Examinando por Materia "Crop yield"

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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.