• Journal of Korea Multimedia Society
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• v.25 no.10
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• pp.1386-1394
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• 2022

As consumption of high-quality fruits increases and sales and packaging units become smaller, the demand for automatic fruit grading systems is increasing. Compared to other crops, the quality of fruit is determined by visual characteristics such as shape, color, and scratches, rather than just physical size and weight. Accordingly, this study presents a CNN model that can effectively extract and classify the visual features of fruits and a perceptron that classifies fruits using physical features, and proposes a stacking ensemble model that can effectively combine the classification results of these two neural networks. The experiments with AI Hub public data show that the stacking ensemble model is effective for grading fruits. However, the ensemble model does not always improve the performance of classifying all the fruit grading. So, it is necessary to adapt the model according to the kind of fruit.

• Journal of Biosystems Engineering
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• v.20 no.1
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• pp.95-103
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• 1995

A series of study has been conducted to develop a multi-purpose fruit grader using a black ＆ white image processing system equipped with a 550 nm interference filter. A device and high performance algorithms were developed for sizing and color grading of Fuji apple in the previous study. In this study an emphasis was put on finding correlations between weights of several kinds of fruits and their area fractions(AF), and on compensating the blurring effect upon sizing and color grading by conveying speed of fruit. Also, the effect of orientation and direction of fruit on conveyor during image forming was analyzed to identify any difficulty (or utilizing an automatic fruit feeder. The results are summarized as follows. 1. The correlation coefficients(r) between the weights of fruits and their image sizes were 0.984~0.996 for apples, 0.983~0.990 for peachs, 0.995 for tomato, 0.986 for sweet persimmon and 0.970~0.993 for pears. 2. It was possible to grade fruits by color with the area weighted mean gray values(AWMGV) based on the mean gray valves of direct image and the compensated values of reflected image of a fruit, and also possible to sort fruits by size with AF. Accuracies in sizing and color grading ranged over 81.0% ~95.0% and 82.0% ~89.7% respectively as compared with results from sizing by electronic weight scale and grading by expert. 3. The blurring effect on the sizing and color grading depending on conveying speed was identified and regression equations were derived. 4. It was found that errors in sizing and coloring grading due to the change in direction and orientation of Fuji apple on the conveyor were not significant as far as the stem end of apple keeping upward.

• Journal of Biosystems Engineering
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• v.18 no.4
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• pp.358-370
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• 1993

In Korea, fruit grading has been mainly done manually, and manual grading depends on human sense. Thus it is subjected to human error and is not always as consistent as would be desired. Therefore, a study on the development of fruit grader was initiated to improve the consistency of fruit grading. The sensitivity for fruit weight of the conventional spring type weight grader has a tendency to decrease by physical characteristics of spring which is used as a weight sensing unit. This study was carried out to develop weight measuring device for establishing the base of weight sensing unit of electronic weight grader. This device consists of a weight sensor using load cell, data acquisition system, and a microcomputer containing program to calculate fruit weight. The weight measuring device using load cell was developed to increase sensitivity of fruit weight. The result of this study showed that the weight sensing unit of electronic weight grader contributed to the improvement of performance of weight measuring device.

• Smart Media Journal
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• v.7 no.4
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• pp.37-43
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• 2018

Most of fruit quality classification has been done by time consuming, inaccurate and intensive manual labor. This study proposed an automated fruit grading system based on appearances and internal flavors. In this study, image processing technique and a weight checker were used to measure the value of appearance features and the near infrared spectroscopy analysis method was used to estimate the value of internal flavors. Additionally, I suggested 8x8x5x5 ANN based fruit quality classifier model to grade fruits quality. The proposed automated fruit quality classification system is expected to be very beneficial for many farms where heavy manual labor is usually needed for fruit quality classification.

• Research in Plant Disease
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• v.14 no.3
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• pp.205-209
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• 2008

This research was carried out to elucidate the causes of fruit skin browning in 'Mansu' pear for the last 2 years. It was observed that skin browning was induced even by the small mechanical injury produced during grading and packing for the market supplies after harvest on the fruits of 'Mansu' pear. The incidences of fruit skin browning in pears treated with artificial mechanical injuries were investigated between 'Niitaka' and 'Mansu' pears. The results showed that fruits of 'Mansu' are more susceptible to skin browning than those of 'Niitaka', We also found that the epidermis of fruits in 'Mansu' pear was thinner than that of 'Niitaka', and that there was lower incidence of fruit browning in epidermis of pears with high chlorophyll content than those with low chlorophyll content. The skin browning in fruits could be considerably reduced by sorting and grading them wrapped with paper bags for the fruits of 'Mansu' pear.

