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学者姓名：钟凤林
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Abstract ：
Tomato (Solanum lycopersicum L.) is a major economic vegetable crop globally, yet it is prone to gray mold disease caused by Botrytis cinerea infection during cultivation. Caffeoyl shikimate esterase (CSE) is a crucial component of the lignin biosynthesis pathway, which significantly contributes to plant stress resistance. Therefore, investigating the expression patterns of SlCSE after Botrytis cinerea infection may offer a theoretical foundation for breeding resistant tomato varieties. In this study, 11 SlCSE family members were identified from the tomato genome using bioinformatics analyses. Public transcriptome databases and RT-qPCR experiments were used to analyze gene expression in tomato tissues, responses to Botrytis cinerea infection, and the temporal characteristics of the response to 2-ethylfuran treatment during infection. These experiments resulted in the identification of the key gene SlCSE06. Transgenic tomato lines that overexpressed SlCSE06 were constructed to examine their resistance levels to gray mold disease. Many SlCSE genes were upregulated when tomato fruit were infected with Botrytis cinerea during the ripening stage. Furthermore, 24 h after treatment with 2-ethylfuran, most SlCSE genes exhibited increased expression levels compared with the control group, but they exhibited significantly lower levels at other time points. Thus, 2-ethylfuran treatment may enhance the responsiveness of SlCSEs. Based on this research, SlCSE06 was identified as the key gene involved in the response to Botrytis cinerea infection. The SlCSE06-overexpressing (OE6) tomato plants exhibited a 197.94% increase in expression levels compared to the wild type (WT). Furthermore, the lignin content in OE6 was significantly higher than in WT, suggesting that the overexpression of SlCSE06 enhanced lignin formation in tomato plants. At 5 days post-inoculation with Botrytis cinerea, the lesion diameter in OE6 decreased by 31.88% relative to the WT, whereas the lignin content increased by 370.90%. Furthermore, the expression level of SlCSE06 was significantly upregulated, showing a 17.08-fold increase compared with the WT. These findings suggest that 2-ethylfuran enhances the activation of the critical tomato disease resistance gene SlCSE06 in response to gray mold stress, thereby promoting lignin deposition to mitigate further infection by Botrytis cinerea.
Keyword ：
2-ethylfuran 2-ethylfuran Botrytis cinerea Botrytis cinerea caffeoyl shikimate esterase caffeoyl shikimate esterase tomato tomato
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| GB/T 7714 | Ye, Huilan , Gao, Hongdou , Li, Jinnian et al. Mitigating Response of SlCSE06 Induced by 2-Ethylfuran to Botrytis cinerea Infection [J]. | PLANTS-BASEL , 2025 , 14 (4) . |
| MLA | Ye, Huilan et al. "Mitigating Response of SlCSE06 Induced by 2-Ethylfuran to Botrytis cinerea Infection" . | PLANTS-BASEL 14 . 4 (2025) . |
| APA | Ye, Huilan , Gao, Hongdou , Li, Jinnian , Lu, Linye , Zheng, Shilan , Wu, Chengxin et al. Mitigating Response of SlCSE06 Induced by 2-Ethylfuran to Botrytis cinerea Infection . | PLANTS-BASEL , 2025 , 14 (4) . |
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Effective weed management is essential for protecting crop yields in cotton production, yet conventional deep learning approaches often falter in detecting small or occluded weeds and can be restricted by large parameter counts. To tackle these challenges, we propose YOLO-ACE, an advanced extension of YOLOv5s, which was selected for its optimal balance of accuracy and speed, making it well suited for agricultural applications. YOLO-ACE integrates a Context Augmentation Module (CAM) and Selective Kernel Attention (SKAttention) to capture multi-scale features and dynamically adjust the receptive field, while a decoupled detection head separates classification from bounding box regression, enhancing overall efficiency. Experiments on the CottonWeedDet12 (CWD12) dataset show that YOLO-ACE achieves notable mAP@0.5 and mAP@0.5:0.95 scores-95.3% and 89.5%, respectively-surpassing previous benchmarks. Additionally, we tested the model's transferability and generalization across different crops and environments using the CropWeed dataset, where it achieved a competitive mAP@0.5 of 84.3%, further showcasing its robust ability to adapt to diverse conditions. These results confirm that YOLO-ACE combines precise detection with parameter efficiency, meeting the exacting demands of modern cotton weed management.
