Neural Networks for Tea Leaf Classification

• Autores: Silva, Jesús, Hernández Palma, Hugo, Niebles Núẽz, William, Ruiz-Lazaro, Alex, Varela, Noel
• Formato: artículo
• Fecha de Publicación: 2020
• Institución: Universidad Peruana de Ciencias Aplicadas
• Repositorio: UPC-Institucional
• Lenguaje: inglés
• OAI Identifier: oai:repositorioacademico.upc.edu.pe:10757/652130
• Enlace del recurso: http://hdl.handle.net/10757/652130
• Nivel de acceso: acceso abierto
• Materia: Dryers (equipment); Neural networks; Development and testing; K-means; Leaf classification; Tea processing; Tea

The process of classification of the raw material, is one of the most important procedures in any tea dryer, being responsible for ensuring a good quality of the final product. Currently, this process in most tea processing companies is usually handled by an expert, who performs the work manually and at his own discretion, which has a number of associated drawbacks. In this work, a solution is proposed that includes the planting, design, development and testing of a prototype that is able to correctly classify photographs corresponding to samples of raw material arrived at a dryer, using intelligence techniques (IA) type supervised for Classification by Artificial Neural Networks and not supervised with K-means Grouping for class preparation. The prototype performed well and is a reliable tool for classifying the raw material slammed into tea dryers.