Evaluating responses of Caucasian apple (Malus orientalis) from Hyrcanian forests to fire blight (Erwinia amylovora) using an in vitro assay

Amirchakhmaghi, Narjes; Yousefzadeh, Hamed; Hosseinpour, Batool; Abdollahi, Hamid; Larsen, B.

doi:10.1080/15427528.2021.2012731

Cited by 2 publications

(2 citation statements)

References 23 publications

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“…Apple scabs, apple rust, and cedar are the most common apple diseases [7]. Fire blight fire-like appearance on infected plant parts, and secretion of golden color bacterial secretion is seen on the stem [8]. Powdery mildew is a serious disease that wreaks havoc on buds, new shoots, and leaves.…”

Section: Introductionmentioning

confidence: 99%

A Framework of Deep Optimal Features Selection for Apple Leaf Diseases Recognition

Rehman¹,

Khan²,

Alhaisoni³

et al. 2023

Computers, Materials &Amp; Continua

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Identifying fruit disease manually is time-consuming, expertrequired, and expensive; thus, a computer-based automated system is widely required. Fruit diseases affect not only the quality but also the quantity. As a result, it is possible to detect the disease early on and cure the fruits using computer-based techniques. However, computer-based methods face several challenges, including low contrast, a lack of dataset for training a model, and inappropriate feature extraction for final classification. In this paper, we proposed an automated framework for detecting apple fruit leaf diseases using CNN and a hybrid optimization algorithm. Data augmentation is performed initially to balance the selected apple dataset. After that, two pre-trained deep models are fine-tuning and trained using transfer learning. Then, a fusion technique is proposed named Parallel Correlation Threshold (PCT). The fused feature vector is optimized in the next step using a hybrid optimization algorithm. The selected features are finally classified using machine learning algorithms. Four different experiments have been carried out on the augmented Plant Village dataset and yielded the best accuracy of 99.8%. The accuracy of the proposed framework is also compared to that of several neural nets, and it outperforms them all.

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Section: Introductionmentioning

confidence: 99%

A Framework of Deep Optimal Features Selection for Apple Leaf Diseases Recognition

Rehman¹,

Khan²,

Alhaisoni³

et al. 2023

Computers, Materials &Amp; Continua

View full text Add to dashboard Cite

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“…Malus orientalis , native to the Caucasus and Middle East, is also a likely contributor to the domesticated apple ( Cornille et al., 2012 ; Cornille et al., 2014 ; Amirchakhmahgi et al., 2018 ). Its attributes of interest to breeding programs include late blooming, environmental adaptation, fire blight resistance ( Amirchakhmaghi et al., 2022 ), and long-term storage ( Khadivi et al., 2020 ; Moradi et al., 2022 ). M. orientalis in the NPGS was primarily collected in exploration trips to Turkey, Russia, Armenia, and the Republic of Georgia between 1998 and 2004 ( Volk et al., 2009b ).…”

Section: Introductionmentioning

confidence: 99%

DNA profiling with the 20K apple SNP array reveals Malus domestica hybridization and admixture in M. sieversii, M. orientalis, and M. sylvestris genebank accessions

et al. 2022

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The USDA-ARS National Plant Germplasm System (NPGS) apple collection in Geneva, NY, USA maintains accessions of the primary Malus domestica (Suckow) Borkh. progenitor species M. sieversii (Ledeb.) M. Roem., M. orientalis Uglitzk., and M. sylvestris (L.) Mill. Many of these accessions originated from seeds that were collected from wild populations in the species’ centers of diversity. Some of these accessions have fruit phenotypes that suggest recent M. domestica hybridization, which if true would represent crop contamination of wild species populations and mislabeled species status of NPGS accessions. Pedigree connections and admixture between M. domestica and its progenitor species can be readily identified with apple SNP array data, despite such arrays not being designed for these purposes. To investigate species purity, most (463 accessions) of the NPGS accessions labeled as these three progenitor species were genotyped using the 20K apple SNP array. DNA profiles obtained were compared with a dataset of more than 5000 unique M. domestica apple cultivars. Only 212 accessions (151 M. sieversii, 26 M. orientalis, and 35 M. sylvestris) were identified as “pure” species representatives because their DNA profiles did not exhibit genotypic signatures of recent hybridization with M. domestica. Twenty-one accessions (17 M. sieversii, 1 M. orientalis, and 3 M. sylvestris) previously labeled as wild species were instead fully M. domestica. Previously unrealized hybridization and admixture between wild species and M. domestica was identified in 230 accessions (215 M. sieversii, 9 M. orientalis, and 6 M. sylvestris). Among these species-mislabeled accessions, ‘Alexander’, ‘Gold Reinette’, ‘Charlamoff’, ‘Rosmarina Bianca’, and ‘King of the Pippins’ were the most frequently detected M. domestica parents or grandparents. These results have implications for collection management, including germplasm distribution, and might affect conclusions of previous research focused on these three progenitor species in the NPGS apple collection. Specifically, accessions received from the NPGS for breeding and genomics, genetics, and evolutionary biology research might not be truly representative of their previously assigned species.

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Evaluating responses of Caucasian apple (Malus orientalis) from Hyrcanian forests to fire blight (Erwinia amylovora) using an in vitro assay

Cited by 2 publications

References 23 publications

A Framework of Deep Optimal Features Selection for Apple Leaf Diseases Recognition

A Framework of Deep Optimal Features Selection for Apple Leaf Diseases Recognition

DNA profiling with the 20K apple SNP array reveals Malus domestica hybridization and admixture in M. sieversii, M. orientalis, and M. sylvestris genebank accessions

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