• IEIE Transactions on Smart Processing and Computing
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• 제4권4호
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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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• 제19권3호
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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.

• 식물병연구
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• 제27권2호
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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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• 제38권4호
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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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• 제22권2호
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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.

• 스마트미디어저널
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• 제12권10호
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• pp.19-28
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• 2023

과실 수확 분야에서 다양한 계절성과 수확 비용 상승 등으로 자동 로봇 수확에 대한 관심이 증가하고 있다. 빛의 변화, 바람에 의한 진동, 나뭇잎 및 가지 겹침 등 복잡한 과수원 환경에서 정확한 사과 검출은 어려운 문제이다. 본 논문에서는 로봇 자동 사과 수확에 유리한 데이터셋과 적응형 히트맵 회귀 모델을 소개한다. 사과 데이터셋은 사과 위치뿐만 아니라 가시성을 같이 레이블링하였다. 가시성에 따라 가우시안 모양을 조절하는 적응형 히트맵 회귀 모델을 사용하여 사과 중심점을 검출하는 방법을 제안한다. 실험 결과 MAP@K가 K=5와 K=10일 때 0.9809, 0.9801로 사과 수확 로봇에 응용 가능한 성능을 나타내었다.

• Journal of Biosystems Engineering
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• 제36권6호
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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.

• 한국식물병리학회:학술대회논문집
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• 한국식물병리학회 2003년도 정기총회 및 추계학술발표회
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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.

• 한국인터넷방송통신학회논문지
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• 제21권3호
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• pp.81-89
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• 2021

자연 조건에서 Apple 감지에는 가림 문제와 작은 대상 감지 어려움이 있다. 본 논문은 SSD 기반의 개선 된 모델을 제안한다. SSD 백본 네트워크 VGG16은 ResNet50 네트워크 모델로 대체되고 수용 필드 구조 RFB 구조가 도입되었다. RFB 모델은 작은 표적의 특징 정보를 증폭하고 작은 표적의 탐지 정확도를 향상시킨다. 유지해야 하는 정보를 필터링하기 위해 주의 메커니즘 (SE)과 결합하면 감지 대상의 의미 정보가 향상된다. 향상된 SSD 알고리즘은 VOC2007 데이터 세트에 대해 학습된다. SSD에 비해 개선 된 알고리즘은 폐색 및 작은 표적 탐지의 정확도를 3.4 % 및 3.9 % 향상 시켰다. 이 알고리즘은 오 탐지율과 누락된 감지율을 향상 시켰다. 본 논문에서 제안한 개선 된 알고리즘은 더 높은 효율성을 갖는다.

• The Plant Pathology Journal
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• 제35권2호
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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.