• Communications for Statistical Applications and Methods
• /
• v.10 no.2
• /
• pp.325-332
• /
• 2003

This study was to develop a system for grading apples by their color using statistical image processing. T-test was used to detect edges in apple images and the chain code method was used for contour coding. The histogram and mean gray level of each RGB channel in a ring-shaped region was used to compare apple colors to reference apple color.

• Korean Journal of Agricultural Science
• /
• v.50 no.3
• /
• pp.373-382
• /
• 2023

The task of sorting and excluding blemished apples and others that lack commercial appeal is currently performed manually by human eye sorting, which not only causes musculoskeletal disorders in workers but also requires a significant amount of time and labor. In this study, an automated apple-sorting machine was developed to prevent musculoskeletal disorders in apple production workers and to streamline the process of sorting blemished and non-marketable apples from the better quality fruit. The apple-sorting machine is composed of an arm-rest, a main body, and a height-adjustable part, and uses object detection through a machine learning technology called 'You Only Look Once (YOLO)' to sort the apples. The machine was initially trained using apple image data, RoboFlow, and Google Colab, and the resulting images were analyzed using Jetson Nano. An algorithm was developed to link the Jetson Nano outputs and the conveyor belt to classify the analyzed apple images. This apple-sorting machine can immediately sort and exclude apples with surface defects, thereby reducing the time needed to sort the fruit and, accordingly, achieving cuts in labor costs. Furthermore, the apple-sorting machine can produce uniform quality sorting with a high level of accuracy compared with the subjective judgment of manual sorting by eye. This is expected to improve the productivity of apple growing operations and increase profitability.

• The Plant Pathology Journal
• /
• v.25 no.3
• /
• pp.291-293
• /
• 2009

The low virus titer in woody plant tissues and the presence of inhibitor compounds such as polyphenols, tannins and polysaccharides are common difficulties that compromise purification of plant viroids from their woody hosts. A simple, reliable method of RNA isolation using CF11 cellulose column on a microcentrifuge tube scale for detecting Apple scar skin viroid (ASSVd) in apple was developed. Total RNA extracted from leaf, woody bark and the fruit skin was used for reverse transcription. RT-PCR products could be detected from RNA prepared from dormant woody bark, fruit skin and fresh leaves with both the CF11 cellulose column method and NucliSens extractor in February, August and November. Meanwhile, with the RNeasy kit RT-PCR, products were detected only in leaves and not from bark or fruit skin. The PCR product, about 330 base pairs, was analyzed by agarose gel electrophoresis. The CF11 cellulose column method was effective for detecting ASSVd. The method enabled the processing of a large numbers of samples of dormant woody bark, leaf and fruit skin of apple.

• Journal of Applied Biological Chemistry
• /
• v.43 no.2
• /
• pp.94-100
• /
• 2000

An analytical method was developed to determine abamectin and milbemectin residues in apple, pear, and citrus using HPLC with ultraviolet absorption detection. Abamectin and milbemectin were extracted with methanol from apple, pear, and citrus samples. The extract was diluted with saline water and dichloromethane partition was followed to recover the compounds from the aqueous phase. Florisil column chromatography and aminopropyl solid-phase extraction were employed as cleanup methods to remove most of co-extractives from the sample extract. Reverse-phase HPLC using an octadecylsilyl column was successfully applied to separate and quantitate abamectin and milbemectin residues in sample extracts at the wavelength of 245 nm. Recoveries of abamectin and milbemectin from fortified samples ranged 80.4~90.3% and 90.9~96.8%, respectively. Relative standard deviations of the analytical method were less than 10% for both acaricides. Detection limit of the analytical method was 0.003 mg/kg sample for all the analytes. The proposed method was reproducible and sensitive enough to evaluate terminal residues of abamectin and milbemectin in apple, pear, and citrus.

• Research in Plant Disease
• /
• v.19 no.4
• /
• pp.288-293
• /
• 2013

In worldwide, viral diseases of apple plants has caused the serious problems like reduced production and malformation of fruits. Also, the damages of apple plants by virus and/or viroid infection (Apple chlorotic leaf spot virus, Apple stem grooving virus, Apple mosaic virus, and Apple scar skin viroid) were reported in Korea. However there is few report about the protection approach against the infection by apple viruses. Therefore, this paper introduced the experimental protocol for the development of virus-free apple cultivars (Danhong, Hongan, Saenara, Summerdream). Apple plants were treated at $37^{\circ}C$ for 4 weeks and shoot tips were cultured in vitro. After heat treatment, the detection of apple viruses was performed by RT-PCR using virusspecific detection primers in new apple cultivars. With the heat treatments followed by in vitro shoot tip culture, the proportion of virus-free stocks of 'Danhong', 'Hongan', 'Saenara', and 'Summerdream' was 28%, 16%, 12%, and 12%, respectively. Taken together, this approach can be a good tool for production of virus-free apple stocks.