• Korean Journal of Agricultural Science
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• v.37 no.1
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• pp.7-12
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• 2010

A significant difference in table grape quality was found between harvest seasons, producers and cultivars. In general, 'Kyoho' grapes showed much greater difference in fruit quality than 'Campbell Early'. The ratio of 'Campbell Early' grapes with poor quality (below quality standard within grades), was higher in fruit harvested early in the season, mainly because of immature fruit harvest. In 'Kyoho', poor quality of fruit seemed to be derived from the deviation of cultural practice between producers. Major factors responsible for poor quality in both cultivars includes harvest of unripe cluster, poor sorting and grading, berry abscission, and poor coloration. It is recommended to introduce a new quality standards considering the market condition in 'Kyoho' or a fresh-cut technology of grape berries for niche market.

• Journal of Biosystems Engineering
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• v.17 no.4
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• pp.363-369
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• 1992

The aim of this study was to examine the blurring effects on performance of the experimental fruit grader in grading Fuji apples by size and coloration of the whole surface of individual apples. The grader consisted of a black/white image prcessing system, one camera, and utilized the algorithm developed for high speed sorting in the previous study. The results are summarized as follows : 1. With the algorithm developed in the previous study, it took 0.27~0.33 second in analyzing the size and coloration of an apple, and relative errors were within 3% for size and 1.3% for coloration. 2. The effect of blurring increased linearly with the conveying speed of apple and showed more significant effect on detection of coloration than on determining of size. 3. Considering the blurring effect, capacity of the experimental fruit grader was estimated to 7,500 apples per hour.

• Proceedings of the Korean Society for Agricultural Machinery Conference
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• 1993.10a
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• pp.1292-1301
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• 1993

This study was intended to examine feasibility of sizing and color grading of Fuji apple with black/white image processing system , to develop a device with which the whole surface of an apple could be captured by one camera , to develop an algorithm for a high speed sorting , and to examine the effects of blurring on the performance of the experimental fruit grader.

• Food Science and Preservation
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• v.7 no.2
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• pp.155-159
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• 2000

Apple fruit grading is largely dependant on skin color degree. This work reports about the possibility of nondestructive assessment of apple fruit color using infrared(NIR) reflectance spectroscopy. NIR spectra of apple fruit were collected in wavelength range of 1100~2500nm using an InfraAlyzer 500C(Bran+Luebbe). Calibration as calculated by the standard analysis procedures MLR(multiple linear regression) and stepwise, was performed by allowing the IDAS software to select the best regression equations using raw spectra of sample. Color degree of apple skin was expressed as 2 factors, anthocyanin content by purification and a-value by colorimeter. A total of 90 fruits was used for the calibration set(54) and prediction set(36). For determining a-value, the calibration model composed 6 wavelengths(2076, 2120, 2276, 2488, 2072 and 1492nm) provided the highest accuracy : correlation coefficient is 0.913 and standard error of prediction is 4.94. But, the accuracy of prediction result for anthocyanin content determining was rather low(R of 0.761).

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.2
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• pp.421-441
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• 2021

Fruit detection in orchards is one of the most crucial tasks for designing the visual system of an automated harvesting robot. It is the first and foremost tool employed for tasks such as sorting, grading, harvesting, disease control, and yield estimation, etc. Efficient visual systems are crucial for designing an automated robot. However, conventional fruit detection methods always a trade-off with accuracy, real-time response, and extensibility. Therefore, an improved method is proposed based on coarse-to-fine multitask cascaded convolutional networks (MTCNN) with three aspects to enable the practical application. First, the architecture of Fruit-MTCNN was improved to increase its power to discriminate between objects and their backgrounds. Then, with a few manual labels and operations, synthetic images and labels were generated to increase the diversity and the number of image samples. Further, through the online hard example mining (OHEM) strategy during training, the detector retrained hard examples. Finally, the improved detector was tested for its performance that proved superior in predicted accuracy and retaining good performances on portability with the low time cost. Based on performance, it was concluded that the detector could be applied practically in the actual orchard environment.