Development of a Core Set of SSR Markers for Cultivar Identification and Seed Purity Tests in Oriental Melon (Cucumis melo L. var. makuwa)

Nguyen, Nam Ngoc; Kwon, Yong-Sham; Park, Jae-Ryoung; Sim, Sung-Chur

doi:10.12972/kjhst.20190011

Cited by 10 publications

(3 citation statements)

References 34 publications

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“…Its cultivation is widespread globally, particularly on subtropical and tropical regions [1], [2]. Melons are esteemed for their delightful sweet tastes, crisp textures, and distinct fragrances [3]. The flesh of the melon is also very good for you because it is full of ascorbic acid (vitamin C), which is a water-soluble vitamin that is known to be one of the safest and most effective nutrients [4], [5].…”

Section: Introductionmentioning

confidence: 99%

Utilizing UAV Data for Neural Network-based Classification of Melon Leaf Diseases in Smart Agriculture

Robi,

Ahmad,

Izhar

et al. 2024

IJACSA

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Integrating unmanned aerial vehicle (UAV) technology with plant disease detection is a significant advancement in agricultural surveillance, marking the beginning of a transformational era characterised by innovation. Traditionally, farmers have had to rely on manual visual inspections to identify melon leaf diseases, which proves to be a time-consuming and costly process in terms of labour. This paper aims to use UAV technology for plant disease detection to achieve notable progress in agricultural surveillance. Incorporating UAV technology, specifically utilising the You Only Look Once version 8 (YOLOv8) deeplearning model, is revolutionary in precision agriculture. This study uses UAV imagery in precision agriculture to explore the utility of YOLOv8, a powerful deep-learning model, for detecting diseases in melon leaves. The labelled dataset is created by annotating disease-affected areas using bounding boxes. The YOLOv8 model has been trained using a labelled dataset to detect and classify various diseases accurately. Following the training, the performance of YOLOv8 stands out significantly compared to other models, boasting an impressive accuracy of 83.2%. This high level of accuracy underscores its effectiveness in object detection tasks and positions it as a robust choice in computer vision applications. It has been shown that rigorous evaluation can help find diseases, which suggests that it could be used for early intervention in precision farming and to change how crop management systems work. This has the potential to assist farmers in promptly identifying and addressing plant issues, hence altering their crop management practices.

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Section: Introductionmentioning

confidence: 99%

Utilizing UAV Data for Neural Network-based Classification of Melon Leaf Diseases in Smart Agriculture

Robi,

Ahmad,

Izhar

et al. 2024

IJACSA

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“…Oriental melon (Cucumis melo L.) belongs to the Cucurbitaceae family and is one of six species (inodorus, reticulatus, cantalupensis, acidulous, saccharinus, and makuwa), and is called 'Chamoe' in the Republic of Korea [1]. It is a commercially valuable fruit cultivated in Korea, Japan, and China because of its sweet and crisp taste, flavor, juicy flesh, and valuable nutrients (such as vitamin C and β-Carotene) [2]. Oriental melon is conventionally cultivated in soil culture; however, the culture method may cause a high workload for farmers, changes in soil environments, and so on.…”

Section: Introductionmentioning

confidence: 99%

Determination of Internal Quality Indices in Oriental Melon Using Snapshot-Type Hyperspectral Image and Machine Learning Model

et al. 2022

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In this study, we aimed to develop a prediction model of the solid solutions concentration (SSC) and moisture content (MC) in oriental melon with snapshot-type hyperspectral imagery (Visible (VIS): 460–600 nm, 16 bands; Red-Near infrared (Red-NIR): 600–860 nm, 15 bands) using a machine learning model. The oriental melons were cultivated in a hydroponic greenhouse, Republic of Korea, and a total of 91 oriental melons that were harvested from March to April of 2022 were used as samples. The SSC and MC of the oriental melons were measured using destructive methods after taking hyperspectral imagery of the oriental melons. The reflectance spectrum obtained from the hyperspectral imagery was processed by the standard normal variate (SNV) method. Variable importance in projection (VIP) scores were used to select the bands related to SSC and MC. As a result, ten (609, 736, 561, 849, 818, 489, 754, 526, 683, and 597 nm) and six (609, 736, 561, 818, 849, and 489 nm) bands were selected for the SSC and MC, respectively. Four machine learning models, support vector regression (SVR), ridge regression (RR), K-nearest neighbors regression (K-NNR), and random forest regression (RFR), were used to develop models to predict SSC and MC, and their performances were compared. The SVR showed the best performance for predicting both the SSC and MC of the oriental melons. The SVR model achieved a relatively high accuracy with R2 values of 0.86 and 0.74 and RMSE values of 1.06 and 1.05 for SSC and MC, respectively. However, it will be necessary to carry out more experiments under various conditions, such as differing maturities of fruits and varying light sources and environments, to achieve more comprehensive predictions and apply them to monitoring robots in the future. Nevertheless, it is considered that the snapshot-type hyperspectral imagery aided by SVR would be a useful tool to predict the SSC and MC of oriental melon. In addition, if the maturity classification model for the oriental melon can be applied to fields, it could lead to less labor and result in high-quality oriental melon production.

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“…Cucumis melo is an important vegetable crop and includes several subspecies, where introgression from unknown germplasms continues to be a critical problem in breeding programs. In recent years, melon F 1 hybrids were developed and released into the seed market by various seed companies (Nguyen et al, 2019;Kishor et al, 2020). The success of any breeding program mostly depends on an adequate supply of genetically pure hybrid seeds in the seed market.…”

Section: Introductionmentioning

confidence: 99%

Identification and Purity Test of Melon Cultivars and F1 Hybrids Using Fluidigm-based SNP Markers

Kishor¹,

Noh²,

Song³

et al. 2020

HST

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In melon (Cucumis melo L.), the grow-out test (GOT) has been traditionally used as a genetic purity test. However, this method is time-consuming, space-demanding, and associated with ambiguous classification of the genotypes. Molecular markers have proved to be an efficient tool in genotyping analyses. In the present study, a total of 96 genome-wide single nucleotide polymorphism (SNP) markers differentiated 85 melon F1 hybrid plants from their parental lines and six other PT melon breeding lines via high-throughput Fluidigm genotyping. Of these, 39 SNP markers showed polymorphism between the parents. Additionally, SNP analysis and population structure analysis showed that several F1 hybrid plants were associated with outcrossing during the breeding program. Unweighted pair group method with arithmetic average (UPGMA) analysis revealed that most of the contaminated plants were closely sub-grouped with the 7_PT1 breeding line, suggesting possible outcrossing with 7_PT1. By combining with simple DNA extraction, the Fluidigm-based SNP marker analysis proved to be a simple and effective approach for the genetic purity analysis of F1 hybrids and melon cultivars.

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Development of a Core Set of SSR Markers for Cultivar Identification and Seed Purity Tests in Oriental Melon (Cucumis melo L. var. makuwa)

Cited by 10 publications

References 34 publications

Utilizing UAV Data for Neural Network-based Classification of Melon Leaf Diseases in Smart Agriculture

Utilizing UAV Data for Neural Network-based Classification of Melon Leaf Diseases in Smart Agriculture

Determination of Internal Quality Indices in Oriental Melon Using Snapshot-Type Hyperspectral Image and Machine Learning Model

Identification and Purity Test of Melon Cultivars and F1 Hybrids Using Fluidigm-based SNP Markers

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