Título:	Detecting and classifying pests in crops using proximal images and machine learning: a review.
Autor:	BARBEDO, J. G. A.
Afiliación:	JAYME GARCIA ARNAL BARBEDO, CNPTIA.
Año:	2020
Referencia:	AI, v. 1, n. 2, p. 312-328, June 2020.
Descripción:	Abstract: Pest management is among the most important activities in a farm. Monitoring all different species visually may not be effective, especially in large properties. Accordingly, considerable research effort has been spent towards the development of effective ways to remotely monitor potential infestations. A growing number of solutions combine proximal digital images with machine learning techniques, but since species and conditions associated to each study vary considerably, it is difficult to draw a realistic picture of the actual state of the art on the subject. In this context, the objectives of this article are (1) to briefly describe some of the most relevant investigations on the subject of automatic pest detection using proximal digital images and machine learning; (2) to provide a unified overview of the research carried out so far, with special emphasis to research gaps that still linger; (3) to propose some possible targets for future research.
Thesagro:	Infestação Inseto
NAL Thesaurus:	Pest monitoring Insects Digital images
Palabras clave:	Aprendizado de máquina Imagem digital Imagens digitais Monitoramento de pragas Pest detection Machine learning Agricultural crops
DOI:	https://doi.org/10.3390/ai1020021
Tipo de Material:	Artigo de periódico
Acceso:	openAccess
Aparece en las colecciones:	Artigo em periódico indexado (CNPTIA)