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Determination of Surface Color of 'All Yellow' Mango Cultivars Using 42 January, 2016 Int J Agric & Biol Eng Open Access at http://www.ijabe.org Vol. 9 No.1 Determination of surface color of ‘all yellow’ mango cultivars using computer vision Marcus Nagle1*, Kiatkamjon Intani1, Giuseppe Romano1, Busarakorn Mahayothee2, Vicha Sardsud3, Joachim Müller1 (1. Institute of Agricultural Engineering, Tropics and Subtropics Group, Universität Hohenheim, Garbenstrasse 9, Stuttgart, 70599, Germany; 2. Department of Food Technology, Faculty of Engineering and Industrial Technology, Silpakorn University, Nakhon Pathom, 73000, Thailand; 3. School of Agro-Industry, Mae Fah Luang University, Chiang Rai, 57100 Thailand) Abstract: Image processing techniques are increasingly applied in sorting applications of agricultural products. This work has assessed the use of image processing for inspecting surface color of two Thai mango cultivars. A computer vision system (CVS) was developed and experiments were conducted to monitor peel color change during the ripening process. Conversion of RGB to CIE-LAB values was done via image processing and prediction models were developed to estimate color parameters from CVS data. Performance evaluations showed insufficient prediction for L values (R2 = 0.42-0.58), but better results for A 2 and B values (R = 0.90-0.95 and 0.80-0.82, respectively). Compared to the calculated color values hue angle and chroma, a yellowness index computed from intermediate XYZ values was found to be much more adept at accurately predicting peel color from CVS data. Correlations were strong for both cultivars (R2 = 0.93 for ‘Nam Dokmai’ and R2 = 0.95 for ‘Maha Chanok’). Results from classification analysis indicated satisfactory results for classifying fruits according to ripeness based on yellowness. Success rates of true positives in the categories unripe, ripe and overripe ranged 72%-92% for ‘Nam Dokmai’ and 98%-100% for ‘Maha Chanok’. Therefore, it was shown that the CVS was capable of producing accurate color values for the two mango cultivars investigated. The findings of this study can be incorporated for development of a robust system for quality prediction and establishment of a CVS for automatic grading and sorting of mangos. Keywords: mango, peel color, computer vision, image processing, fruit quality, Thailand DOI: 10.3965/j.ijabe.20160901.1861 Citation: Nagle M, Intani K, Romano G, Mahayothee B, Sardsud V, Müller J. Determination of surface color of ‘all yellow’ mango cultivars using computer vision. Int J Agric & Biol Eng, 2016; 9(1): 42－50. perception more than any other factor. Color is often 1 Introduction regarded as an indicator of flavor, edibility, shelf life and For agricultural products, color influences consumer nutritional value, since it is related to the physical, [1] chemical and sensory properties of food . Many Received date: 2015-04-16 Accepted date: 2015-11-02 climacteric fruit crops (e.g. mango, papaya, banana) Biographies: Kiatkamjon Intani, MSc, Research interests: mechanics & design. Email: [email protected]. undergo significant color changes during postharvest [2] Giuseppe Romano, PhD, Research interests: sensors in food & ripening and thus peel color becomes a main agriculture. Email: [email protected]. Busarakorn characteristic of fruit quality, playing a dominant role in Mahayothee, PhD, Professor, Research interests: food science & consumer acceptance[3]. Thailand is one of the key technology. Email: [email protected]. Vicha Sardsud, PhD, Professor, Research interests: horticulture & processing. mango producers globally. Production has increased by Email: [email protected]. Joachim Müller, PhD, a factor of 2.7 in the last two decades, meanwhile Professor, Research interests: postharvest technology, irrigation & becoming the second largest mango exporter in the world energy. Email: [email protected]. 6 *Corresponding author: Marcus Nagle, PhD, Research interests: and reaching an annual harvest of almost 3.3×10 t in postharvest technology & sensors. University of Hohenheim, 2014[4,5]. However, local fruit sorting techniques are Institute of Agricultural Engineering (440e), Garbenstrasse 9, done by visual inspection, based on physical and 70593 Stuttgart, Germany. Tel: +49 711 459 23119. Email: [email protected]. morphological characteristics as well as on visual January, 2016 Nagle M, et al. Determination of surface color of ‘all yellow’ mango cultivars using computer vision Vol. 9 No.1 43 evaluation of external appearance[6]. Unfortunately, the and providing continuous spatial information without current sorting methods are not consistent to properly contact between the device and object. Digital imaging classify fruits according to quality criteria closer to those obtains the color