Keyword ：
attention mechanism attention mechanism deep learning deep learning weed detection weed detection YOLOv5s YOLOv5s
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| GB/T 7714 | Zhou, Qi , Li, Huicheng , Cai, Zhiling et al. YOLO-ACE: Enhancing YOLO with Augmented Contextual Efficiency for Precision Cotton Weed Detection [J]. | SENSORS , 2025 , 25 (5) . |
| MLA | Zhou, Qi et al. "YOLO-ACE: Enhancing YOLO with Augmented Contextual Efficiency for Precision Cotton Weed Detection" . | SENSORS 25 . 5 (2025) . |
| APA | Zhou, Qi , Li, Huicheng , Cai, Zhiling , Zhong, Yiwen , Zhong, Fenglin , Lin, Xiaoyu et al. YOLO-ACE: Enhancing YOLO with Augmented Contextual Efficiency for Precision Cotton Weed Detection . | SENSORS , 2025 , 25 (5) . |
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Bitter melon, an important medicinal and edible economic crop, is often threatened by diseases such as downy mildew, powdery mildew, viral diseases, anthracnose, and blight during its growth. Efficient and accurate disease detection is of significant importance for achieving sustainable disease management in bitter melon cultivation. To address the issues of weak generalization ability and high computational demands in existing deep learning models in complex field environments, this study proposes an improved lightweight YOLOv8-LSW model. The model incorporates the inverted bottleneck structure of LeYOLO-small to design the backbone network, utilizing depthwise separable convolutions and cross-stage feature reuse modules to achieve lightweight design, reducing the number of parameters while enhancing multi-scale feature extraction capabilities. It also integrates the ShuffleAttention mechanism, strengthening the feature response in lesion areas through channel shuffling and spatial attention dual pathways. Finally, WIoUv3 replaces the original loss function, optimizing lesion boundary regression based on a dynamic focusing mechanism. The results show that YOLOv8-LSW achieves a precision of 95.3%, recall of 94.3%, mAP50 of 98.1%, mAP50-95h of 95.6%, and F1-score of 94.80%, which represent improvements of 2.2%, 2.7%, 1.2%, 2.2%, and 2.46%, respectively, compared to the original YOLOv8n. The effectiveness of the improvements was verified through heatmap analysis and ablation experiments. The number of parameters and GFLOPS were reduced by 20.58% and 20.29%, respectively, with an FPS of 341.58. Comparison tests with various mainstream deep learning models also demonstrated that YOLO-LSW performs well in the bitter melon disease detection task. This research provides a technical solution with both lightweight design and strong generalization ability for real-time detection of bitter melon diseases in complex environments, which holds significant application value in promoting precision disease control in smart agriculture.
Keyword ：
bitter melon bitter melon deep learning deep learning disease detection disease detection leaf disease leaf disease YOLO-LSW YOLO-LSW
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| GB/T 7714 | Liu, Shuang , Xu, Haobin , Deng, Ying et al. YOLOv8-LSW: A Lightweight Bitter Melon Leaf Disease Detection Model [J]. | AGRICULTURE-BASEL , 2025 , 15 (12) . |
| MLA | Liu, Shuang et al. "YOLOv8-LSW: A Lightweight Bitter Melon Leaf Disease Detection Model" . | AGRICULTURE-BASEL 15 . 12 (2025) . |
| APA | Liu, Shuang , Xu, Haobin , Deng, Ying , Cai, Yixin , Wu, Yongjie , Zhong, Xiaohao et al. YOLOv8-LSW: A Lightweight Bitter Melon Leaf Disease Detection Model . | AGRICULTURE-BASEL , 2025 , 15 (12) . |
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Key messageGA participates in FR light-induced internode elongation of cucumber by regulating the expression of genes/proteins related to aquaporins, expansins, cell wall biosynthesis, hormone metabolism, and signal transduction.AbstractThis study investigated the effects of the interaction between far-red (FR) light and gibberellin (GA) on the internode elongation of cucumber (Cucumis sativus L. 'Zhongnong No. 26') seedlings through combined physiological, biochemical, transcriptomic, and proteomic analyses. The results revealed that FR light and GA significantly promoted internode elongation in cucumber seedlings, whereas a GA biosynthesis inhibitor (PAC) inhibited the promoting effect of FR light. Hormone content determination revealed that FR light and GA decreased the contents of abscisic acid (ABA), indole-3-acetic acid (IAA), cytokinin (CTK), and jasmonate (JA) in cucumber seedling internodes. Bioinformatics analysis revealed that the expression patterns of the Co-DEGs and Co-DEPs were consistent in the FR (WL combined with FR light) and WLG (WL, in which plants were sprayed GA) groups, as well as in the FRP (FR, in which plants were sprayed PAC) and WL (full-spectrum LED white light) groups, suggesting that the mechanisms of FR and GA were similar in these Co-DEGs and Co-DEPs. Further analysis of these Co-DEGs and Co-DEPs revealed that they were involved mainly in cell wall biosynthesis and modification, lignin synthesis, hormone metabolism, and signal transduction pathways. In conclusion, this study revealed the important role of GA in FR light-induced internode elongation in cucumber seedlings, and this promoting effect was achieved mainly through the regulation of aquaporins, expansins, hormone metabolism, and signal transduction-related genes/proteins. This study provides new insights into the molecular mechanism of FR light-induced internode elongation in cucumber seedlings.
