Computational geometry is a discipline focused on solving problems in the geometric domain. In this context, the algorithm for computing the convex polygon called Convex Hull (CH) is important, because it is the basis for many other algorithms. The objective of the research was to implement algorithms that compute the CH incorporating modifications to reduce the execution time. The research started with a bibliographic review of computational geometry and the algorithms highlighted in the calculation of CH. Subsequently, the QuickHull, Gift Wrapping, and Graham Scan algorithms were implemented in JAVA in their original versions; some versions with modifications were also implemented. Upon completion of implementation, tests were run to verify the execution times. Finally, the QuickHull algorithm was found to be the fastest among the implementations performed in this research. It is also noted a reduction in execution times in the modified implementations in relation to the original ones of the Gift Wrapping and Graham Scan algorithms.

In Colombia, one of the foods that guarantees food security is the plantain plant. Given its nutritional properties and low cost, it is grown mainly by peasant families with low financial resources. One of the greatest problems in the production chain of this crop is the incorrect selection of soils and integrating the field with the technology. This research proposes a recommendation model for the efficient choice of soil for this crop. This model analyzes a set of data (pH, organic material, phosphorus, sulfur, potassium, calcium and magnesium) product of the soil analysis in the laboratory and generates a list of recommended soil values adjusted for this crop and their respective correlation coefficient (similarity) with the soil values of the land requested by the farmer. For this, a similarity matrix was constructed by applying Pearson’s coefficient. Additionally, the model allows us to obtain the probability of compatibility between the identified edaphic values and the values needed by the soil for this crop. The KNN, SVM, C5.0 and logistic regression algorithms were compared using the following metrics: AUC, accuracy and error rate, F1 index, recall and precision. The KNN model. presented the best indicators. Finally, the recommendation model was built using the R programming language and implemented through a software application. This research aims to help farmers choose the right soil for cultivation by suggesting nutrients that should be applied to increase soil quality and adopt strategies that reduce soil nutrient loss.

Context: This paper proposes a method for the prediction of monthly precipitation in the department of Boyacá using models based on deep neural networks (DNNs). These approaches have achieved significant improvements in prediction accuracy when compared to traditional methods. Method: Data with a spatial resolution of 0.5° were extracted from CHIRPS 2.0 and subsequently preprocessed for the implementation of two approaches based on recurrent neural networks (RNNs) with long short-term memory (LSTM) and ConvLSTM architectures, aiming to provide accurate predictions of monthly precipitation in the studied region. Objectives: The goal of this time series analysis is to predict monthly precipitation and develop accurate models that can forecast future rainfall patterns based on historical data. This aids in water resource management and agricultural planning, as well as in mitigating the impacts of droughts or floods. Results: According to the results obtained, the LSTM model stands out for its robustness in terms of performance metrics, such as a lower mean squared error, a lower root mean squared error, and a coefficient of determination closer to 1. This demonstrates its higher accuracy compared to the ConvLSTM model.Conclusions: Deep learning models, especially RNNs with LSTM, are effective tools for predicting crucial climate data.

El aguacate Hass se ha convertido en un producto fundamental como alternativa para el desarrollo del sector agrícola del país. En nuestro país es un cultivo relativamente nuevo y la calidad en las exportaciones depende en gran medida de lograr un nivel de madurez óptima en el momento de la cosecha. Las herramientas tecnológicas no invasivas que utilizan análisis de imágenes han demostrado ser prometedoras para abordar este problema, aunque su efectividad y características computacionales siguen siendo inciertas. Este estudio presenta un modelo computacional para la medición del grado de madurez de cosecha de aguacate Hass mediante el procesamiento digital de imágenes a partir de las características físicas del fruto. El procesamiento de imágenes contribuye significativamente a proporcionar un método preciso, objetivo y consistente para evaluar la madurez, reducir el error humano y respaldar el proceso general de control de calidad.