Next Article in Journal
Asymmetric Programming: A Highly Reliable Metadata Allocation Strategy for MLC NAND Flash Memory-Based Sensor Systems
Next Article in Special Issue
A Novel Low-Cost Open-Hardware Platform for Monitoring Soil Water Content and Multiple Soil-Air-Vegetation Parameters
Previous Article in Journal
A Fiber-Optic Sensor Using an Aqueous Solution of Sodium Chloride to Measure Temperature and Water Level Simultaneously
Previous Article in Special Issue
FPGA-Based Smart Sensor for Drought Stress Detection in Tomato Plants Using Novel Physiological Variables and Discrete Wavelet Transform
 
 
Article

Detection of Cracks on Tomatoes Using a Hyperspectral Near-Infrared Reflectance Imaging System

1
Department of Biosystems Machinery Engineering, College of Agricultural and Life Science, Chungnam National University, 99 Daehak-ro, Yuseong-gu, Daejeon 305-764, Korea
2
Environmental Microbiology and Food Safety Laboratory, Agricultural Research Service, U.S. Department of Agriculture, Powder Mill Rd. Bldg. 303, BARC-East, Beltsville, MD 20705, USA
3
Department of Food and Nutrition, Hanyang University, Seoul 133-791, Korea
4
Fruit Quality Laboratory, USDA-ARS, Beltsville, MD 20705, USA
*
Author to whom correspondence should be addressed.
Sensors 2014, 14(10), 18837-18850; https://doi.org/10.3390/s141018837
Received: 7 August 2014 / Revised: 10 September 2014 / Accepted: 24 September 2014 / Published: 10 October 2014
(This article belongs to the Special Issue Agriculture and Forestry: Sensors, Technologies and Procedures)
The objective of this study was to evaluate the use of hyperspectral near-infrared (NIR) reflectance imaging techniques for detecting cuticle cracks on tomatoes. A hyperspectral NIR reflectance imaging system that analyzed the spectral region of 1000–1700 nm was used to obtain hyperspectral reflectance images of 224 tomatoes: 112 with and 112 without cracks along the stem-scar region. The hyperspectral images were subjected to partial least square discriminant analysis (PLS-DA) to classify and detect cracks on the tomatoes. Two morphological features, roundness (R) and minimum-maximum distance (D), were calculated from the PLS-DA images to quantify the shape of the stem scar. Linear discriminant analysis (LDA) and a support vector machine (SVM) were then used to classify R and D. The results revealed 94.6% and 96.4% accuracy for classifications made using LDA and SVM, respectively, for tomatoes with and without crack defects. These data suggest that the hyperspectral near-infrared reflectance imaging system, in addition to traditional NIR spectroscopy-based methods, could potentially be used to detect crack defects on tomatoes and perform quality assessments. View Full-Text
Keywords: hyperspectral near infrared reflectance imaging technique; crack tomato; imaging processing; principle component analysis; F-value hyperspectral near infrared reflectance imaging technique; crack tomato; imaging processing; principle component analysis; F-value
Show Figures

MDPI and ACS Style

Lee, H.; Kim, M.S.; Jeong, D.; Delwiche, S.R.; Chao, K.; Cho, B.-K. Detection of Cracks on Tomatoes Using a Hyperspectral Near-Infrared Reflectance Imaging System. Sensors 2014, 14, 18837-18850. https://doi.org/10.3390/s141018837

AMA Style

Lee H, Kim MS, Jeong D, Delwiche SR, Chao K, Cho B-K. Detection of Cracks on Tomatoes Using a Hyperspectral Near-Infrared Reflectance Imaging System. Sensors. 2014; 14(10):18837-18850. https://doi.org/10.3390/s141018837

Chicago/Turabian Style

Lee, Hoonsoo, Moon S. Kim, Danhee Jeong, Stephen R. Delwiche, Kuanglin Chao, and Byoung-Kwan Cho. 2014. "Detection of Cracks on Tomatoes Using a Hyperspectral Near-Infrared Reflectance Imaging System" Sensors 14, no. 10: 18837-18850. https://doi.org/10.3390/s141018837

Find Other Styles

Article Access Map by Country/Region

1
Only visits after 24 November 2015 are recorded.
Back to TopTop