Keyword ：
Cucumber Cucumber Far-red light Far-red light Gibberellin Gibberellin Internode Internode Proteome Proteome Transcriptome Transcriptome
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| GB/T 7714 | Li, Shuhao , Xu, Yang , Tian, Jun et al. Multiomics analysis of gibberellin involved in far-red light-regulated internode elongation in cucumber seedlings [J]. | PLANT CELL REPORTS , 2025 , 44 (10) . |
| MLA | Li, Shuhao et al. "Multiomics analysis of gibberellin involved in far-red light-regulated internode elongation in cucumber seedlings" . | PLANT CELL REPORTS 44 . 10 (2025) . |
| APA | Li, Shuhao , Xu, Yang , Tian, Jun , Zheng, Hanbing , Sun, Ji , Wu, Haitao et al. Multiomics analysis of gibberellin involved in far-red light-regulated internode elongation in cucumber seedlings . | PLANT CELL REPORTS , 2025 , 44 (10) . |
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Nonanal, as a volatile organic compound (VOC) produced during the biosynthesis of polyunsaturated fatty acids (PUFAs), could inhibit damage to tomatoes caused by Botrytis cinerea. In this study, an 0.054 moL L-1 nonanal treatment decreased the incidence rate of gray mold on tomato and enhanced the defense capacity by altering the activities of the main defense-related enzymes, including NADPH-cytochrome c reductase, aminopyrine-n-demethylase, aniline-4-hydroxylase, and erythromycin N-demethylase. Tomato gray mold is associated with defense mechanisms and with cell degradation. PUFAs are closely related to the maintenance of cell integrity. The enzymes related to PUFAs showed weak activities until 36 h after the 0.054 moL L-1 nonanal treatment, afterwards, their activities increased. The 0.054 moL L-1 nonanal treatment decrease the energy wasted by decreasing NAD+, NADH, and ATP levels. The nonanal treatment also decreased the contents of key substances required for the synthesis of PUFAs (mainly aldehydes and alcohols, VAAs). The most important substances for disease resistance (hexanal, 2-hexanal, and citral) also significantly decreased after the 0.054 moL L-1 nonanal treatment. The transcriptome data revealed corresponding gene expression changes. Among the genes related to PUFAs, 14 were down-regulated and 5 were up-regulated after the 0.054 moL L-1 nonanal treatment compared with the control, and the genes in the plant–pathogen interaction pathway were up-regulated in the control group. This indicated that the nonanal treatment increased the postharvest tomato plant’s resistance capability and helped maintain cell integrity, which meant that less genes and substances were required to fight the B. cinerea infection. © 2025, The Authors. All rights reserved.
