Use of classifier to determine coffee harvest time by detachment force

Autor: Flávio Castro da Silva, Murilo Machado de Barros, Anderson Gomide Costa, Fábio Moreira da Silva, Gabriel Araújo e Silva Ferraz
Jazyk: angličtina
Rok vydání: 2018
Předmět:
Zdroj: Revista Brasileira de Engenharia Agrícola e Ambiental v.22 n.5 2018
Revista Brasileira de Engenharia Agrícola e Ambiental
Universidade Federal de Campina Grande (UFCG)
instacron:UFCG
Revista Brasileira de Engenharia Agrícola e Ambiental-Agriambi, Vol 22, Iss 5, Pp 366-370
Popis: Coffee quality is an essential aspect to increase its commercial value and for the Brazilian coffee business to remain prominent in the world market. Fruit maturity stage at harvest is an important factor that affects the quality and commercial value of the product. Therefore, the objective of this study was to develop a classifier using neural networks to distinguish green coffee fruits from mature coffee fruits, based on the detachment force. Fruit detachment force and the percentage value of the maturity stage were measured during a 75-day harvest window. Collections were carried out biweekly, resulting in five different moments within the harvest period. A classifier was developed using neural networks to distinguish green fruits from mature fruits in the harvest period analyzed. The results show that, in the first half of June, the supervised classified had the highest success percentage in differentiating green fruits from mature fruits, and this period was considered as ideal for a selective harvest under these experimental conditions.
Databáze: OpenAIRE