Este artículo describe el desarrollo de un Módulo Terminal Remoto, escalable y compatible con diversidad de sensores y actuadores disponibles en el mercado. El dispositivo fue diseñado para automatización y monitoreo de procesos agroindustriales, con posibilidad de intervención en sitio o a través de servicios de Internet de las cosas (IoT). El sistema cuenta con puertos digitales y analógicos programables, USB On-The-Go (OTG) y bus 1-wirepara conexión de diferentes sensores (temperatura, pH, humedad relativa y nivel de oxígeno, entre otros); en cuanto a los actuadores se configuran en dependencia de eventos, o calendario electrónico por reloj de tiempo real (RTC). El registro de datos se realiza de forma local o mediante servicios de computación en la nube, a través de un sistema de comunicación compatible con protocolos TCP/IP, GSM, SMS, EGPRS, 3G y 4G. Por su parte el acceso al sistema se establece de forma remota, o local, a través de dispositivos USB con funcionalidad OTG o Bluetooth. El módulo está compuesto por tres subsistemas independientes; software, hardware y módulo de comunicación. El ciclo de vida del Proyecto se llevó a cabo mediante un modelo en V; integrando la metodología SCRUM para el desarrollo de la plataforma software. Así mismo el módulo de comunicación se desarrolló mediante un protocolo abierto, que permite a otros desarrolladores, generar nuevas soluciones tecnológicas e incorporar otras funcionalidades al sistema. La validación del sistema se realizó bajo el estándar IEEE 1012-2016 y las pruebas de funcionamiento del prototipo se efectuaron en cultivos agrícolas de la región de Casanare-Colombia.

This paper presents a comprehensive study on the design and performance of a high-power amplifier (PA) covering a broad frequency band from 0.1 to 4.8 GHz. Leveraging a 10 W GaN device, the amplifier achieves output power levels surpassing 10 W across the entire frequency band. Furthermore, the power-added efficiency (PAE) of the amplifier ranges from 47% to 59%, indicating its energy-efficient operation. With consistent gain characteristics varying between 7 and 15 dB, the design ensures reliable signal amplification for diverse applications. Notably, the approach introduces a simplified output matching network based on an LC network, prioritizing practicality without sacrificing performance. Additionally, comprehensive guidance is provided on utilizing the Smith chart for streamlined amplifier design, enabling engineers with an accessible methodology. Through a meticulous analysis, this work contributes to advancing the field of high-power amplification, offering enhanced performance and usability for next-generation wireless communication systems.

Research project formulation remains one of the least formalized tasks in scientific practice. Proposals are developed iteratively, with each component retroactively conditioning the others, while the decision of when a proposal is sufficiently complete still lacks a computable criterion. Current artificial intelligence tools support text generation, synthesis, and information retrieval, but, to our knowledge, none explicitly models formulation as a convergence problem subject to methodological constraints or defines a formal stopping rule. This paper introduces FARO (Augmented Formulation and Optimized Review via Artificial Intelligence), a framework that represents a research proposal as a directed methodological graph, quantifies its global coherence through a weighted loss function, refines it through conceptual backpropagation, and verifies it using structural consistency checks. Baseline uncertainty is quantified through a four-dimensional index U₀ (α = 0.941, n = 26), which sets module activation and the adaptive rigor parameter SE_τ. For accepted updates, we establish that the loss decreases monotonically; deterministic code-level constraints enforce this property independently of the generative model. A Variance Inflation Factor analogue formalizes semantic redundancy among diagnostic queries, orthogonal to inter-node contradiction. An ordinal decision layer with an abstention option expresses irreducible terminal uncertainty as a probability of favorable evaluation, to be calibrated empirically, and routes unresolved items to an actionable risk map. Operational evidence comes from 155 iterations of a real pilot project; multi-project validation and empirical calibration are underway.

Field-based phenotyping of water-related traits for precision irrigation in tropical agroecosystems poses a persistent methodological challenge, driven by high climatic variability and the complex water-use physiology of Crassulacean Acid Metabolism (CAM) crops such as pineapple (Ananas comosus var. MD2). We developed and validated a Physics-Informed Machine Learning (PIML) framework that integrates high-resolution UAV multispectral imagery, IoT-based microclimatic records, and a mechanistic soil water balance based on the FAO-56 Penman–Monteith standard to predict plot-scale soil moisture depletion as a proxy of plant water status. A six-month field campaign (March–August 2022) across 25 georeferenced commercial pineapple plots in the Colombian Orinoquia piedmont yielded a spatiotemporally balanced dataset of $N=150$ observations. Soil-adjusted vegetation indices (OSAVI, MSAVI) outperformed standard NDVI for capturing water-related canopy traits, effectively decoupling spectral responses from substrate noise. A Gradient Boosting regressor achieved $R^2=0.842$ and $RMSE=0.0705$ on a normalized target scale, corresponding to a $7.05\%$ error over the prediction range, while the traffic-light Decision Support System (DSS) for irrigation scheduling reached $91.1\%$ accuracy (Cohen’s Kappa $=0.91$). Incorporating daily soil moisture depletion as a mechanistic feature improved predictive accuracy over a spectral-only baseline ($\mathrm{\Delta }{R}^2=+0.052$) and anchored predictions within a physically consistent framework based on the FAO-56 water balance, with no false negatives observed for water deficit detection in the hold-out validation set. This framework advances high-throughput, population-scale phenotyping of water-related traits in open-canopy CAM crops, establishing a transferable methodology for operational precision irrigation under tropical savanna conditions.