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Sustainable Strategies for Tea Crops Protection
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Sustainable Strategies for Tea Crops Protection

A special issue of Plants (ISSN 2223-7747). This special issue belongs to the section "Plant Protection and Biotic Interactions".

Deadline for manuscript submissions: 20 February 2025 | Viewed by 2370

Special Issue Editors

Dr. Lei Bian

E-Mail Website
Guest Editor
Tea Research Institute, Chinese Academy of Agricultural Sciences, Hangzhou 310008, China
Interests: tea plants; tea disease; pest management; protection
Dr. Shan Jin

E-Mail Website
Co-Guest Editor
College of Horticulture, Fujian Agriculture and Forestry University, Fuzhou, China
Interests: tea; tea quality; tea Chemistry; ecological tea garden

Special Issue Information

Dear Colleagues,

Tea, one of the most widely consumed beverages worldwide, plays a significant role in the global agricultural and economic landscapes. However, tea crops are confronted with numerous threats, including pests, diseases, climate change, and environmental degradation. It is paramount to develop innovative and sustainable strategies to protect tea crops and ensure their long-term viability.

This Special Issue aims to address the pressing challenges faced by the tea industry in ensuring the health and productivity of tea crops, while minimizing the use of harmful chemicals and promoting ecological sustainability. We welcome reviews and research articles that explore novel pest and disease management techniques, eco-friendly and bio-based interventions, precision farming methods, and the utilization of beneficial microbes in tea cultivation.

Together, we can shape the future of sustainable tea farming and pave the way for a thriving tea industry. We look forward to receiving your valuable contributions.

Dr. Lei Bian
Dr. Shan Jin
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Plants is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2700 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • tea diseases
  • pest management
  • ecological sustainability

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Published Papers (2 papers)

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Research

24 pages, 7302 KiB  
Article
CTDUNet: A Multimodal CNN–Transformer Dual U-Shaped Network with Coordinate Space Attention for Camellia oleifera Pests and Diseases Segmentation in Complex Environments
by Ruitian Guo, Ruopeng Zhang, Hao Zhou, Tunjun Xie, Yuting Peng, Xili Chen, Guo Yu, Fangying Wan, Lin Li, Yongzhong Zhang and Ruifeng Liu
Plants 2024, 13(16), 2274; https://doi.org/10.3390/plants13162274 - 15 Aug 2024
Viewed by 898
Abstract
Camellia oleifera is a crop of high economic value, yet it is particularly susceptible to various diseases and pests that significantly reduce its yield and quality. Consequently, the precise segmentation and classification of diseased Camellia leaves are vital for managing pests and diseases [...] Read more.
Camellia oleifera is a crop of high economic value, yet it is particularly susceptible to various diseases and pests that significantly reduce its yield and quality. Consequently, the precise segmentation and classification of diseased Camellia leaves are vital for managing pests and diseases effectively. Deep learning exhibits significant advantages in the segmentation of plant diseases and pests, particularly in complex image processing and automated feature extraction. However, when employing single-modal models to segment Camellia oleifera diseases, three critical challenges arise: (A) lesions may closely resemble the colors of the complex background; (B) small sections of diseased leaves overlap; (C) the presence of multiple diseases on a single leaf. These factors considerably hinder segmentation accuracy. A novel multimodal model, CNN–Transformer Dual U-shaped Network (CTDUNet), based on a CNN–Transformer architecture, has been proposed to integrate image and text information. This model first utilizes text data to address the shortcomings of single-modal image features, enhancing its ability to distinguish lesions from environmental characteristics, even under conditions where they closely resemble one another. Additionally, we introduce Coordinate Space Attention (CSA), which focuses on the positional relationships between targets, thereby improving the segmentation of overlapping leaf edges. Furthermore, cross-attention (CA) is employed to align image and text features effectively, preserving local information and enhancing the perception and differentiation of various diseases. The CTDUNet model was evaluated on a self-made multimodal dataset compared against several models, including DeeplabV3+, UNet, PSPNet, Segformer, HrNet, and Language meets Vision Transformer (LViT). The experimental results demonstrate that CTDUNet achieved an mean Intersection over Union (mIoU) of 86.14%, surpassing both multimodal models and the best single-modal model by 3.91% and 5.84%, respectively. Additionally, CTDUNet exhibits high balance in the multi-class segmentation of Camellia oleifera diseases and pests. These results indicate the successful application of fused image and text multimodal information in the segmentation of Camellia disease, achieving outstanding performance. Full article
(This article belongs to the Special Issue Sustainable Strategies for Tea Crops Protection)
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12 pages, 1887 KiB  
Article
Mixture of Synthetic Plant Volatiles Attracts More Stick Tea Thrips Dendrothrips minowai Priesner (Thysanoptera: Thripidae) and the Application as an Attractant in Tea Plantations
by Zhengwei Xu, Guowei Zhang, Yan Qiu, Zongxiu Luo, Xiaoming Cai, Zhaoqun Li, Lei Bian, Nanxia Fu, Li Zhou, Fida Hussain Magsi, Zongmao Chen, Xiaoming Zhang and Chunli Xiu
Plants 2024, 13(14), 1944; https://doi.org/10.3390/plants13141944 - 15 Jul 2024
Viewed by 1107
Abstract
The stick tea thrip (Dendrothrips minowai) is one of the most serious sucking pests of tea plants (Camellia sinensis) in China, North Korea, and Japan. Plant volatile lures are widely used for both monitoring and mass trapping. Previously, we [...] Read more.
The stick tea thrip (Dendrothrips minowai) is one of the most serious sucking pests of tea plants (Camellia sinensis) in China, North Korea, and Japan. Plant volatile lures are widely used for both monitoring and mass trapping. Previously, we demonstrated that sticky traps baited with p-anisaldehyde, eugenol, farnesene, or 3-methyl butanal captured significantly more D. minowai in tea plantations, with p-anisaldehyde notably capturing the most. In this study, we showed that D. minowai adults exhibited significantly higher attraction to mixtures of p-anisaldehyde, eugenol, and farnesene compared to an equivalent dose of p-anisaldehyde alone in H-tube olfactometer assays under laboratory conditions. Moreover, in field experiments conducted in 2022, rubber septa impregnated with a ternary blend of p-anisaldehyde, eugenol, and farnesene (at 3–4.5 mg and a ratio of 3:1:1) captured the highest number of adults on sticky traps, outperforming traps bailed with individual components or a solvent control over two weeks. Significantly, the mass trapping strategy employing these lures achieved control efficacies ranging from 62.8% to 70.7% when compared to traps without attractant, which achieved control efficacies of only 14.2% to 35.4% across three test sites in 2023. These results indicate that the combination of p-anisaldehyde, eugenol, and farnesene exhibits an additive or synergistic effect on D. minowai. In conclusion, our findings establish a theoretical framework and provide practical technological support for integrating attractant-based strategies into comprehensive thrips management strategies. Full article
(This article belongs to the Special Issue Sustainable Strategies for Tea Crops Protection)
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