Coffee and its derivatives are part of the world's gastronomy and are also one of the main export products in Imbabura, Ecuador. Coffee producers and marketers must ensure the quality of their coffee beans, as a single seed can present several morphological defects. However, the selection of quality beans is a manual, tedious, and time-consuming process, prone to misclassification due to human limitations such as fatigue and varying subjective classification criteria. This work aims to detect and classify multiple coffee beans automatically present in an image (both good and bad) using modern convolutional neural networks, specifically YOLOv11 (Nano and Small variants), which are selected for their computational efficiency and good performance in real-time detection tasks. A custom dataset with a total of 4,090 RGB images with multiple coffee beans randomly located within each image was collected and manually labeled into two classes (good and bad) using the CVAT tool. Image preprocessing included background variation (white, gray, and black) to improve dataset variability and model robustness. Models were trained on the Kaggle platform with an NVIDIA Tesla P100 GPU (16 GB VRAM), using the PyTorch framework and the Ultralytics library. Results showed an accuracy of 0.880 for YOLO v11-Nano and 0.871 for YOLO v11-Small, along with inference times of 7.89 ms (126.7 fps) and 10.08 ms (99.20 fps), respectively. These results are comparable to those reported in the literature, but now considering the challenge of superimposing multiple coffee beans randomly located in each image with a variable background. These results suggest the potential for applying the model to embedded systems in practical settings for small and medium-sized producers and associations, thereby contributing to technological innovation in the Ecuadorian coffee sector.

Furrow irrigation is widely practiced in arid and semi-arid regions but suffers from low efficiency due to infiltration variability and water losses. This systematic review analysed 92 peer-reviewed studies (2017–2024) on short-cycle crops, identified through a reproducible search of five bibliographic databases (Scopus, Web of Science, IEEE Xplore, SpringerLink, ScienceDirect) supplemented by a manually directed search of publisher platforms, following Kitchenham & Charters’ protocol. Study quality was assessed narratively, based on validation against field data and comparison with established simulation software (SIRMOD, WinSRFR), rather than through a formal quality-appraisal tool. The narrative synthesis indicates that optimized irrigation techniques achieve efficiency gains ranging from 20% to over 50% relative to conventional practice, depending on crop, soil, and technology, addressing the two operational questions that determine furrow-irrigation performance: when to irrigate and how much water to apply. Advances were identified in coupled surface–subsurface modelling, soil moisture prediction, sensor-driven scheduling, and water–plant–atmosphere interaction. Persistent limitations include scarce field validation beyond single-furrow, single-season conditions, limited generalization across agroclimatic contexts, and interpretability challenges in machine learning (ML) models. Future research should prioritize embedding physical constraints into AI architectures, routine uncertainty quantification, multi-plot and multi-season validation, and pairing technological development with affordable hardware and farmer training to strengthen reproducibility and practical adoption in irrigation science.

Early detection of foliar diseases in potato crops remains challenging in real-world agricultural settings because symptoms can be similar and field images present challenges (weather, illumination, orientation, scale, blur, etc.). Although deep learning models have shown promising results in leaf disease detection in agricultural images, comparisons between modern lightweight convolutional neural network (CNN) architectures and attention-based mechanisms (Transformers) for potato crops remain limited, especially for local inference on edge computing devices. This study experimentally compares lightweight YOLO26 (CNN-based) and RT-DETRv2 (Transformer-based) object detectors under a common training and evaluation protocol for leaf-level detection and classification of four potato leaf classes: Healthy leaf, Early blight, Late blight, and Septoria leaf spot. These diseases exhibit noticeable symptoms in the foliage, distinguishable in the visible spectrum. These conditions generate irregular and asymmetric visual patterns on the leaf surface, making them difficult to detect under varying lighting and environmental conditions. The models were trained on the PyTorch framework using the CRISP-DM methodology on a hybrid dataset of 3060 RGB images, consisting of 2152 public images from the PlantVillage dataset and 908 custom images collected from agricultural plots in the Carchi province of Ecuador. The images were annotated with leaf-level bounding boxes using the Roboflow software. The task was formulated as leaf-level disease detection, where each bounding box represents a complete leaf assigned to a single diagnostic class. Among the evaluated configurations, YOLO26n provided the most favorable observed balance between predictive performance and computational efficiency, achieving 98.89% mAP50, 98.05% mAP50-95, 96.99% precision, and 95.76% recall, with a GPU inference time of 5.06 ms. The evaluated RT-DETRv2 configurations achieved competitive detection performance but required greater computational resources and higher inference latency. The YOLO26n model turned out to be comparable to some existing works and was therefore exported to TensorFlow Lite and integrated into a mobile application called CultivoScan, published on the Google Play Store. The application performs inference locally on Android devices, without relying on an external server, making it suitable as technological support for the visual identification of foliar diseases in potato crops in real-world agricultural contexts.

La base del diseño ergonómico de las estaciones de trabajo destinadas al personal académico universitario debe contemplar fundamentos científicos, técnicas actualizadas y metodologías propias de la ingeniería industrial que permitan mejorar los métodos de trabajo y disminuir los desperdicios. El presente documento es producto de un análisis investigativo desarrollado en la Universidad Técnica del Norte y responde a la creciente demanda de optimización de los entornos laborales académicos en el contexto ecuatoriano. Asimismo, considera las exigencias ergonómicas existentes, las cuales progresivamente continúan intensificándose como consecuencia del uso intensivo de instrumentos tecnológicos emergentes y de la evolución tecnológica continua.