Potential of NIR computer vision technique for the detection of mango’s defects
NIR technique for detection of mango’s defects
DOI:
https://doi.org/10.21921/jas.v9i04.%2011595Keywords:
Defect, Detection, Mango, NIR imagingAbstract
Currently, research on computer vision technique is most prevalent for quality assessment of agricultural produce. This technique assists in achieving the specified objectives prior to sale of agricultural products in the global market. Defect detection is an important task during sorting and grading of mangoes. But, the colour camera based computer vision system (CVS) has been found unable to detect the hidden defects just below the peel of mangoes. The near infra-red (NIR) camera, in contrast, can be used for identification of such defects. A semi-automatic NIR CVS was developed for inspection of hidden defects just underneath of the peel of mangoes. Image acquisition, image processing and image analysis are the main functions of developed NIR CVS. The NIR camera was used to capture standard image from the samples of Chausa fruits. Algorithm steps were developed for image processing and analysis of hidden defects. Water core, fungal infection and the defects on the surface of mangoes, which were hidden just below the peel, identified and quantified. The present study, thus, showed that the NIR CVS has potential for detection of hidden defects of mangoes. The semi-automatic CVS can be upgraded to fully automatic for commercial application.
References
Aleixos N, Blasco J, Navarron F and Molto E. 2002. Multispectral inspection of citrus in real-time using machine vision and digital signal processors. Comp and Electronics Agriculture, 33(2): 121–137.
Jha SN, Jaiswal P, Narsaiah K, Gupta M, Bhardwaj R and Singh AK. 2012. Nondestructive prediction of sweetness of intact mango using near infrared spectroscopy. Scientia Horticulturae 138: 171 – 175.
Jha SN, Narsaiah K, Jaiswal P, Bharadwaj R, Gupta M, Kumar R and Sharma R. 2014. Nondestructive prediction of maturity of mango using near infrared spectroscopy. Journal of Food Engineering 124C: 152-157
Lu R. 2003. Detection bruises on apples using near-infrared hyper spectral imaging. Trans ASAE, 46 (2): 523–530
Malchow D. 2007. Imaging in the Shortwave Infrared (SWIR) for Industrial Applications, Advanced Imaging magazine, February 2007. SUI (Sensors Unlimited, Inc.), part of Goodrich Corporation Otsu N (1979) A threshold selection method from gray-level histogram. IEEE Transactions on Systems Man and Cybernetics 9: 62–66.
Marhoon AF, Younis ANS and Taha FT. 2013. Automated Visual Inspection System for Specifying Brick Quality. Journal of Sensor Technology 3: 110-114.
More SS and Hippargi SS. 2018. Implementation of Soft Computing Models for Fruit Grading- A Review. Int Research J Engineering and Technology (IRJET) 5(5): 3205-3209.
Patel KK, Kar A and Khan MA. 2012. Nondestructive Food Quality Evaluation Techniques: Principle and Potential. Agricultural Engineering Today 36(4): 29-34.
KK, Kar A and Khan MA. 2019b. Common External Defect Detection of Mangoes using Color Computer Vision. J Institution of Engineers (India): Series A. 100(4): 559-568. DOI 10.1007/s40030-019-00396-6
Patel K K, Kar A and Khan MA. 2020. NIR computer vision for rapid measurements of some physical properties of mangoes. Journal of AgriSearch 7(4):211-219.
Raghavendra A. 2016. A Survey on Internal Defect Detection in Fruits by Non-Intrusive Methods. Int. J of Latest Trends in Engineering and Technology 6(6): 343-348
Tao Y and Wen Z. 2003. Item defect detection apparatus and method. US Patent, Publication No. US6610953 B1
Unay D and Gosselin B. 2007. Stem and calyx recognition on ‘Jonagold’ apples by pattern recognition. Journal of Food Engineering 78: 597–605
Uthaisombut P. 1996. Detecting Defects In Cherries Using Machine Vision. Thesis, published online, citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.34.3500.
Xing J, Bravo C, Moshou D, Ramon H and De Baerdemaeker J. 2006. Bruise detection on ‘Golden Delicious’ apples by VIS/NIR spectroscopy. Computer and Electronics in Agriculture 52: 11–20.
