• IEIE Transactions on Smart Processing and Computing
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• v.4 no.4
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• pp.265-271
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• 2015

This study proposes an algorithm for recognizing apple trees in images and detecting apples to measure the number of apples on the trees. The proposed algorithm explores whether there are apple trees or not based on the number of image block-unit edges, and then it detects apple areas. In order to extract colors appropriate for apple areas, the CIE $L^*a^*b^*$ color space is used. In order to extract apple characteristics strong against illumination changes, modified census transform (MCT) is used. Then, using the AdaBoost learning algorithm, characteristics data on the apples are learned and generated. With the generated data, the detection of apple areas is made. The proposed algorithm has a higher detection rate than existing pixel-based image processing algorithms and minimizes false detection.

• The Plant Pathology Journal
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• v.19 no.3
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• pp.159-161
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• 2003

The coat protein (CP) gene of Apple mosaic virus (ApMV), a member of the genus Ilarvirus, was selected for the design of virus-specific primers for amplification and molecular detection of the virus in cultivated apple. A combined assay of reverse transcription and polymerase chain reaction (RT-PCR) was performed with a single pair of ApMV-specific primers and crude nucleic acid extracts from virus-infected apple for rapid detection of the virus. The PCR product was verified by restriction mapping analysis and by sequence determination. The lowest concentration of template viral RNA required for detection was 100 fg. This indicates that the RT-PCR for detection of the virus is a 10$^3$times more sensitive, reproducible and time-saving method than the enzyme-linked immunosorbent assay. The specificity of the primers was verified using other unrelated viral RNAs. No PCR product was observed when Cucumber mosaic virus (Cucumovirus) or a crude extract of healthy apple was used as a template in RT-PCR with the same primers. The PCR product (669 bp) of the CP gene of the virus was cloned into the plasmid vector and result-ant recombinant (pAPCP1) was selected for molecule of apple transformation to breed virus-resistant transgenic apple plants as the next step. This method can be useful for early stage screening of in vitro plantlet and genetic resources of resistant cultivar of apple plants.

• Research in Plant Disease
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• v.27 no.2
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• pp.79-83
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• 2021

The aim of the present study was to develop a sensitive and specific detection method for the rapid detection of apple scar skin viroid (ASSVd) in apple leaves. The resulting reverse transcription recombinase polymerase amplification (RT-RPA) assay can be completed in 10 min at 42℃, is 10 times more sensitive than conventional reverse transcription polymerase chain reaction, and can specifically amplify ASSVd without any cross-reactivity with other common apple viruses, including apple stem grooving virus, apple stem pitting virus, and apple chlorotic leaf spot virus. The reliability of the RT-RPA assay was assessed, and the findings suggested that it can be successfully utilized to detect ASSVd in field-collected samples. The RT-RPA assay developed in the present study provides a potentially valuable means for improving the detection of ASSVd in viroid-free certification programs, especially in resource-limited conditions.

• The Plant Pathology Journal
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• v.38 no.4
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• pp.417-422
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• 2022

Apple stem grooving virus (ASGV) is a destructive viral pathogen of pome fruit trees that causes significant losses to fruit production worldwide. Obtaining ASGV-free propagation materials is essential to reduce economic losses, and accurate and sensitive detection methods to screen ASGV-free plantlets during in vitro propagation are urgently necessary. In this study, ASGV was sensitively and accurately quantified from in vitro propagated apple plantlets using a reverse transcription droplet digital polymerase chain reaction (RT-ddPCR) assay. The optimized RT-ddPCR assay was specific to other apple viruses, and was at least 10-times more sensitive than RT-real-time quantitative PCR assay. Furthermore, the optimized RT-ddPCR assay was validated for the detection and quantification of ASGV using micropropagated apple plantlet samples. This RT-ddPCR assay can be utilized for the accurate quantitative detection of ASGV infection in ASGV-free certification programs, and can thus contribute to the production of ASGV-free apple trees.

• The Plant Pathology Journal
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• v.22 no.2
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• pp.168-173
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• 2006

To develop the diagnostic method for the viral infection in apple, the partial genes corresponding to the N-terminal region of RNA polymerase of Apple stem grooving virus (ASGV) and coat protein of Apple chlorotic leaf spot virus (ACLSV) were characterized from the infected apple cultivars in Korea. Based on the nucleotide sequences of the characterized partial genes, the virus gene-specific primers were designed for the detection of ASGV and ACLSV infected in species of Malus. The RT-PCR using the primers for the genes of ASGV and ACLSV successfully gave rise to 404 and 566 bp DNA fragments, respectively. Using those viral gene-specific primers, the multiplex RT-PCR assays were also established to diagnose the mixed infection by ASGV and ACLSV simultaneously. Furthermore, the control primers, which have to be included for the RT-PCR as an internal control, were designed using the nucleotide sequence of the gene encoding elongation factor $1{\alpha}(EF1{\alpha})$. This multiplex RT-PCR including the control primers provides more reliable, rapid and sensitive assay for the detection of ASGV and ACLSV infected in Korean apple cultivars.

