title: Machine Vision-Based Measurement Systems for Fruit and Vegetable Quality Control in Postharvest.
abstract: Individual items of any agricultural commodity are different from each other in terms of colour, shape or size. Furthermore, as they are living thing, they change their quality attributes over time, thereby making the development of accurate automatic inspection machines a challenging task. Machine vision-based systems and new optical technologies make it feasible to create non-destructive control and monitoring tools for quality assessment to ensure adequate accomplishment of food standards. Such systems are much faster than any manual non-destructive examination of fruit and vegetable quality, thus allowing the whole production to be inspected with objective and repeatable criteria. Moreover, current technology makes it possible to inspect the fruit in spectral ranges beyond the sensibility of the human eye, for instance in the ultraviolet and near-infrared regions. Machine vision-based applications require the use of multiple technologies and knowledge, ranging from those related to image acquisition (illumination, cameras, etc.) to the development of algorithms for spectral image analysis. Machine vision-based systems for inspecting fruit and vegetables are targeted towards different purposes, from in-line sorting into commercial categories to the detection of contaminants or the distribution of specific chemical compounds on the product's surface. This chapter summarises the current state of the art in these techniques, starting with systems based on colour images for the inspection of conventional colour, shape or external defects and then goes on to consider recent developments in spectral image analysis for internal quality assessment or contaminant detection.
authors.affiliation: IVIA, Centro de Agroingeniería, Cra. Moncada-Náquera km 5, 46113, Moncada, Spain.
authors.forename: José
authors.initials: J
authors.lastname: Blasco
authors.affiliation: IVIA, Centro de Agroingeniería, Cra. Moncada-Náquera km 5, 46113, Moncada, Spain.
authors.forename: Sandra
authors.initials: S
authors.lastname: Munera
authors.affiliation: Departamento de Ingeniería Gráfica, Universitat Politècnica de València, Camino de Vera s/n, 46022, Valencia, Spain.
authors.forename: Nuria
authors.initials: N
authors.lastname: Aleixos
authors.affiliation: IVIA, Centro de Agroingeniería, Cra. Moncada-Náquera km 5, 46113, Moncada, Spain.
authors.forename: Sergio
authors.initials: S
authors.lastname: Cubero
authors.affiliation: IVIA, Centro de Agroingeniería, Cra. Moncada-Náquera km 5, 46113, Moncada, Spain. molto_enr@gva.es.
authors.forename: Enrique
authors.initials: E
authors.lastname: Molto
mesh_terms: D005504:Food Analysis / Q000379:methods* / Q000592:standards; D005524:Food Technology / Q000379:methods / Q000592:standards; D005638:Fruit / Q000033:anatomy & histology / Q000145:classification*; D000069550:Machine Learning; D010363:Pattern Recognition, Automated / Q000379:methods; D010781:Photography / Q000379:methods*; D011786:Quality Control; D014675:Vegetables / Q000033:anatomy & histology / Q000145:classification*
keywords: Hyperspectral; Image processing; In-line inspection; Postharvest; Quality; Real-time; Spectral imaging