Open AccessPotential of Near-Infrared (NIR) spectroscopy technique for early detection of Insidious Fruit Rot (IFR) disease in Harumanis mango
Author(s) -
Norma Zakaria,
Azizi Abdullah,
Fathinul Syahir Ahmad Saad,
Muhamad Khairul Ali Hassan,
Muhammad Nasri Abu Bakar,
Abdul Rahman Mohd Saad,
Suhizaz Sudin,
Mohtar Ibrahim
Publication year - 2021
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/2107/1/012014
Subject(s) - near infrared spectroscopy , reflectivity , horticulture , artificial neural network , materials science , mathematics , environmental science , remote sensing , optics , computer science , artificial intelligence , physics , biology , geology
Harumanis mango ‘Insidious Fruit Rot’(IFR), is one of the common issues that hampered the fruit quality and consequently lowered the premium value of Harumanis Mango. Physically and visually the affected fruit does not show any attributes that indicates the presence of IFR on any part of the fruit until it has been cut open. This paper investigates the feasibility of a non-destructive method to screen the Harumanis mango from IFR using near-infra red light and artificial neural network. A common NIR light emitting diodes of 1000nm wavelength was used as the light source to emit NIR light while a photodiode was used to measure the intensity of the reflected NIR light from Harumanis mango. Early detection of IFR were done manually by local expert using acoustic method by flicking fingers to detect any abnormality inside the fruit. Sample data on NIR Spectroscopy reflectance results of 120 samples were used to classify the presence of IFR using neural network. Mean value of NIR reflectance of RBG for Harumanis mango with an incidence of Insidious Fruit Rot are R=0.651, G=0.465 and B=0.458, while without IFR are R = 0.211, G=0.15 and B=0.146. Using MATLAB’s neural network training tool, a training set regression was obtained with an accuracy value of 0.9805 for prediction of IFR, thus this value is very high in accuracy.