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.

Obtaining highly accurate predictive models to precisely estimate corn crop yields is essential for making informed decisions in a sustainable agriculture environment. There are various approaches to achieve this goal, including models based on Fuzzy Logic, Association Rules, and Machine Learning. However, some of these models have limitations in terms of the accuracy of their predictions, attributable to the high complexity and non-linearity in the interactions between factors. While Machine Learning techniques alone can achieve high precision, the inclusion of multiple attributes can reduce it. This study focuses on identifying the most influential factors at the regional level through a comprehensive analysis of the relevance of features associated with corn crop yields in Colombia, a country in the Neotropical zone. To accomplish this, climatological time series and historical yield records are used through a methodology based on CRISP- DM, widely used in the field of data mining, involving a review of related work, data cleaning and transformation, relevance evaluation using the RReliefF algorithm, and verification of the performance of the most influential factors through prediction algorithms. The results obtained demonstrate that solar radiation, precipitation, vapor pressure, and maximum and minimum temperatures exert the greatest influence on corn crop yield prediction, with a relevance factor of 0.033, 0.032, 0.026, 0.022, and 0.021, respectively. In the validation of the performance of the selected factors, two predictive models were implemented. The first, based on Artificial Neural Networks, yielded a RMSE of 0.1216 with the subset of variables and 0.1403 with all available variables. In the second, Linear Regression was applied, resulting in an RMSE of 0.1417 with the subset of variables and 0.1424 with all available variables. These results highlight the importance of the selected features as the most influential climatic factors in defining highly accurate predictive models in the Neotropical zone.

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.

Currently, the increase in devices capable of continuously collecting data on non-stationary and dynamic variables affects predictive models, particularly if they are not equipped with algorithms capable of adapting their parameters and structure, causing them to be unable to perceive certain time-varying properties or the presence of missing data in data streams. A constantly developing solution to such problems is evolving fuzzy inference systems. The aim of this work was to systematically review forecasting models implemented through evolving fuzzy inference systems, identifying the most common structures, implementation outcomes, and predicted variables to establish an overview of the current state of this technique and its possible applications in other unexplored fields. This research followed the PRISMA methodology of systematic reviews, including scientific articles and patents from three academic databases, one of which offers free access. This was achieved through an identification, selection, and inclusion workflow, obtaining 323 records on which analyses were carried out based on the proposed review questions. In total, 62 investigations were identified, proposing 115 different system structures, mainly focused on increasing precision, in addition to addressing eight main fields of application and some optimization techniques. It was observed that these systems have been successfully implemented in forecasting variables with dynamic behavior and handling missing values, continuous data flows, and non-stationary characteristics. Thus, their use can be extended to phenomena with these properties.

Colombian transitory crop production exhibits marked structural heterogeneity across department–crop combinations, yet empirical characterizations of productive scale at the subnational level remain scarce. This study presents a descriptive analysis and clustering-based productive scale segmentation of Colombian transitory crops at the departmental level for the period 2007–2024. Data from the Evaluaciones Agropecuarias Municipales (EVA) were processed through a structured CRISP-DM pipeline comprising preprocessing of 347,141 records, departmental aggregation, and engineering of five clustering features: average production, average planted area, number of active periods, and temporal and spatial Herfindahl–Hirschman indices. K-Means clustering ($k=3$) was applied to a final dataset of 490 department–crop pairs and validated based on a global silhouette coefficient of 0.888. The segmentation reveals a markedly asymmetric productive structure: 93.7% small scale (459 pairs), 5.3% medium scale (26 pairs), and 1.0% large scale (5 pairs), with natural breakpoints at approximately 35,386 t and 275,959 t. Large-scale production is concentrated in papa (Cundinamarca, Boyacá, Nariño) and arroz (Casanare, Tolima). Clustering demonstrated quantitative superiority over quartile-based classification, reducing the within-group coefficient of variation from 223.9% to 30.6% for the upper segment. The methodology is replicable across national agricultural statistics systems, and the processed dataset is publicly available under CC BY 4.0.