• Smart Media Journal
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• v.12 no.10
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• pp.19-28
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• 2023

In the fruit harvesting field, interest in automatic robot harvesting is increasing due to various seasonality and rising harvesting costs. Accurate apple detection is a difficult problem in complex orchard environments with changes in light, vibrations caused by wind, and occlusion of leaves and branches. In this paper, we introduce a dataset and an adaptive heatmap regression model that are advantageous for robot automatic apple harvesting. The apple dataset was labeled with not only the apple location but also the visibility. We propose a method to detect the center point of an apple using an adaptive heatmap regression model that adjusts the Gaussian shape according to visibility. The experimental results showed that the performance of the proposed method was applicable to apple harvesting robots, with MAP@K of 0.9809 and 0.9801 when K=5 and K=10, respectively.

• Journal of Biosystems Engineering
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• v.36 no.6
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• pp.484-490
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• 2011

Defects exist underneath the fruit skin are not easily discernable by using conventional color imaging technique in the visible wavelength ranges. Development of sensitive detection methods for the defects is necessary to ensure accurate quality sorting of fruits. Hyperspectral imaging techniques, which combine the features of image and spectroscopy to acquire spatial and spectral information simultaneously, have demonstrated good potentials for identifying and detecting anomalies on biological substances. In this study, a high spatial resolution hyperspectral reflectance technique was presented as a tool for detecting bruises on apple. The two-band ratio (494 nm / 952 nm) and simple threshold methods were applied to investigate the feasibility of discriminating the bruises from sound tissue of apple. The pixel wise accuracy of the discrimination was 74%. The resultant images processed with selected wavebands and morphologic algorithm distinctively showed the early stages of bruises on apple which were not discernable by naked eyes as well as a conventional color camera. Results demonstrated good potential of the hyperspectral reflectance imaging for detection of bruises on apple.

• Proceedings of the Korean Society of Plant Pathology Conference
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• 2003.10a
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• pp.143.2-143
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• 2003

A rapid and sensitive assay for the specific detection of plant viroids using reverse transcription-polymerase chain reaction(RT-PCR) has been developed already. The nested RT-PCR assay cloud be applied for the detection of apple scar skin viroid(ASSVd) from young leaves and other tissues. ASSVd has central conserved region(CCR), terminal left(T$\sub$L/) and terminal right(T$\sub$R/) domain. Primers were designed from these regions. Primer sets were successfully applicable for the amplification of full length or partial region of ASSVd by nested RT-PCR. Nested RT-PCR assay was more sensitive and accurate method to detect ASSVd from young trees during the early time of apple cultivation.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.21 no.3
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• pp.81-89
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• 2021

Under natural conditions, Apple detection has the problems of occlusion and small object detection difficulties. This paper proposes an improved model based on SSD. The SSD backbone network VGG16 is replaced with the ResNet50 network model, and the receptive field structure RFB structure is introduced. The RFB model amplifies the feature information of small objects and improves the detection accuracy of small objects. Combined with the attention mechanism (SE) to filter out the information that needs to be retained, the semantic information of the detection objectis enhanced. An improved SSD algorithm is trained on the VOC2007 data set. Compared with SSD, the improved algorithm has increased the accuracy of occlusion and small object detection by 3.4% and 3.9%. The algorithm has improved the false detection rate and missed detection rate. The improved algorithm proposed in this paper has higher efficiency.

• The Plant Pathology Journal
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• v.35 no.2
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• pp.164-171
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• 2019

An assay for detecting Apple scar skin viroid (ASSVd) was developed based on nucleic acid sequence based amplification (NASBA) in combination with realtime detection during the amplification process using molecular beacon. The ASSVd specific primers for amplification of the viroid RNA and molecular beacon for detecting the viroid were designed based on highly conserved regions of several ASSVd sequences including Korean isolate. The assay had a detection range of $1{\times}10^4$ to $1{\times}10^{12}$ ASSVd RNA $copies/{\mu}l$ with reproducibility and precision. Following the construction of standard curves based on time to positive (TTP) value for the serial dilutions ranging from $1{\times}10^7$ to $1{\times}10^{12}$ copies of the recombinant plasmid, a standard regression line was constructed by plotting the TTP values versus the logarithm of the starting ASSVd RNA copy number of 10-fold dilutions each. Compared to the established RT-PCR methods, our method was more sensitive for detecting ASSVd. The real-time quantitative NASBA method will be fast, sensitive, and reliable for routine diagnosis and selection of viroid-free stock materials. Furthermore, real-time quantitative NASBA may be especially useful for detecting low levels in apple trees with early viroid-infection stage and for monitoring the influence on tree growth.