Architectural framework for prediction systems applied to banana production in Ecuador
DOI:
https://doi.org/10.46480/esj.10.1.280Keywords:
systems architecture, , machine learning, agricultural forecasting.Abstract
Context: Banana cultivation is a fundamental component of the Ecuadorian economy, and its yield is significantly influenced by environmental and soil variables. This renders traditional prediction methods ineffective. Despite the evidence from prior studies demonstrating the efficacy of machine learning and geospatial analysis in enhancing agricultural forecasting, a conceptual framework tailored to the Ecuadorian banana sector remains to be developed. The proposed architecture is designed to integrate environmental, soil, and historical production data to enhance decision-making processes in agricultural settings, particularly on farms and within producer organizations. Method: The evaluation of the architecture was conducted through the implementation of a hybrid approach. In accordance with a qualitative approach, the information systems success model developed by DeLone and McLean was employed to assess system quality, information quality, and the impact on users and organizations. Quantitative metrics derived from the ISA model were employed to assess internal consistency, complexity, and level of integration. Results: The findings indicate that the acquisition and processing modules exhibit both high technical and informational quality, while the visualization components are noteworthy for their direct impact on users. Quantitative metrics have been demonstrated to reveal an organized, scalable structure that can be adapted to different production scenarios. Conclusions: In conclusion, the proposed architecture provides a solid conceptual basis for future predictive systems in the Ecuadorian banana sector, promoting the use of advanced technologies in agriculture.
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