Examinando por Materia "Seguridad alimentaria"

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    Diseño de una propuesta de actualización de la política pública de seguridad y soberanía alimentaria en Medellín basado en la teoría del cambio
    (Universidad EAFIT, 2025) Trujillo Morales, César Augusto; Giraldo Hernández, Gina María; Bedoya Cardona, Nelson de Jesús
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    Integración de sistemas agroforestales en los procesos de compensación por pérdida de biodiversidad en Colombia
    (Universidad EAFIT, 2025) Botero Llinás, Sarita; Parra Sierra, Jersain Orlando; Gutiérrez Rua, Juliana María
    Biodiversity Loss Compensation Processes in Colombia don’t contemplate agriculture as a method to improve ecosystems due to a long history of green revolution-based production and the expansion of the agricultural frontier. Different authors, countries and interviewees identify that some practices and agricultural methods such as Agroforestry Systems are capable of offering agricultural production, social and economic development, and the protection and conservation of biodiversity in the compensation processes. Nevertheless, there are normative, technical and cultural gaps that limit this articulation. With the commitment of the actors involved and the leadership of the Ministry of Environment and Sustainable Development, these gaps can be closed and route Colombia in the Articulation of both processes.
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    Microbiología predictiva mediante aprendizaje automatizado para la optimización de procesos productivos : Metanálisis
    (Universidad EAFIT, 2024) Yepes Medina, Verónica; Pinel Peláez, Nicolás
    Unsafe food containing harmful bacteria, viruses, parasites or chemicals can cause more than 200 different illnesses, from diarrhea to cancer. Worldwide, an estimated 600 million (nearly 1 in 10 people) fall ill each year after eating contaminated food, resulting in 420.000 deaths and the loss of 33 million years of healthy life. Therefore, it is necessary to detect and respond to public health threats associated with unsafe food with enabling technologies or tools. Predictive microbiology is concerned with preventing, controlling or limiting the existence of microorganisms by mapping their potential responses to particular environmental conditions, such as temperature, pH, nutrients (protein and fat), water activity (aw) and others. And machine learning as a branch or artificial intelligence learns from these data, identifying patterns for decision making. Recent studies are based in the use of supervised machine learning models to predict the presence of a foodborne pathogenic microorganism at any stage of the production chain, the most commonly used models include Random Forest and support vector machine with rating metrics for accuracy and sensitivity >80%. The main evaluation metrics of the algorithms are: accuracy, F1 score, confusion matrix, sensitivity, specificity and area under the curve (ROC-AUC, Receiver-Operating-Characteristic). Studies have shown that Random Forest was the best model, exhibiting an accuracy of 95% and a F1 score of 98%. Here were evaluated twenty five (25) articles with library metafor of Rstudio version 4.2.1 and this information provides new opportunities to explore non-destructive models for rapid detection of microorganisms in the production chain.
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    Predicción del rendimiento de cultivos agrícolas en los cinco corregimientos de la ciudad de Medellín, utilizando modelos de Machine Learning
    (Universidad EAFIT, 2024) Gómez Arango, Alba Miriam; Valencia Diaz, Edison; Zuluaga Orrego, Juan Fernando
    In a global context where agriculture and food production play a crucial role in food security, employment, and sustainability, this study focuses on predicting the yield of agricultural crops in the five districts of Medellín. The main objective is to design a prediction model for nine local crops using machine learning techniques. Medellín is distinguished by its diversity of crops, including peri-urban agriculture characterized by productive small plots distributed across various chagra-type crops. These traditional agricultural practices are carried out by an aging population of farmers. Accuracy in yield prediction becomes essential, as a significant portion of the production is dedicated to self-consumption, with a subsistence focus. However, surpluses are also traded, directly impacting the food security of the local community. The results highlight the effectiveness of machine learning models, particularly Boosting models such as PCA Random Forest and PCA XGB Boosting, in predicting the crops under study. These models demonstrate the ability to capture relationships between variables and the heterogeneity present in territorial production. However, opportunities for improvement related to reducing model errors have been identified, which can be addressed through continuous data collection and technical support provided to farmers. This will not only increase data availability but also contribute to refining the model and understanding performance behavior in the analyzed crops, facilitating decision-making in the agricultural sector of the municipality of Medellín. This project represents a valuable tool for professionals in the agricultural sector and institutions responsible for planning and agricultural development. It offers an innovative approach to sector data analysis, leveraging the advantages of data science. Through these techniques, opportunities are opened to establish strategies, plans, and projects that contribute to crop planning, the management of productive areas in the municipality, and the strengthening of local food security.
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    Semillas más fuertes, cultivos más sostenibles: el potencial de la biotecnología en la agricultura colombiana
    (2020-12-01) Martinez Guerrero, Christian Alexander; Christian Alexander Martinez-Guerrero; Villanueva Mejía, Diego; Vicerrectoría de Descubrimiento y Creación