Keyword ：
Biochemistry Biochemistry Fruits Fruits Gene expression Gene expression Plants (botany) Plants (botany) Volatile organic compounds Volatile organic compounds
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| GB/T 7714 | Gao, Hongdou , Li, Jinnian , Wu, Chengxin et al. Elucidating Voc Dynamics and Molecular Mechanisms in Nonanal-Treated Postharvest Tomatoes Infected with Botrytis Cinerea [J]. | SSRN , 2025 . |
| MLA | Gao, Hongdou et al. "Elucidating Voc Dynamics and Molecular Mechanisms in Nonanal-Treated Postharvest Tomatoes Infected with Botrytis Cinerea" . | SSRN (2025) . |
| APA | Gao, Hongdou , Li, Jinnian , Wu, Chengxin , Ye, Huilan , Jia, Qi , Xu, Yang et al. Elucidating Voc Dynamics and Molecular Mechanisms in Nonanal-Treated Postharvest Tomatoes Infected with Botrytis Cinerea . | SSRN , 2025 . |
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Gray mold is one of the major diseases affecting tomato production. Its early symptoms are often inconspicuous, yet the disease spreads rapidly, leading to severe economic losses. Therefore, the development of efficient and non-destructive early detection technologies is of critical importance. At present, multispectral imaging-based detection methods are constrained by two major bottlenecks: limited sample size and single modality, which hinder precise recognition at the early stage of infection. To address these challenges, this study explores a detection approach integrating multispectral fluorescence and reflectance imaging, combined with machine learning algorithms, to enhance early recognition of tomato gray mold. Particular emphasis is placed on evaluating the effectiveness of multimodal information fusion in extracting early disease features, and on elucidating the quantitative relationships between disease progression and key physiological indicators such as chlorophyll content, water content, malondialdehyde levels, and antioxidant enzyme activities. Furthermore, an improved WGAN-GP (Wasserstein Generative Adversarial Network with Gradient Penalty) is employed to alleviate data scarcity under small-sample conditions. The results demonstrate that multimodal data fusion significantly improves model sensitivity to early-stage disease detection, while WGAN-GP-based data augmentation effectively enhances learning performance with limited samples. The Random Forest model achieved an early recognition precision of 97.21% on augmented datasets, and transfer learning models attained an overall precision of 97.56% in classifying different disease stages. This study provides an effective approach for the early prediction of tomato gray mold, with potential application value in optimizing disease management strategies and reducing environmental impact.
Keyword ：
disease detection disease detection gray mold gray mold machine learning machine learning multispectral fluorescence-reflectance technology multispectral fluorescence-reflectance technology tomato tomato
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| GB/T 7714 | Zhong, Xiaohao , Li, Huicheng , Cai, Yixin et al. Early Detection of Tomato Gray Mold Based on Multispectral Imaging and Machine Learning [J]. | HORTICULTURAE , 2025 , 11 (9) . |
| MLA | Zhong, Xiaohao et al. "Early Detection of Tomato Gray Mold Based on Multispectral Imaging and Machine Learning" . | HORTICULTURAE 11 . 9 (2025) . |
| APA | Zhong, Xiaohao , Li, Huicheng , Cai, Yixin , Deng, Ying , Xu, Haobin , Tian, Jun et al. Early Detection of Tomato Gray Mold Based on Multispectral Imaging and Machine Learning . | HORTICULTURAE , 2025 , 11 (9) . |
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The species and quantity of volatile organic compounds (VOCs) and the disease resistance are different in red and green ripening periods of tomato. In this study, treatments against Botrytis cinerea infections occurred during the red (RB.) and green (GB.) ripening periods were investigated. GB. decreased disease incidence rate and enhanced the defense capacity by altering the key defense-related enzymes [NADPH-cytochrome c reductase (NCR), aminopyrine-n-demethylase (AND), aniline-4-hydroxylase (AH) and erythromycin N-demethylase (ERND)] activities. Gray mold development in tomatoes is associated with defense mechanisms and cell degradation. PUFAs are involved in maintaining cell integrity. PUFA-related enzymes, such as lipoxygenase (LOX) and alcohol dehydrogenase (ADH), showed lower activity levels during GB.. Additionally, GB. enhanced the energy level by increasing NAD+, NADH, and ATP amounts. At the metabolic level, the VOCs composition was altered by the GB., leading to a reduction in key precursors of PUFA synthesis, including aldehydes/alcohols (VAAs). Among them, the concentrations of key disease resistance-associated substances-hexanal, 2-hexanal, and citral-significantly declined during the GB.. Transcriptomic analysis revealed that GB. modulated gene expression changes corresponded to metabolomic changes, with 37 PUFAs-related genes down-regulated and 79 up-regulated compared to RB.. These findings indicate that GB. enhances disease-resistance capability and maintains cellular integrity. Thereby, fewer genes and VOCs need to be mobilized to increase resistance in postharvest tomatoes.
