Detecting rottenness caused by Penicillium genus fungi in citrus fruits using machine learning techniques

Penicillium fungi are among the main defects that may affect the commercialization of citrus fruits. Economic losses in fruit production may become enormous if an early detection of that kind of fungi is not carried out. That early detection is usually based either on UltraViolet light carried out manually. This work presents a new approach based on hyperspectral imagery for defect segmentation. Both the physical device and the data processing (geometric corrections and band selection) are presented. Achieved results using classifiers based on Artificial Neural Networks and Decision Trees show an accuracy around 98%; it shows up the suitability of the proposed approach.

[1]  Gustavo Camps-Valls,et al.  Automatic correction of the effects of the light source on spherical objects. An application to the analysis of hyperspectral images of citrus fruits , 2008 .

[2]  José Blasco,et al.  Multispectral inspection of citrus in real-time using machine vision and digital signal processors , 2002 .

[3]  G. Camps-Valls,et al.  Hyperspectral system for early detection of rottenness caused by Penicillium digitatum in mandarins , 2008 .

[4]  J. Curcio,et al.  Near infrared absorption spectrum of liquid water , 1951 .

[5]  R. Eckmiller.  Advanced neural computers , 1990 .

[6]  Simon Haykin,et al.  Neural Networks and Learning Machines , 2010 .

[7]  Chris H. Q. Ding,et al.  Minimum Redundancy Feature Selection from Microarray Gene Expression Data , 2005, J. Bioinform. Comput. Biol..

[8]  Vincent Leemans,et al.  Regular ArticleAE—Automation and Emerging Technologies: On-line Fruit Grading according to their External Quality using Machine Vision , 2002 .

[9]  Lluís Palou,et al.  Control of Postharvest Blue and Green Molds of Oranges by Hot Water, Sodium Carbonate, and Sodium Bicarbonate. , 2001, Plant disease.

[10]  Robert Tibshirani,et al.  The Elements of Statistical Learning: Data Mining, Inference, and Prediction, 2nd Edition , 2001, Springer Series in Statistics.

[11]  Christopher M. Bishop,et al.  Neural networks for pattern recognition , 1995 .

[12]  M. Destain,et al.  Development of a multi-spectral vision system for the detection of defects on apples , 2005 .

[13]  José Blasco,et al.  Machine Vision System for Automatic Quality Grading of Fruit , 2003 .

[14]  Jacob Cohen,et al.  Weighted kappa: Nominal scale agreement provision for scaled disagreement or partial credit. , 1968 .

[15]  C. Ververis,et al.  Cellulose, hemicelluloses, lignin and ash content of some organic materials and their suitability for use as paper pulp supplements. , 2007, Bioresource technology.

[16]  Jerome H. Friedman,et al.  An Overview of Predictive Learning and Function Approximation , 1994 .

[17]  José Blasco,et al.  Computer vision detection of peel defects in citrus by means of a region oriented segmentation algorithm , 2007 .

[18]  Fuhui Long,et al.  Feature selection based on mutual information criteria of max-dependency, max-relevance, and min-redundancy , 2003, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[19]  Andrew R. Webb,et al.  Statistical Pattern Recognition , 1999 .