José-Joel González-Barbosa

This study presents a YOLO-based deep learning pipeline for detecting visual symptoms associated with calcium (Ca), potassium (K), iron (Fe), and phosphorus (P) deficiencies in Hibiscus sabdariffa L., together with healthy samples. A multi-device dataset collected over eight consecutive weeks under variable greenhouse conditions was used to evaluate YOLOv11, YOLOv12, and YOLOv13 architectures using Precision, Recall, F1-score, mAP@50, mAP@75, and mAP@[50–95]. YOLOv13 achieved the highest overall localization accuracy, particularly at mAP@75, while YOLOv12 provided the best Precision–Recall balance and YOLOv11 the most consistent weekly performance. These results demonstrate the feasibility of detecting nutrient-deficiency symptoms across different growth stages and acquisition conditions. The proposed workflow provides a reproducible, scalable, and cost-effective basis for developing low-cost nutritional monitoring tools using consumer-grade imaging devices, supporting precision agriculture and timely nutrient management.

Automated wildlife monitoring systems are essential for studying bat populations in natural environments, where nocturnal behavior, high flight speeds, and limited illumination make conventional observation difficult. This paper presents a modular multi-sensor vision system that integrates RGB, near-infrared (NIR), and depth cameras for automated bat monitoring. The proposed architecture consists of one main module and two secondary modules that can be configured into multiple operating modes according to monitoring requirements. The main module operates independently to perform real-time habitat reconstruction using an integrated depth camera or bat detection using a YOLO-based model. When combined with one secondary module, it forms a stereo vision system for three-dimensional localization; when combined with both secondary modules, it generates panoramic images that substantially expand the field of view for monitoring large cave entrances and other complex environments. The proposed modular architecture enables flexible deployment while supporting multiple sensing configurations within a single platform. The modular design provides scalability, geometric consistency through multi-sensor calibration, and flexible deployment, enabling accurate bat detection, habitat reconstruction, and wide-area monitoring within a unified sensing framework. The proposed system provides a versatile and scalable solution for adapting wildlife monitoring to different environmental conditions and observation scenarios. Experimental results demonstrate a detection precision of 0.893, a panoramic field of view of 119°, and real-time processing at 60 fps, validating the effectiveness of the proposed modular architecture.

We present a panoramic speckle interferometer designed to measure micro-displacements on the inner surfaces of cylindrical objects across a full 360°field of view. The system employs a single parabolic mirror to expand the optical field and enable comprehensive displacement analysis within cylindrical structures. Speckle fringe patterns are generated in response to mechanical state changes and recorded in real-time, capturing displacements along the X , Y , and Z axes. The relationship between induced displacement and fringe formation was established through prior characterization of a piezoelectric actuator, ensuring that each applied voltage corresponded to a known physical displacement. A synthetic speckle generation algorithm was also implemented using a deterministic geometric model of the optical system. Experimental results confirm the system’s ability to detect micro-displacements from 473 nm to 4.1 μ m. Simulated fringe patterns closely matched the experimental ones, validating the accuracy of the geometric model and demonstrating the system’s potential as a compact and effective tool for panoramic interferometric metrology.

Bats are ecologically vital mammals, serving as pollinators, seed dispersers, and bioindicators of ecosystem health. Many species inhabit natural caves, which offer optimal conditions for survival but present challenges for direct ecological monitoring due to their dark, complex, and inaccessible environments. Traditional monitoring methods, such as mist-netting, are invasive and limited in scope, highlighting the need for non-intrusive alternatives. In this work, we present a portable multisensor platform designed to operate in underground habitats. The system captures multimodal data, including near-infrared (NIR) imagery, ultrasonic audio, 3D structural data, and RGB video. Focusing on NIR imagery, we evaluate the effectiveness of the YOLO object detection framework for automated bat detection and counting. Experiments were conducted using a dataset of NIR images collected in natural shelters. Three YOLO variants (v10, v11, and v12) were trained and tested on this dataset. The models achieved high detection accuracy, with YOLO v12m reaching a mean average precision (mAP) of 0.981. These results demonstrate that combining NIR imaging with deep learning enables accurate and non-invasive monitoring of bats in challenging environments. The proposed approach offers a scalable tool for ecological research and conservation, supporting population assessment and behavioral studies without disturbing bat colonies.

Hibiscus sabdariffa (H. sabdariffa) is a high-value economic and functional crop, limited by agroclimatic conditions and low technological adoption. This systematic review examines the current state of artificial intelligence applications in agricultural management, analyzing 2111 records, selecting 82, and synthesizing 22 studies that meet the inclusion criteria. This review adopts a holistic framework aligned with three priority areas in agriculture—resource and climate management, crop productivity and quality, and sustainability—to explore how AI addresses key challenges in the cultivation and post-harvest processing of Hibiscus sabdariffa. The results show a predominance of classical machine learning techniques, with limited implementation of deep learning models. The most common applications include image classification, yield prediction, and analysis of bioactive compounds. However, limitations remain in the availability of open data, reproducible code, and standardized metrics. The narrative synthesis identified clear opportunities to integrate emerging technologies, such as deep neural networks and the Internet of Things (IoT), particularly in water management and stress monitoring. The review concludes that strengthening interdisciplinary research and promoting data openness is key to achieving a more resilient, sustainable, and technologically advanced crop.