First Report of Brown Leaf Spot on Kiwifruit Caused by <i>Corynespora cassiicola</i> in Sichuan, China

Cui, Yuting; Gong, Guoshu; Yu, Xiaodong; Xu, Jing; Wen, Xiaopeng; Zhang, M.; Chen, H.; Zheng, X.; Zhou, You; Chang, Xiaoli

doi:10.1094/pdis-08-14-0808-pdn

Cited by 15 publications

(12 citation statements)

References 3 publications

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“…More than 30 kinds of kiwifruit diseases exist, including bacterial canker disease, brown spot, root rot, flower rot, fruit soft rot, phytophthora, leaf blight, root-knot nematode disease, black spot, and other common and severe diseases (Jeong et al, 2008 ). Kiwifruit brown spot caused by the fungal pathogen Corynespora cassiicola is particularly problematic in Sichuan, China, occurring and spreading rapidly in July and August in particular (Cui et al, 2015 ). It leads to leaf desiccation and early leaf abscission, with high disease rates of 60%−100%, thus adversely affecting photosynthesis, causing branches to wither, fruit loss, and yield reductions of 30%−50%.…”

Section: Introductionmentioning

confidence: 99%

“…It leads to leaf desiccation and early leaf abscission, with high disease rates of 60%−100%, thus adversely affecting photosynthesis, causing branches to wither, fruit loss, and yield reductions of 30%−50%. Thus, the kiwifruit brown spot has become a significant obstacle to the kiwifruit industry's growth (Cui et al, 2015 ; Zhang et al, 2021 ).…”

Section: Introductionmentioning

confidence: 99%

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Evaluation of the control efficacy of antagonistic bacteria from V-Ti magnetite mine tailings on kiwifruit brown spots in pot and field experiments

Cui,

Zhu,

Dong

et al. 2024

Front. Microbiol.

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Seemingly barren heavy-metal-polluted vanadium (V) and titanium (Ti) magnetite mine tailings contain various functional microbes, yet it is unclear whether this includes microbial resources relevant to the biological control of plant diseases. Kiwifruit brown leaf spot disease, caused by Corynespora cassiicola, can seriously reduce kiwifruit yield. To discover effective control measures for kiwifruit leaf spot, 18 bacteria strains among 136 tailing-isolated bacteria from V-Ti magnetite mine tailings were identified as inhibiting C. cassiicola by the confrontation plate method, indicating that antagonistic bacteria surviving in the V-Ti magnetite mine tailings were present at a low level. The 18 antagonistic strains could be divided into two BOX-A1R clusters. The 13 representative strains that were selected for phylogenetic tree construction based on their 16S rRNA sequences belonged to the Bacillus genus. Five predominant strains exhibited different toxin-production times and intensities, with four of them initiating toxin production at 32 h. Among them, Bacillus sp. KT-10 displayed the highest bacteriostatic rate (100%), with a 37.5% growth inhibition rate and an antagonistic band of 3.2 cm against C. cassiicola. Bacillus sp. KT10 also showed a significant inhibitory effect against the expansion speed of kiwifruit brown spots in the pot. The relative control effect was 78.48 and 83.89% at 7 days after the first and last spraying of KT-10 dilution, respectively, confirming a good effect of KT-10 on kiwifruit brown leaf spots in the field. This study demonstrated for the first time that there are some antagonistic bacteria to pathogenic C. cassiicola in V-Ti magnetite mine tailings, and Bacillus sp. KT10 was found to have a good control effect on kiwifruit brown leaf spots in pots and fields, which provided an effective biological control measurement for kiwifruit brown leaf spots.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Evaluation of the control efficacy of antagonistic bacteria from V-Ti magnetite mine tailings on kiwifruit brown spots in pot and field experiments

Cui,

Zhu,

Dong

et al. 2024

Front. Microbiol.

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“…Its typical symptoms consist of brown circular to irregular target spots with obvious concentric rings appearing on the leaves. The lesions are extended and coalesced, causing leaf necrosis and finally defoliation [ 9 , 11 ]. Brown spot disease and canker disease on leaves are serious and difficult to distinguish in the daily planting process.…”

Section: Introductionmentioning

confidence: 99%

Two-Stage Detection Algorithm for Kiwifruit Leaf Diseases Based on Deep Learning

Jia

Wang

Xiang

et al. 2022

Plants

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The prevention and management of crop diseases play an important role in agricultural production, but there are many types of crop diseases and complex causes, and their prevention and identification add difficulties to the process. The traditional methods of identifying diseases mostly rely on human visual and manual inspection, which requires a certain amount of expert knowledge and experience. There are shortcomings such as strong subjectivity and low accuracy. This paper takes the common diseases of kiwifruit as the research object. Based on deep learning and computer vision models, and given the influence of a complex background in actual scenes on the detection of diseases, as well as the shape and size characteristics of diseases, an innovative method of target detection and semantic segmentation was proposed to identify diseases accurately. The main contributions of this research are as follows: We produced the world’s first high-quality dataset on kiwifruit. We used the target detection algorithm YOLOX, we stripped the kiwi leaves from the natural background and removed the influencing factors existing in the complex background. Based on the mainstream semantic segmentation networks UNet and DeepLabv3+, the experimental results showed that the ResNet101 network achieved the most effective results in the identification of kiwi diseases, with an accuracy rate of 96.6%. We used the training method of learning rate decay to further improve the training effect without increasing the training cost. After experimental verification, our two-stage disease detection algorithm had the advantages of high accuracy, strong robustness, and wide detection range, which provided a more efficient solution for solving the problem of precise monitoring of crop growth environment parameters.

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“…The specimen was isolated from a brown leaf spot disease of cultivated kiwifruit (Actinidia chinensis) in Sichuan Province, China (103.24 E; 30.15 N). The sample was identified as C. cassiicola according to Cui et al (2015). The strain was stored in Sichuan Academy of Agricultural Sciences (No.…”

mentioning

confidence: 99%

“…The sample was identified as C. cassiicola according to Cui et al. ( 2015 ). The strain was stored in Sichuan Academy of Agricultural Sciences (No.…”

mentioning

confidence: 99%

Characterization of the complete mitochondrial genome of Corynespora cassiicola (Pleosporales: Dothideomycetes), with its phylogenetic analysis

Chen

et al. 2019

Mitochondrial DNA Part B

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Corynespora cassiicola is a well-known plant pathogen with a broad host range and diverse lifestyles. In this study, we presented the complete mitochondrial genome (mitogenome) of C. cassiicola for the first time. It has a total length of 40,752 bp, which encodes 17 protein-coding genes (PCGs), 2 ribosomal RNA genes (rRNA), and 27 transfer RNA (tRNA) genes. The nucleotide composition of the mitogenome is: A (36.24%), T (34.62%), G (15.74%), and C (13.39%). Phylogenetic analysis revealed that C. cassiicola has a close relationship with Didymella pinodes from Didymellaceae.

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First Report of Brown Leaf Spot on Kiwifruit Caused by Corynespora cassiicola in Sichuan, China

Cited by 15 publications

References 3 publications

Evaluation of the control efficacy of antagonistic bacteria from V-Ti magnetite mine tailings on kiwifruit brown spots in pot and field experiments

Evaluation of the control efficacy of antagonistic bacteria from V-Ti magnetite mine tailings on kiwifruit brown spots in pot and field experiments

Two-Stage Detection Algorithm for Kiwifruit Leaf Diseases Based on Deep Learning

Characterization of the complete mitochondrial genome of Corynespora cassiicola (Pleosporales: Dothideomycetes), with its phylogenetic analysis

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