In Oaxaca, Mexico, there are more than 30 species of the Agave genus, and its cultivation is of great economic and social importance. The incidence of pests, diseases, and environmental stress cause significant losses to the crop. The identification of damage through non-invasive tools based on visual information is important for reducing economic losses. The objective of this study was to evaluate and compare five deep learning models: YOLO versions 7, 7-tiny, and 8, and two from the Detectron2 library, Faster-RCNN and RetinaNet, for the detection and classification of Agave angustifolia plants in digital images. In the town of Santiago Matatlán, Oaxaca, 333 images were taken in an open-air plantation, and 1317 plants were labeled into five classes: sick, yellow, healthy, small, and spotted. Models were trained with a 70% random partition, validated with 10%, and tested with the remaining 20%. The results obtained from the models indicate that YOLOv7 is the best-performing model, in terms of the test set, with a mAP of 0.616, outperforming YOLOv7-tiny and YOLOv8, both with a mAP of 0.606 on the same set; demonstrating that artificial intelligence for the detection and classification of Agave angustifolia plants under planting conditions is feasible using digital images.

Background/Objectives: Arboviral diseases share common vectors, geographic distribution, and symptoms. Developing Machine Learning diagnostic tools for co-circulating arboviral diseases faces data-scarcity challenges. This study aimed to demonstrate that proof of concept using synthetic data can establish computational feasibility and guide future real-world validation efforts. Methods: We assembled a synthetic dataset of 28,000 records, with 7000 for each disease—Dengue, Zika, and Chikungunya—plus Influenza as a negative control. These records were obtained from the existing literature. A binary matrix with 67 symptoms was created for detailed statistical analysis using Odds Ratios, Chi-Square, and symptom-specific conditional prevalence to validate the clinical relevance of the simulated data. This dataset was used to train and evaluate various algorithms, including Multi-Layer Perceptron (MLP), Narrow Neural Network (NN), Quadratic Support Vector Machine (QSVM), and Bagged Tree (BT), employing multiple performance metrics: accuracy, precision, sensitivity, specificity, F1-score, AUC-ROC, and Cohen’s kappa coefficient. Results: The dataset aligns with the PAHO guidelines. Similar findings are observed in other arboviral databases, confirming the validity of the synthetic dataset. A notable performance across all evaluated metrics was observed. The NN model achieved an overall accuracy of 0.92 and an AUC above 0.98, with precision, sensitivity, and specificity values exceeding 0.85, and an average Uniform Cohen’s Kappa of 0.89, highlighting its ability to reliably distinguish between Dengue and Influenza, with a slight decrease between Zika and Chikungunya. Conclusions: These models could accelerate early diagnosis of arboviral diseases by leveraging encoded symptom features for Machine Learning and Deep Learning approaches, serving as a support tool in regions with limited healthcare access without replacing clinical medical expertise.

This work presents a synthetic binary database of Dengue, Zika, Chikungunya, and Influenza constructed entirely from clinical information extracted from the scientific literature. Due to the limited availability and heterogeneity of clinical records in medical units—particularly for arboviral diseases—existing datasets are often insufficient for developing robust Machine Learning models. To address this limitation, an extensive search of PubMed and Google Scholar was conducted between February 2024 and May 2025, following strict selection criteria focused on diagnostic confirmation. The resulting dataset comprises 48,214 records and 67 standardized signs and symptoms, homogenized across all pathologies. Each record is fully binary, contains no missing values, and represents symptom presence or absence. The composition includes 22,379 Dengue records, 7135 Zika records, 7959 Chikungunya records, and 10,741 Influenza records. Symptom prevalence was analyzed, revealing consistency with patterns reported in epidemiological and clinical studies, supporting the dataset’s plausibility. This database enables statistical exploration and direct integration into Machine Learning pipelines without the need for imputation. It has been used in an in silico predictive study of arboviral diseases, employing Influenza as a negative control, and serves as a reproducible, literature-derived resource for computational modeling.

The article analyzes scientific information concerning the effects of computer science on the creative industries, an approach that has been consolidated as a driver of the global economy fundamentally based on knowledge, innovation, and creativity. A bibliometric review of articles in the Scopus database (1983–November 2025) was applied to evaluate the conceptual evolution, fundamental themes, and most influential authors. The research was developed in three phases: (1) search criteria within the research field, (2) perfor-mance analysis, and (3) results analysis. The results showed a steady increase in the pro-duction of studies, particularly since 2019 due to the COVID-19 pandemic, focusing pri-marily on the areas of digitalization, innovation, and artificial intelligence. The authors with the highest number of publications originate from China, Indonesia, and Malaysia. The research determines the convergence between computing and creativity, which con-stitutes a strategic opportunity for the global economy. However, it acknowledges re-strictions linked to the period of analysis and the dependence on a single database, thus suggesting future studies be expanded to include other sources and temporal contexts.

El agave tiene gran relevancia cultural, económica y social en México. Su importancia es notable en el estado de Oaxaca, donde se concentran diversas especies utilizadas en la producción de mezcal, alimentos y fibras. Este estudio presenta un análisis preliminar para la clasificación de tres variedades: Agave angustifoliaHaw, Agave rhodacanthaTrel y Agave marmorata Roezl, mediante una arquitectura de redes neuronales profundas ResNet-34. Se recolectaron 3,871 imágenes de plantas en tres parcelas de los Valles Centrales de Oaxaca. El entrenamiento del modelo durante 50 épocas mostró una alta precisión, recall y exactitud, alcanzando valores máximos de 99,87% en validación. Los resultados confirman la viabilidad de las redes neuronales profundas para la agricultura de precisión y constituyen una contribución al desarrollo de bases de datos multiclase de agave. Esto sirve como punto de partida para optimizar futuros modelos, fortalecer la producción y preservación de esta especie en la región.