Detection and isolation of Ceratocystis fimbriata in mango trees on semi-selective medium

Brito, R. A. S.; Cavalcante, Gilcianny Pignata; Borel, Filipe Constantino; Maffia, Luiz A.

doi:10.1007/s10658-019-01785-0

Cited by 5 publications

(2 citation statements)

References 2 publications

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“…Another subset of surface-sterilized wood sections was wrapped between carrot slices to bait for Ceratocystis spp. ( Brito et al, 2019 ; Moller and DeVay, 1968 ). Baiting was also performed by inserting diseased tissue into freshly harvested cacao pods and cucumber fruit in an attempt to isolate Phytophthora .…”

Section: Methodsmentioning

confidence: 99%

Identification and Characterization of Ceratocystis fimbriata Causing Lethal Wilt on the Lansium Tree in Indonesia

et al. 2021

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Bark canker, wood discoloration, and wilting of the duku tree ( Lansium domesticum ) along the watershed of Komering River, South Sumatra Province, Indonesia first appeared in 2013. The incidence of tree mortality was 100% within 3 years in badly infected orchards. A Ceratocystis species was consistently isolated from the diseased tissue and identified by morphological and sequence analyses of the internal transcribed spacer (ITS) and β-tubulin regions. Pathogenicity tests were conducted and Koch’s postulates were confirmed. The fungus was also pathogenic on Acacia mangium , but was less pathogenic on mango. Partial flooding was unfavourable for disease development. Two described isolates (WRC and WBC) had minor variation in morphology and DNA sequences, but the former exhibited a more pathogenic on both duku and acacia. The ITS phylogenies grouped the most pathogenic isolate (WRC) causing wilting of the duku tree within the aggressive and widely distributed ITS5 haplotype of C. fimbriata .

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Section: Methodsmentioning

confidence: 99%

Identification and Characterization of Ceratocystis fimbriata Causing Lethal Wilt on the Lansium Tree in Indonesia

et al. 2021

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“…Crop losses are being experienced by farmers due to plant diseases. Farmers used excessive amounts of pesticides to cure plant diseases because they lacked knowledge of the diseases affecting the plants (Brito et al., 2019; Raj et al., 2017). Therefore, careful care is needed for the early diagnosis of mango plant leaf diseases (Wu et al., 2020).…”

Section: Introductionmentioning

confidence: 99%

IBSHC: Improved BIRCH‐based segmentation and hybrid classification model for Mango Disease Detection

Lavanya,

Packialatha

2024

Journal of Phytopathology

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Mangoes are referred to as the ‘king of fruits’ and are in great demand; yet, in order to optimize profitability, disease control is crucial. Automatic leaf disease segmentation and detection remains challenge due to the variability of symptoms. The basic necessity for any computer‐aided system is an accurate approach for identifying diseases. This paper intends to propose a four‐stage Mango Disease Detection model, IBSHC (Improved BIRCH‐based Segmentation and Hybrid Classification model for mango disease classification) that covers preprocessing, segmentation, feature extraction, and classification to address this detection problem. Initially, preprocessing phase is performed by using the Gaussian filtering process. Then the improved Balanced Iterative Reducing and Clustering using Hierarchies (BIRCH) process‐based segmentation is carried out in the segmentation phase. Subsequently, the Colour histogram, shape index histogram, improved significant Local Binary Patterns (LBP), and Resnet are the features extracted during the feature extraction phase. Finally, a hybrid classification model is developed for identifying the healthy and diseased mangoes. The optimized Dilated Residual Networks (DRN) and optimized Long Short‐Term Memory (LSTM) classifiers are employed in this hybrid model, and the PMRFO (Proposed Manta‐Ray‐Foraging Optimization) algorithm is used to optimize the weights of these two classifiers. Finally, the efficiency of the suggested PMRFO is contrasted over other conventional methods, and also the disease detection of the proposed model has better accuracy in each learning percentage like 60th LP = 92.5%, 70th LP = 93%, 80th LP = 94.02%, and 90th LP = 96.5%, respectively.

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Trichoderma species show biocontrol potential against Ceratocystis wilt in mango plants

et al. 2020

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Detection and isolation of Ceratocystis fimbriata in mango trees on semi-selective medium

Cited by 5 publications

References 2 publications

Identification and Characterization of Ceratocystis fimbriata Causing Lethal Wilt on the Lansium Tree in Indonesia

Identification and Characterization of Ceratocystis fimbriata Causing Lethal Wilt on the Lansium Tree in Indonesia

IBSHC: Improved BIRCH‐based segmentation and hybrid classification model for Mango Disease Detection

Trichoderma species show biocontrol potential against Ceratocystis wilt in mango plants

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