of weeds found in agricultural fields. One of the technologies that can help us to process digital information gathered from the agricultural fields is high-performance computing (HPC); this technology has been adopted to carry out projects requiring extra processing and storage in order to execute tasks with a large computational cost. This paper shows the implementation of an HPC cluster (HPCC), in which image processing (IP) and analysis are executed using deep learning (DL) techniques, specifically, convolutional neural networks (CNNs) with the VGG16 and InceptionV3 models, to classify different weed species. The results show the great benefits of using high-performance computing clusters in PA, specifically for classifying images. To apply distributed computing within the HPCC, the Keras and Horovod frameworks were used to train the CNN models, obtaining the best time with the InceptionV3 model with a value of 37 min 55.193 s using six HPCC cores, obtaining an accuracy of 0.65 as a result.

This Technical Note presents the design and implementation of a low-cost modular growth chamber developed to keep mother plants under controlled environmental conditions for vegetative propagation. The system was conceived as an accessible alternative to expensive commercial equipment, offering reproducibility and adaptability for small-scale and research-based cultivation. The proposed chamber integrates thermal insulation, LED lighting, forced ventilation through the implementation of extractors, a recirculating irrigation system with double filtration, and a sensor-based environmental monitoring platform operated via an Arduino UNO microcontroller. The design features a removable tray that serves as a support for the mother plant, an observation window covered by a movable dark acrylic that prevents the passage of external light, and a vertical structure that facilitates optimal space utilization and ergonomic access. Functionality was conducted using a Stevia rebaudiana Bertoni mother plant maintained for 30 days under monitored conditions. Environmental parameters—temperature, relative humidity, and illuminance—were recorded continuously. The plant showed vegetative development through new shoot emergence and the growth in height of the plant, and despite a loss in foliage expansion, it confirmed the chamber’s capacity to support sustained growth. Although no statistical replication or control group was included in this preliminary evaluation, the system demonstrates technical feasibility and practical utility. This chamber provides a replicable platform for future experimentation and propagation studies. Complete technical specifications, schematics, and component lists are provided to enable replication and further development by other researchers. The growth chamber design aligns with the goals of open-source agricultural innovation and supports knowledge transfer in controlled-environment plant propagation technologies.

Agriculture is an area that currently benefits from the use of new technologies and techniques, such as artificial intelligence, to improve production in crop fields. Zacatecas is one of the states producing the most onions in the northeast region of Mexico. Identifying and determining vegetation, soil, and humidity zones could help solve problems such as irrigation demands or excesses, identify spaces with different levels of soil homogeneity, and estimate the yield or health of the crop. This study examines the application of artificial intelligence through the use of deep learning, specifically convolutional neural networks, to identify the patterns that can be found in a crop field, in this case, vegetation, soil, and humidity zones. To extract the mentioned patterns, the K-nearest neighbor algorithm was used to pre-process images taken using unmanned aerial vehicles and form a dataset composed of 3672 images of vegetation, soil, and humidity (1224 for each class). A total of six convolutional neural network models were used to identify and classify the patterns, namely Alexnet, DenseNet, VGG16, SqueezeNet, MobileNetV2, and Res-Net18. Each model was evaluated with the following validation metrics: accuracy, F1-score, precision, and recall. The results showed a variation in performance between 90% and almost 100%. Alexnet obtained the highest metrics with an accuracy of 99.92%, while MobileNetV2 had the lowest accuracy of 90.85%. Other models, such as DenseNet, VGG16, SqueezeNet, and ResNet18, showed an accuracy of between 92.02% and 98.78%. Furthermore, our study highlights the importance of adopting artificial intelligence in agriculture, particularly in the management of onion fields in Zacatecas, Mexico. The findings can help farmers and agronomists make more informed and efficient decisions, which can lead to greater production and sustainability in local agriculture.

The prediction of concrete compressive strength (CSMPa) is fundamental in experimental civil engineering as it enables the optimization of mix design and complements laboratory testing through predictive tools. This study presents a systematic and reproducible methodology for comparing eight regression algorithms—including linear models, neural networks, and boosting methods—applied to three experimental datasets that represent different types of concrete: high-performance concrete (HPC), conventional concrete, and recycled-aggregate concrete (RAC). In order to make such comparison, some performance metrics were calculated (RMSE, MAE, MAPE, $R^2$, and nRMSE) through hyperparameter optimization using RandomizedSearchCV and homogeneous cross-validation. The boosting methods achieved the best performance, with CatBoost standing out by reaching $R^2$ values between 0.92 and 0.95 and RMSE between 3.4 and 4.4 MPa, confirming its inter-dataset stability and generalization capability. These results indicate consistent predictive accuracy across concretes of different compositions and production contexts. As an applied contribution, three interactive inference systems were developed in Google Colab to estimate CS from mix parameters, promoting reproducibility, open access, and practical use in quality-control processes.

Artificial intelligence has experienced tremendous growth in various areas of knowledge, especially in computer science. Distributed computing has become necessary for storing, processing, and generating large amounts of information essential for training artificial intelligence models and algorithms that allow knowledge to be created from large amounts of data. Currently, cloud services offer products for running distributed data training, such as NVIDIA Deep Learning Solutions, Amazon SageMaker, Microsoft Azure, and Google Cloud AI Platform. These services have a cost that adapts to the needs of users who require high processing performance to perform their artificial intelligence tasks. This study highlights the relevance of distributed computing in image processing and classification tasks using a low-scalability distributed system built with devices considered obsolete. To this end, two of the most widely used libraries for the distributed training of deep learning models, PyTorch’s Distributed Data Parallel and Distributed TensorFlow, were implemented and evaluated using the ResNet50 model as a basis for image classification, and their performance was compared with modern environments such as Google Colab and a recent Workstation. The results demonstrate that even with low scalability and outdated distributed systems, comprehensive artificial intelligence tasks can still be performed, reducing investment time and costs. With the results obtained and experiments conducted in this study, we aim to promote technological sustainability through device recycling to facilitate access to high-performance computing in key areas such as research, industry, and education.

Standard maturity methods for concrete monitoring rely primarily on temperature history, often neglecting the influence of internal relative humidity (RH) on hydration kinetics and self-desiccation risks. Continuous in situ monitoring of internal RH remains a challenge due to the high cost, proprietary nature, and lack of reproducibility of existing solutions. This study evaluates a low-cost, open-source embedded sensor array designed to characterize early-age curing behavior through trend-based monitoring—defined here as the evaluation of ensemble consistency and repeatability rather than absolute metrological traceability. The prototype system, based on SHT31 sensors controlled by an ESP32 microcontroller, was embedded in high-performance concrete cylinders (f′c = 45 MPa) to capture the exothermic hydration peak and the equilibration of internal humidity. Results demonstrate that while the sensor encapsulation introduced a geometric disturbance that reduced compressive strength by approximately 25%—a limitation requiring mitigation in structural applications—the system successfully captured reproducible curing transitions. The proposed framework provides an accessible tool for experimental research into internal curing conditions, offering a digital complement to traditional surface-based quality control.