Keyword ：
Botrytis cinerea Botrytis cinerea Ripening periods Ripening periods Tomato Tomato Volatile organic compounds Volatile organic compounds
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| GB/T 7714 | Gao, Hongdou , Wu, Chengxin , Jia, Zhen et al. Metabolomic and transcriptomic analyses of VOCs functions in different ripening periods tomato resistance responses to Botrytis cinerea infection [J]. | SCIENTIA HORTICULTURAE , 2025 , 350 . |
| MLA | Gao, Hongdou et al. "Metabolomic and transcriptomic analyses of VOCs functions in different ripening periods tomato resistance responses to Botrytis cinerea infection" . | SCIENTIA HORTICULTURAE 350 (2025) . |
| APA | Gao, Hongdou , Wu, Chengxin , Jia, Zhen , Li, Jinnian , Jia, Qi , Xu, Yang et al. Metabolomic and transcriptomic analyses of VOCs functions in different ripening periods tomato resistance responses to Botrytis cinerea infection . | SCIENTIA HORTICULTURAE , 2025 , 350 . |
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应对全球碳减排的紧迫挑战，可靠的碳达峰路径对中国碳减排的实施具有重要作用。然而，由于碳排放过程受众多因素影响，且相互作用复杂，传统情景分析方法难以有效识别最优减排路径。为此，在分析福建省的能源消费和碳排放数据的基础上，构建了麻雀搜索算法-支持向量回归模型(Sparrow Search Algorithm-Support Vector Regression, SSA-SVR)模型，该模型综合考虑了影响碳排放的14个关键因素，并基于SVR模型对福建省1999—2022年的碳排放量进行了预测和验证。随后，采用SSA算法优化了各因素的年度变化率组合，探索满足2030年碳达峰目标的多种可能路径。研究结果表明，模型具有较高的准确性和可靠性，探索出的所有路径均能在2030年实现碳达峰，但碳排放量存在显著差异。SSA-SVR模型能够为福建省工业部门实现碳达峰目标提供科学依据和策略建议。
Keyword ：
情景分析 情景分析 碳减排 碳减排 碳达峰路径 碳达峰路径 麻雀搜索算法 麻雀搜索算法
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| GB/T 7714 | 蔡湟 , 林晓宇 , 蔡志铃 et al. 基于麻雀搜索算法的福建省碳达峰路径优化研究 [J]. | 能源环境保护 , 2024 , 38 (03) : 173-183 . |
| MLA | 蔡湟 et al. "基于麻雀搜索算法的福建省碳达峰路径优化研究" . | 能源环境保护 38 . 03 (2024) : 173-183 . |
| APA | 蔡湟 , 林晓宇 , 蔡志铃 , 钟一文 , 钟凤林 . 基于麻雀搜索算法的福建省碳达峰路径优化研究 . | 能源环境保护 , 2024 , 38 (03) , 173-183 . |
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Nonheading Chinese cabbage is an important leafy vegetable, and quantitative identification and automated analysis of nonheading Chinese cabbage leaves are crucial for cultivating new varieties with higher quality, yield, and resistance. Traditional leaf phenotypic analysis relies mainly on phenotypic observation and the practical experience of breeders, leading to issues such as time consumption, labor intensity, and low precision, which result in low breeding efficiency. Considering these issues, a method for the extraction and analysis of phenotypes of nonheading Chinese cabbage leaves is proposed, targeting four qualitative traits and ten quantitative traits from 1500 samples, by integrating deep learning and OpenCV image processing technology. First, a leaf classification model is trained using YOLOv8 to infer the qualitative traits of the leaves, followed by the extraction and calculation of the quantitative traits of the leaves using OpenCV image processing technology. The results indicate that the model achieved an average accuracy of 95.25%, an average precision of 96.09%, an average recall rate of 96.31%, and an average F1 score of 0.9620 for the four qualitative traits. From the ten quantitative traits, the OpenCV-calculated values for the whole leaf length, leaf width, and total leaf area were compared with manually measured values, showing RMSEs of 0.19 cm, 0.1762 cm, and 0.2161 cm2, respectively. Bland-Altman analysis indicated that the error values were all within the 95% confidence intervals, and the average detection time per image was 269 ms. This method achieved good results in the extraction of phenotypic traits from nonheading Chinese cabbage leaves, significantly reducing the personpower and time costs associated with genetic resource analysis. This approach provides a new technique for the analysis of nonheading Chinese cabbage genetic resources that is high-throughput, precise, and automated.
