Machine vision technology for agricultural applications

By Yud-Ren Chen, K. Chao, Moon Sung Kim. 2002

485 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/32ac20f7bd9a2fe25ff01316b7acbb7f5d6cd72b

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

Da‐Wen Sun
M. Kamruzzaman
Gamal ElMasry
Paul Allen
L. Dale
A. Théwis
J. Pierna
C. Boudry

Citations in the corpus

Deep learning and machine vision for food processing: A survey (2021, background): “Machine vision systems can acquire image data in a variety of land-based and aerial-based methods, and they can complete diverse tasks, including quality and safety inspection, agriculture produce grading, foreign object detection and crop monitoring (Chen et al., 2002).”
Sensing and Automation in Pruning of Apple Trees: A Review (2018, methodology): “A Lidar sensor also has been tried for the branch length and diameter identiﬁcation [75].”
Multispectral detection of fecal contamination on apples based on hyperspectral imagery: Part II. Application of hyperspectral fluorescence imaging (2002, background): “…imaging, Apples, Fecal contamination. n–line sensing using spectral imaging with several spectral bands can provide rapid and non–invasive means to assess agricultural commodities for their quality and safety for human consumption (Abbott et al., 1997; Chen et al., 2002; Throop et al., 1995).”
Hyperspectral and Chlorophyll Fluorescence Imaging for Early Detection of Plant Diseases, with Special Reference to Fusarium spec. Infections on Wheat (2014, background): “Major advantage of hyperspectral imaging is the pixel-wise incorporation of a continuous spectral signature of hundreds of wavelengths [52] into a two-dimensional image of the object under inspection [53].”
A Survey on Food Computing (2018, methodology): “pact on health and well-being. 4.3 Agriculture Foodcomputingcanalsobeusedintheagricultureorfoodproducts.Foodimageanalysishasmanypotentialapplications for automated agricultural and food safety tasks [Chen et al. 2002;Senthilnath et al. 2016;Xiang et al. 2014]. For example, [Jimenez et al. 1999] prop”
Hyperspectral Imaging Applications in Agriculture and Agro-Food Product Quality and Safety Control: A Review (2013, background): “concentration of nutrients and feed value in dried and fresh crop materials (2–7), food and feed quality control (8, 9), and food safety (10–12).”
Principles and Applications of Hyperspectral Imaging in Quality Evaluation of Agro-Food Products: A Review (2012, methodology): “The optical properties and fluorescence emission from the object are integrated functions of the angle and wavelength of the incident light and chemical and physical composition of the object (Chen et al., 2002).”