Cultivating in greenhouses constitutes a fundamental tool for the development of high-quality crops with a high degree of profitability. Prediction and control models guarantee the correct management of environment variables, for which fuzzy inference systems have been successfully implemented. The purpose of this review is determining the various relationships in fuzzy inference systems currently used for the modelling, prediction, and control of humidity in greenhouses and how they have changed over time to be able to develop more robust and easier to understand models. The methodology follows the PRISMA work guide. A total of 93 investigations in 4 academic databases were reviewed; their bibliometric aspects, which contribute to the objective of the investigation, were extracted and analysed. It was finally concluded that the development of models based in Mamdani fuzzy inference systems, integrated with optimization and fuzzy clustering techniques, and following strategies such as model-based predictive control guarantee high levels of precision and interpretability.

The degree of roasting of the coffee determines the physical properties of the bean which are directly represented in the quality of the coffee, to classify the coffee bean efficiently represents a challenge that has been addressed from different technological approaches with colorimeters. This research aims to simplify the identification of the roast level of ground coffee on the Agtron scale by characterizing the degree of roast using an optoelectronic color sensor and establishing a correlation between the Red, Green, and Blue (RGB) scales. This allows for the assurance of quality levels of the beans right from the roasting process. This research comprehends the collection and preparation of samples, the definition of RGB and CIE L*a*b* values, and their interpretation in the Agtron scale using the red component of the RGB scale. The results showed an efficient and accurate estimation for the roast degree of ground coffee beans (0.1371 MSE) that uses minimum processing requirements and a function to assess the intermediate values in the Agtron scale. The characterization of the roast degree of ground coffee beans using data collected from an optoelectronic color sensor through a high-precision function with a linear structure enables the description of intermediate values not fully represented on the Agtron scale. This enhances the process of identifying the roast degree, facilitating subsequent quality assurance processes by maintaining the beans at the desired roast level. Key words: Agtron; coffee bean color; color sensor; RGB.

Establishing the indoor and outdoor humidity values in a greenhouse allows us to describe the crop yield during its entire developmental cycle. This study seeks to develop a predictive model of indoor relative humidity values in a greenhouse with high accuracy and interpretability through the use of optimized fuzzy inference systems, in order to offer greenhouse users a clear and simple description of their behaviour. The three-phase methodology applied made use of descriptive statistics techniques, correlation analysis, and prototyping paradigm for the iterative and incremental development of the predictive model, validated through error measurement. The research resulted in six models which define the behaviour of humidity as a result of temperature, CO₂, and soil moisture, with percentages of effectiveness above 90. The implementation of a Mamdani-type fuzzy inference system, optimized by a hybrid method combining genetic and interior point algorithms, allowed to predict the relative humidity in greenhouses with high interpretability and precision, with an effectiveness percentage of 90.97 and MSE (mean square error) of 8.2e3.

The bacterium Xanthomonas campestris poses a significant threat to global agriculture due to its ability to infect leaves, fruits, and stems under various climatic conditions. Its rapid spread across large crop areas results in economic losses, compromises agricultural productivity, increases management costs, and threatens food security, especially in small-scale agricultural systems. To address this issue, this study developed a model that combines fuzzy logic and neural networks, optimized with intelligent algorithms, to detect symptoms of this foliar disease in 15 essential crop species under different environmental conditions using images. For this purpose, Sugeno-type fuzzy inference systems and adaptive neuro-fuzzy inference systems (ANFIS) were employed, configured with rules and clustering methods designed to address cases where diagnostic uncertainty arises due to the imprecision of different agricultural scenarios. The model achieved an accuracy of 93.81%, demonstrating robustness against variations in lighting, shadows, and capture angles, and proving effective in identifying patterns associated with the disease at early stages, enabling rapid and reliable diagnoses. This advancement represents a significant contribution to the automated detection of plant diseases, providing an accessible tool that enhances agricultural productivity and promotes sustainable practices in crop care.

La detección temprana de enfermedades en las plantas mediante técnicas de inteligencia artificial, ha sido un avance tecnológico muy importante para la agricultura, ya que por medio del aprendizaje automático y algoritmos de optimización, se ha logrado incrementar el rendimiento de diversos cultivos en varios países alrededor del mundo. Distintos investigadores han enfocado sus esfuerzos en desarrollar modelos que permitan apoyar la tarea de detección de enfermedades en las plantas como solución a las técnicas tradicionales utilizadas por los agricultores. En esta revisión sistemática de literatura se presenta un análisis de los artículos más relevantes, en los que se usaron técnicas de procesamiento de imágenes y aprendizaje automático, para detectar enfermedades por medio de imágenes de las hojas de diferentes cultivos, y a su vez se lleva a cabo un análisis de interpretabilidad y precisión de estos métodos, teniendo en cuenta cada fase las fases de procesamiento de imágenes, segmentación, extracción de características y aprendizaje, de cada uno de los modelos. De esta manera se evidencia vacío en el campo de la interpretabilidad, ya que los autores se han enfocado principalmente en obtener buenos resultados en sus modelos, más allá de brindar al usuario una explicación clara de las características propias del modelo.

Deforestation in Colombia, driven by armed conflict and illicit crops, triggered an environmental crisis, particularly in the Caribbean region, where forest loss in areas such as the Sierra Nevada de Santa Marta degraded ecosystems, reduced carbon sequestration, and increased soil erosion, threatening biodiversity and local food security. In response, the Arhuaco Indigenous community implemented an ethnic territorial management system to restore degraded lands and safeguard their ancestral territory. This study evaluates the effectiveness of their efforts, supporting their call for territorial expansion by analyzing forest cover changes (1973–2023) in the Tucurinca and Aracataca river basins. Using Landsat imagery, remote sensing, and a maximum likelihood algorithm, we generated thematic maps and statistical vegetation change data, validated by a 91.4% accuracy rate (kappa coefficient and confusion matrices). Results demonstrate significant forest recovery, highlighting collective reforestation and Indigenous sustainable management as pivotal strategies for reversing deforestation in post-conflict scenarios.