• Research in Plant Disease
• /
• v.28 no.2
• /
• pp.92-97
• /
• 2022

Apple stem grooving virus (ASGV) is a high-risk viral pathogen that infects many types of fruit trees, especially pear and apple, and causes serious economic losses across the globe. Thus, rapid and reliable detection assay is needed to identify ASGV infection and prevent its spread. A reliable reverse transcription loop-mediated isothermal amplification (RT-LAMP) was developed, optimize, and evaluated for the coding region of coat protein of ASGV in pear leaf. The developed RT-LAMP facilitated the simple screening of ASGV using visible fluorescence and electrophoresis. The optimized reaction conditions for the RT-LAMP were 63℃ for 50 min, and the results showed high specificity and 100-fold greater sensitivity than the reverse transcription polymerase chain reaction. In addition, the reliability of the RT-LAMP was validated using field-collected pear leaves. Furthermore, the potential application of paper-based RNA isolation, combined with RT-LAMP, was also evaluated for detecting ASGV from field-collected samples. These assays could be widely applied to ASGV detection in field conditions and to virus-free certification programs.

• Journal of Biosystems Engineering
• /
• v.39 no.3
• /
• pp.166-173
• /
• 2014

Purpose: In this study, we focused on the image processing method to determine the external quality of Fuji apples by identifying surface defects such as scabs and bruises. Method: A CCD camera was used to capture filter images with 24 different wavelengths ranging between 530 nm and 1050 nm. Image subtraction and division operations were performed to distinguish the defect area from the normal areas including calyx, stem, and glaring on the apple surface image. All threshold values of the image were examined to reveal the defect area of pretreated filter images. Results: The developed operation methods were [image (720 nm) - image (900 nm)]/image (700 nm) for bruise detection and [image (740 nm) - image (900 nm)]/image (590 nm) for scab detection, which revealed 81% and 90% recognition ratios, respectively. Conclusions: Our results showed several optimal wavelengths and image processing methods to detect Fuji apple surface defects such as bruises and scabs.

• Journal of Biosystems Engineering
• /
• v.39 no.2
• /
• pp.142-149
• /
• 2014

Purpose: A multispectral algorithm for detection and differentiation of defective (defects on apple skin) and normal Red Delicious apples was developed from analysis of a series of hyperspectral line-scan images. Methods: A fast line-scan hyperspectral imaging system mounted on a conventional apple sorting machine was used to capture hyperspectral images of apples moving approximately 4 apples per second on a conveyor belt. The detection algorithm included an apple segmentation method and a threshold function, and was developed using three wavebands at 676 nm, 714 nm and 779 nm. The algorithm was executed on line-by-line image analysis, simulating online real-time line-scan imaging inspection during fruit processing. Results: The rapid multispectral algorithm detected over 95% of defective apples and 91% of normal apples investigated. Conclusions: The multispectral defect detection algorithm can potentially be used in commercial apple processing lines.

• Journal of Biosystems Engineering
• /
• v.26 no.2
• /
• pp.169-176
• /
• 2001

Defect of apple was depreciated the product value and causes storage disease seriously. To detect the defect of ‘Fuji’apple with machine vision system, the optical characteristics of defect should be investigated. In this research, absorbance spectra of defect were acquired by spectrophotometer in the range of visible and NIR region(400∼1,100nm) and L*a*b* color values were also acquired by colorimeter. NIR machine vision system was constructed with B&W camera, frame grabber, 16 tungsten-halogen lamps, variable focal length lens and NIR bandpass filter which was mounted to lens outward. Average gray values of defect at 15 NIR wavelength were acquired and the significant NIR wavelength was selected by comparing Mahalanobis distance between sound and defective apple. As the result of Mahalanobis distance analysis, the significant wavelength to discriminate the defectives in ‘Fuji’apple were found to be 720nm for scab and 970nm for bruise and cuts and 920nm was also effective regardless of defective types.

• International Journal of Computer Science & Network Security
• /
• v.21 no.7
• /
• pp.324-330
• /
• 2021

Agriculture is one of the essential needs of human life on planet Earth. It is the source of food and earnings for many individuals around the world. The economy of many countries is associated with the agriculture sector. Lots of diseases exist that attack various fruits and crops. Apple Tree Leaves also suffer different types of pathological conditions that affect their production. These pathological conditions include apple scab, cedar apple rust, or multiple diseases, etc. In this paper, an automatic detection framework based on deep learning is investigated for apple leaves disease classification. Different pre-trained models, VGG16, ResNetV2, InceptionV3, and MobileNetV2, are considered for transfer learning. A combination of parameters like learning rate, batch size, and optimizer is analyzed, and the best combination of ResNetV2 with Adam optimizer provided the best classification accuracy of 94%.