Development of Neutrosophic Cognitive Maps (NCM) for the Evaluation and Ranking of the Main Causes of the Appearance of Fruit Fly Pests

Emerson Javier Jácome-Mogro^{1, *}, Pablo Morales², Cristian Jiménez-Jácome¹, Dilfuza Abidova³

¹Technical University of Cotopaxi, Ecuador

²Technical Partner of the Union of Agroecological Farmers Organizations of the Province of Tungurahua PACAT. Ecuador

³Tashkent State University of Economics, Uzbekistan

Emails: emerson.jacome@utc.edu.ec; pimorales@institutos.gob.ec; cristian.jacome@utc.edu.ec; d.abidova@tsue.uz

Abstract

The development of Neutrosophic Cognitive Maps (NCM) for the evaluation and ranking of the main causes of the appearance of fruit fly pests represents a significant advance in the field of agriculture and entomology  ̣This innovative approach allows for a holistic and integrated view of the complex and often interdependent factors that contribute to the proliferation of these destructive pests  ̣Using neutrosophic theory, which incorporates degrees of truth, falsehood, and indeterminacy, NCMs offer a powerful tool for identifying and prioritizing critical variables  ̣In this way, a more nuanced and precise understanding of the phenomenon is facilitated, enabling the design of more effective and sustainable management strategies  ̣The methodology applied in the construction of the NCM is characterized by its ability to manage the uncertainty and ambiguity inherent to ecological and agricultural systems  ̣Through the participation of experts and the analysis of empirical data, maps can be outlined that reflect the real complexity of the problem  ̣These maps not only highlight direct causes, such as weather conditions and poor agricultural practices, but also address underlying and systemic factors  ̣Thus, the use of NCM provides a robust conceptual framework for informed decision making, improving the efficiency of interventions and contributing significantly to crop protection and global food security.

Keywords: Teaching Methods; Fruit Fly, Neutrosophic Cognitive Maps (NCM); NCM