Autoria: CAMARGO, G. A.; CRUZ, H. B. da; BÜHLMANN, A.; BÜCHELE, F.; KESKE, C.; OLIVATO, J. B.; AYUB, R. A.; THEWEYUBYS, F. R.; FREITAS, S. T. de; NEUWALD, D. A.

Resumo: This review aims to identify prominent studies related to applications of novel techniques involving sensors and machine learning for fruit storage through the method of narrative literature review, considering the concepts of Agriculture 4.0. The advent of this new phase of agriculture brought new concepts for post-harvest professionals and scholars, such as to sensor technology and automated intelligence. Additionally, a collection of 28 studies focusing on post-harvest of fruit in the span of five years (2018 to 2022) was carefully evaluated in order to discuss the most prevalent techniques explored in the field. Therefore, this review provides a picture of achievements in a relatively new area of knowledge with supporting data and discussion analyzing the current panorama in the PHF context and how effective is the use of sensors associated with artificial intelligence for post-harvest of fruit. Among the latest developments highlighted, the application of support vector machine classifiers as machine learning algorithms alongside computer vision sensors are the most promising technologies in terms of accuracy and popularity among recent scientific developments for post-harvest of fruit. Implementation of such new technologies must consider constraints related to different national contexts.

Ano de publicação: 2024

Tipo de publicação: Artigo de periódico

Unidade: Embrapa Semiárido

Palavras-chave: Agricultura, Armazenamento inteligente, Ciência da computação, Computer science, Engenharia de alimentos, Fruta Tropical, Postharvest physiology, Postharvest technology, Postharvest treatment, Pós-Colheita