of any pixel in the red, green and blue required by international regulations. Thus, there is an (RGB) bands. Different models have been tested in essential need for inexpensive and non-destructive order to transform RGB readings into CIE-LAB color sensing technologies capable of sorting fruits according values[36]. However, RGB measurements obtained from to their external and internal properties to meet the a camera are device-dependent and calibration is demands of high-value markets. required[21]. This study intends to investigate the Visual inspection by humans is highly subjective, application of image processing for measuring peel color laborious and time-consuming. An alternative method is in two typically exported Thai mango cultivars. The required which is suitable to evaluate quality following objectives have been developed to be addressed characteristics of products, e.g. by image processing. In in the study: identify a suitable and economic image sorting lines, a computer vision system (CVS) is usually acquisition unit for color evaluation of mango fruits; engaged to acquire and analyze digital images evaluate correlations between reference color values and automatically. A CVS can be constructed using a image data; and test a prototype CVS for monitoring standard illumination source, a camera for image color change of mango fruits at different color stages acquisition coupled with hardware and software to during ripening. capture and process the image. In the agricultural sector, 2 Materials and methods the tasks of CVS are various[7], with many applications for the inspection of fruits and vegetables[8-12]. For fruits, 2.1 Materials CVS is commonly utilized for size and shape Two important export cultivars of Thai mango, determination[13] and detection of defects[14-16]. For namely ‘Nam Dokmai’ and ‘Maha Chanok’ were used for mango specifically, several works exist concerning the the experiments (Figure 1). ‘Nam Dokmai’ has a use of a CVS for size-mass measurements[17-19]. CVS is homogenous light yellow-green peel color, while a blend also widely developed for analyzing the color of of yellow, green and red is characteristic for ‘Maha foodstuffs[20-22], with many cases already presented for Chanok’. Previous research on harvest maturity of the fruit products[23-26]. However, studies on the use of a cultivar ‘Nam Dokmai’ has shown a high variability in digital color measurement system to determine the color peel color at harvest[34]. This range and variation of of different mango cultivars is so far lacking. Color color makes it motivating to investigate such cultivars. parameters of mango have already been evaluated to Export-grade fruits harvested at a hard mature green show significant correlations with fruit quality[28-33]. stage[37] were obtained from a local distribution company. While the use of a CVS to obtain the color values of a Fruits were checked for any external injuries and green-red cultivar has been investigated[27], further blemishes and were subjected to hot water treatment in studies are needed in order to realize a viable CVS for order to reduce the presence of fruit flies, anthracnose and assessment of mango color and the approach of previous other diseases. For the evaluation of color, 80 fruits of researchers should be extended to additional varieties. each cultivar were used per trial. The sample weight of More investigation needs to be carried out to develop a ‘Nam Dokmai’ and ‘Maha Chanok’ fruits were fully-functioning automated sorting and grading system (393.7±71.1) g and (368.9±72.8) g, respectively. Fruits for commonly traded mango cultivars. were allowed to ripen for up to eight days in baskets In the mango industry, scale charts are often used to covered with tissue paper under natural atmospheric evaluate fruit color by visual comparison[34,35]. Devices conditions (temperature of 31.8°C±1.2°C and relative such as a colorimeter are able to precisely measure the humidity of 70.3%±5.7%), depending on fruit senescence surface color of products, but digital camera technology (i.e., fruits which become senescent were no longer has the advantage of acquiring a large number of images measured). Samples were continuously monitored 44 January, 2016 Int J Agric & Biol Eng Open Access at http://www.ijabe.org Vol. 9 No.1 during ripening by taking optical measurements daily to Camera settings were adjusted and optimized via the track the progress of color change. All trials were motion detection software. The settings of the CVS carried out in a randomized block format and were were calibrated based on the established procedures[21] replicated three times. using 130 color standard palettes (Pantone LLC, U.S.A.), which covered the range of colors typical for mango peel. Figure 1 Image
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