Keyword ：
deep learning deep learning leaf phenotype leaf phenotype nonheading Chinese cabbage nonheading Chinese cabbage OpenCV OpenCV
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| GB/T 7714 | Xu, Haobin , Fu, Linxiao , Li, Jinnian et al. A Method for Analyzing the Phenotypes of Nonheading Chinese Cabbage Leaves Based on Deep Learning and OpenCV Phenotype Extraction [J]. | AGRONOMY-BASEL , 2024 , 14 (4) . |
| MLA | Xu, Haobin et al. "A Method for Analyzing the Phenotypes of Nonheading Chinese Cabbage Leaves Based on Deep Learning and OpenCV Phenotype Extraction" . | AGRONOMY-BASEL 14 . 4 (2024) . |
| APA | Xu, Haobin , Fu, Linxiao , Li, Jinnian , Lin, Xiaoyu , Chen, Lingxiao , Zhong, Fenglin et al. A Method for Analyzing the Phenotypes of Nonheading Chinese Cabbage Leaves Based on Deep Learning and OpenCV Phenotype Extraction . | AGRONOMY-BASEL , 2024 , 14 (4) . |
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Automatic harvesting robots are crucial for enhancing agricultural productivity, and precise fruit maturity detection is a fundamental and core technology for efficient and accurate harvesting. Strawberries are distributed irregularly, and their images contain a wealth of characteristic information. This characteristic information includes both simple and intuitive features, as well as deeper abstract meanings. These complex features pose significant challenges to robots in determining fruit ripeness. To increase the precision, accuracy, and efficiency of robotic fruit maturity detection methods, a strawberry maturity detection algorithm based on an improved CES-YOLOv8 network structure from YOLOv8 was developed in this study. Initially, to reflect the characteristics of actual planting environments, the study collected image data under various lighting conditions, degrees of occlusion, and angles during the data collection phase. Subsequently, parts of the C2f module in the YOLOv8 model's backbone were replaced with the ConvNeXt V2 module to enhance the capture of features in strawberries of varying ripeness, and the ECA attention mechanism was introduced to further improve feature representation capability. Finally, the angle compensation and distance compensation of the SIoU loss function were employed to enhance the IoU, enabling the rapid localization of the model's prediction boxes. The experimental results show that the improved CES-YOLOv8 model achieves an accuracy, recall rate, mAP50, and F1 score of 88.20%, 89.80%, 92.10%, and 88.99%, respectively, in complex environments, indicating improvements of 4.8%, 2.9%, 2.05%, and 3.88%, respectively, over those of the original YOLOv8 network. This algorithm provides technical support for automated harvesting robots to achieve efficient and precise automated harvesting. Additionally, the algorithm is adaptable and can be extended to other fruit crops.
Keyword ：
automatic harvesting robots automatic harvesting robots CES-YOLOv8 CES-YOLOv8 strawberry maturity strawberry maturity
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| GB/T 7714 | Chen, Yongkuai , Xu, Haobin , Chang, Pengyan et al. CES-YOLOv8: Strawberry Maturity Detection Based on the Improved YOLOv8 [J]. | AGRONOMY-BASEL , 2024 , 14 (7) . |
| MLA | Chen, Yongkuai et al. "CES-YOLOv8: Strawberry Maturity Detection Based on the Improved YOLOv8" . | AGRONOMY-BASEL 14 . 7 (2024) . |
| APA | Chen, Yongkuai , Xu, Haobin , Chang, Pengyan , Huang, Yuyan , Zhong, Fenglin , Jia, Qi et al. CES-YOLOv8: Strawberry Maturity Detection Based on the Improved YOLOv8 . | AGRONOMY-BASEL , 2024 , 14 (7) . |
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