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.

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.

Agricultural research and propagation systems often suffer due to a lack of access to affordable, adaptable, and well-structured technological solutions. Traditional plant growth devices typically rely on ad hoc construction, which limits their scalability, reuse, and adaptability. This study employs a user-centered conceptual design methodology based on product platform development and modular architecture to design a growth chamber for plant cuttings. The approach followed three main phases: (i) identification and classification of user needs, (ii) functional modeling of the base system and its variants, and (iii) architectural modularization through heuristic principles. Interviews with researchers yielded 55 functional requirements, of which 26 were defined as essential. Functional models were developed for both a base system and two variant systems incorporating alternative irrigation and sensing technologies. Heuristic analysis identified independent modules, such as irrigation, lighting, environmental monitoring, and control. Subsequently, block diagrams were used to translate functional logic into spatially coherent conceptual designs. The resulting architecture supports modular integration, reconfiguration, and scalability for diverse experimental needs. This work demonstrates that structured design methodologies, which are commonly used in industrial contexts, can be effectively applied in agricultural research settings to produce solutions that are versatile, low-cost, and have enduring value, offering a pathway for innovation, reproducibility, and technology transfer in resource-limited environments.

Urban agriculture in space-constrained cities requires compact, reproducible propagation systems. Therefore, the aim of this Technical Note is to design, implement, and functionally validate a low-cost, modular hydroponic chamber (SSHG) for early-stage vegetative propagation. This system couples DHT11-based temperature/RH monitoring with rule-based actuation—irrigation 4×/day and temperature-triggered ventilation—under the control of an Arduino Uno microcontroller; LED lighting was not controlled nor analyzed. Two 15-day trials with basil (Ocimum basilicum L.) yielded rooting rates of 61.7% (37/60) and 43.3% (26/60) under a deliberate minimal-input configuration without nutrient solutions or rooting hormones. Environmental summaries and spatial survival maps revealed edge-effect patterns and RH variability that inform irrigation layout improvements. The chamber, bill of materials, and protocol are documented to support replication and iteration. Thus, the SSHG provides a transferable baseline for educators and researchers to audit, reproduce, and improve small-footprint, controlled-environment propagation. Beyond its technical feasibility, the SSHG contributes to sustainability by leveraging low-cost, readily available components, enabling decentralized seedling production in space-constrained settings, and operating under a minimal-input configuration. In line with widely reported hydroponic efficiencies (e.g., lower water use relative to soil-based propagation), this open and replicable platform aligns with SDGs 2, 11, 12, and 13.

Compact, low-power absorption cooling supports decentralized refrigeration needs and is positioned here as a sustainable approach within environmental technologies. This paper presents the design, fabrication, and experimental validation of a compact LiBr–H₂O evaporator–absorber, in which a low-energy fan assists in transporting refrigerant vapor from the evaporator to the absorber within a single vertical falling-film vessel. Twelve heat-load phases were tested with the fan OFF/ON, while temperatures, pressures, and flow rates were continuously monitored. The analysis focuses on temperature and pressure separation metrics, as well as a dimensionless separation index. Results show that fan assistance stabilizes thermal and pressure differentials and attenuates oscillations across grouped loads. The most significant benefits are observed at low to intermediate heat inputs, whereas the effect becomes marginal at higher loads, indicating the dominance of natural transport mechanisms. The compact unit remains thermally stable under all tested conditions. These findings indicate that a simple, low-power mechanical enhancement can improve controllability in an integrated evaporator–absorber without complex internal geometries. Protected under a Mexican utility model (IMPI, MX 4573 B), this prototype provides a replicable experimental basis for supporting compact, low-power solutions for sustainable, decentralized cooling in the field of environmental technologies.

In this study, daily historical records from Mexican weather stations across the country were classified according to corresponding Moon phases at the time of rainfall occurrence: New Moon, Waxing Crescent, First Quarter, Waxing Gibbous, Full Moon, Waning Gibbous, Last Quarter, and Waning Crescent. Out of the 5839 Mexican weather stations analyzed, 2412 met the specified data quality standards, which included a historical daily record period ranging from 30 to 51 years (1960–2011) and a maximum tolerance of 20% missing data. Correlation behavior between Moon phases and historical cumulative rainfall in Mexico was identified at two levels: general and particular. At the general level, the total historical cumulative rainfall by Moon phase was quantified. At the particular level, the correlation patterns between the Moon phases and the highest and lowest historical cumulative rainfall were identified. The results showed that the historical cumulative rainfall was highest at 17.24% during the New Moon and lowest at about 10.01% on average during the Waxing Crescent, First Quarter, and Waning Crescent phases (with 9.64% as the lowest value). During the Waxing Gibbous, Full Moon, and Waning Gibbous phases, rainfall remained at average values of approximately 13.18%. At 89.09% of the weather stations, the rainiest Moon phase was New Moon, and at 56.05%, the least rainy was Waning Crescent. In a few geographical areas, there are clearly defined patterns, which is atypical, given that in other geographical areas, the patterns are typically not so evident. This work demonstrates remarkable and strong correlation behavior between Moon phases and historical cumulative rainfall in Mexico.