2002
DOI: 10.1016/s0168-1699(02)00100-x
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Machine vision technology for agricultural applications

Abstract: Current applications of machine vision in agriculture are briefly reviewed. The requirements and recent developments of hardware and software for machine vision systems are discussed, with emphases on multispectral and hyperspectral imaging for modern food inspection. Examples of applications for detection of disease, defects, and contamination on poultry carcasses and apples are also given. Future trends of machine vision technology applications are discussed. #

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Cited by 361 publications
(164 citation statements)
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“…A major factor limiting the implementation of CI for routine process monitoring arises from the relatively lengthy times necessary for hypercube image acquisition processing and classification [64]. proposed to reduce the data load and increase processing speed in Chemical Imaging [66].…”
Section: Image Acquisition Timementioning
confidence: 99%
“…A major factor limiting the implementation of CI for routine process monitoring arises from the relatively lengthy times necessary for hypercube image acquisition processing and classification [64]. proposed to reduce the data load and increase processing speed in Chemical Imaging [66].…”
Section: Image Acquisition Timementioning
confidence: 99%
“…The past two decades have seen significant progress in the development of non-destructive sensing technologies for automated quality evaluation and safety inspection of agricultural and food products. Among the most successful technologies are near-infrared spectroscopy (NIRS) and machine vision [3,4]. In NIRS, the near-infrared (NIR) light covering the wavelength range of about 750-2500 nm is incident on the food product in the form of diffuse reflectance, transmittance or interactance, and the acquired spectra are then quantitatively related to the chemical and physical properties of the product.…”
Section: Introductionmentioning
confidence: 99%
“…As informações obtidas são capazes de extrapolar os domínios dos objetos na região visível, fazendo uso de sistemas de visão ultravioleta (UV), infravermelho próximo (NIR), infravermelho (IF) e outros. Tais abordagens possibilitam o fornecimento de dados adicionais importantes, como maturidade da planta, qualidade, doença e estresse (CHEN; CHAO; KIM, 2002).…”
Section: "Sistema De Gerenciamento Agrícola Baseado Na Variação Espacunclassified