RT article
T1 Automated systems based on machine vision for inspecting citrus fruits from the field to postharvest - A review
A1 Cubero, Sergio
A1 Lee, Won Suk
A1 Aleixos, Nuria
A1 Albert, Francisco
A1 Blasco, José
K1 Citrus sorting
K1 Quality inspection
K1 Hyperspectral imaging
K1 Citrus colour index
K1 Citrus canker
K1 Citrus decay
K1 Citrus Huanglongbing
K1 Citrus postharvest
K1 H20  Plant diseases
AB Computer vision systems are becoming a scientific but also a commercial tool for food quality assessment. In the field, these systems can be used to predict yield, as well as for robotic harvesting or the early detection of potentially dangerous diseases. In postharvest handling, it is mostly used for the automated inspection of the external quality of the fruits and for sorting them into commercial categories at very high speed. More recently, the use of hyperspectral imaging is allowing the detection of not only defects in the skin of the fruits but also their association to certain diseases of particular importance. In the research works that use this technology, wavelengths that play a significant role in detecting some of these dangerous diseases are found, leading to the development of multispectral imaging systems that can be used in industry. This article reviews recent works that use colour and non-standard computer vision systems for the automated inspection of citrus. It explains the different technologies available to acquire the images and their use for the non-destructive inspection of internal and external features of these fruits. Particular attention is paid to inspection for the early detection of some dangerous diseases like citrus canker, black spot, decay or citrus Huanglongbing.
PB Springer
SN 1935-5149
YR 2016
FD 2016
LK http://hdl.handle.net/20.500.11939/6349
UL http://hdl.handle.net/20.500.11939/6349
LA en
NO Cubero, S., Lee, W. S., Aleixos, N., Albert, F., & Blasco, J. (2016). Automated systems based on machine vision for inspecting citrus fruits from the field to postharvest—a review. Food and Bioprocess Technology, 9(10), 1623-1639.
DS MINDS@UW
RD Jan 20, 2021