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Precision agriculture methodology and artificial vision for the prediction of viability and vigor of native seeds

Adriana Cruz

Collaboration with Luz Stella Fuentes and Viviana Rubiano
Collaboration with Gwendolyn Peyre, Mateo Marulanda, Juan David Ríos, Maria Amorocho, Catalina Ramírez, and Orlando Morales

This project aims to develop a methodology based on artificial vision techniques and statistical learning for the prediction of the type of seeds (species, variety, viability, and vigor). Therefore, allowing the identification in real-time of the highest quality seeds, building relevant knowledge for the implementation of tasks of planting new crops. In this way, we propose the development of an automatic and non-destructive methodology, which detects the best seeds for the establishment of smart crops. As a result, we will design a computational method based on artificial vision techniques and statistical learning, that optimizes the tasks of planting and storing native seeds.