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.

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.

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.