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ArticleID :JARA/2025/11/01/01

A simple and highly efficient protocol for 13C-labeling of plant cell wall for structural and quantitative analyses via solid-state nuclear magnetic resonance

Issue No. 01 (Jan-Mar)

Authors: Tasleem Javaid, Akshayaa Venkataraghavan, Matrika Bhattarai, Debkumar Debnath, Wancheng Zhao, Tuo Wang and Ahmed Faik

Background: Plant cell walls are made of a complex network of interacting polymers that play a critical role in plant development and responses to environmental changes. Thus, improving plant biomass and fitness requires the elucidation of the structural organization of plant cell walls in their native environment. The 13C-based multi-dimensional solid-state nuclear magnetic resonance (ssNMR) has been instrumental in revealing the structural information of plant cell walls through 2D and 3D correlation spectral analyses. However, the requirement of enriching plants with 13C limits the applicability of this method. To our knowledge, there is only a very limited set of methods currently available that achieve high levels of 13C-labeling of plant materials using 13CO2, and most of them require large amounts of 13CO2 in larger growth chambers.

Results: In this study, a simplified protocol for 13C-labeling of plant materials is introduced that allows ca 60% labeling of the cell walls, as quantified by comparison with commercially labeled samples. This level of 13C-enrichment is sufficient for all conventional 2D and 3D correlation ssNMR experiments for detailed analysis of plant cell wall structure. The protocol is based on a convenient and easy setup to supply both 13C-labeled glucose and 13CO2 using a vacuumdesiccator. The protocol does not require large amounts of 13CO2.

Conclusion: This study shows that our 13C-labeling of plant materials can make the accessibility to ssNMR technique easy and affordable. The derived high-resolution 2D and 3D correlation spectra are used to extract structural information of plant cell walls. This helps to better understand the influence of polysaccharide-polysaccharide interaction on plant performance and allows for a more precise parametrization of plant cell wall models.

How To Cite this Article

© The Author(s) 2024. Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by-nc-nd/4.0/.

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ArticleID :JARA/2025/11/01/02

Development of a robust and efficient virus-induced gene silencing system for reverse genetics in recalcitrant Camellia drupifera capsules

Issue No. 01 (Jan-Mar)

Authors: Hongjian Shen, Huajie Chen, Weimeng Li, Shan He, Boyong Liao, Wanyu Xiong, et.al.,

Background: Virus-induced gene silencing (VIGS) is a rapid and powerful method for gene functional analysis in plants that pose challenges in stable transformation. Numerous VIGS systems based on Agrobacterium infiltration has been widely developed for tender tissues of various plant species, yet none is available for recalcitrant perennial woody plants with firmly lignified capsules, such as tea oil camellia. Therefore, there is an urgent need for an efficient, robust, and cost-effective VIGS system for recalcitrant tissues.

Results: Herein, we initiated the Tobacco rattle virus (TRV)-elicited VIGS in Camellia drupifera capsules with an orthogonal analysis including three factors: silencing target, virus inoculation approach, and capsule developmental stage. To facilitate observation and statistical analysis, two genes predominantly involved in pericarp pigmentation were selected for silencing efficiency: CdCRY1 (coding for a key photoreceptor affecting light-responsive perceivable anthocyanin accumulation in exocarps) and CdLAC15 (coding for an oxidase catalyzing the oxidative polymerization of proanthocyanidins in mesocarps, resulting in unperceivable red-hued mesocarps). The infiltration efficiency achieved for each gene was ~ 93.94% by pericarp cutting immersion. The optimal VIGS effect for each gene was observed at early (~ 69.80% for CdCRY1) and mid stages (~ 90.91% for CdLAC15) of capsule development.

Conclusions: Using our optimized VIGS system, CdCRY1 and CdLAC15 expression was successfully knocked down in Camellia drupifera pericarps, leading to fading phenotypes in exocarps and mesocarps, respectively. The established VIGS system may facilitate functional genomic studies in tea oil camellia and other recalcitrant fruits of woody plants.

How To Cite this Article

© The Author(s) 2025. Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http:// creat iveco mmons. org/ licen ses/ by- nc- nd/4. 0/.

Research Article

ArticleID :JARA/2025/11/01/03

Effects of hormone concentrations on anther cultures and the acquisition of regenerated plants of five awnless triticale genotypes

Issue No. 01 (Jan-Mar)

Authors: Ma, Fangyuan Zhao, Yinxia Zhang, Xinhui Tian and Wenhua Du

Background: The rapid production of doubled haploids by anther culture technology is an important breeding method for awnless triticale. The aim of this study was to explore the effects of triticale genotype and the types and ratios of exogenous hormones in the medium on the efficiency of triticale anther culture.

Results: Anthers of five triticale genotypes were cultured on four different callus induction media and the calli were induced to differentiate into green plants by culture on three different differentiation media. The triticale genotype T8004 showed the best performance in anther culture, with a callus induction rate of 28.64%, a green plantlet differentiation frequency of 33.33%, and a green plantlet production rate of 2.78%. The highest callus induction rates were obtained by culturing anthers on C3 medium (the main components were potassium nitrate, glutamine, inositol, etc.), and the highest green plantlet differentiation frequency was obtained by culturing calli on D2 differentiation medium (the main components were potassium nitrate, ammonium nitrate, calcium chloride dihydrate, etc.). Flow cytometry analyses showed that 15 of the 20 DH0 generation plants that grew normally in the field were doubled haploids. The average chromosome doubling success rate was 55.6%. Analyses of agronomic traits showed that the 11 DH1 doubled haploid plants reached the standard for awnless triticale, so they are candidate materials for breeding new awnless triticale varieties.

Conclusion: The anther culture technology of triticale was optimized in this paper, which made it possible to rapidly breed homozygous varieties of awnless triticale.

How To Cite this Article

© The Author(s) 2024. Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit h t t p : / / c r e a t i v e c o m m o n s . o r g / l i c e n s e s / b y - n c - n d / 4. 0 / .

Research Article

ArticleID :JARA/2025/11/01/04

Beneficial mutualistic fungus Suillus luteus provided excellent buffering insurance in Scots pine defense responses under pathogen challenge at transcriptome level

Issue No. 01 (Jan-Mar)

Authors: Zilan Wen, Minna J. Manninen and Fred O. Asiegbu

Background: Mutualistic mycorrhiza fungi that live in symbiosis with plants facilitates nutrient and water acquisition, improving tree growth and performance. In this study, we evaluated the potential of mutualistic fungal inoculation to improve the growth and disease resistance of Scots pine (Pinus sylvestris L.) against the forest pathogen Heteroba sidion annosum.

Results: In co-inoculation experiment, Scots pine seedlings were pre-inoculated with mutualistic beneficial fungus (Suillus luteus) prior to H. annosum infection. The result revealed that inoculation with beneficial fungus promoted plant root growth. Transcriptome analyses revealed that co-inoculated plants and plants inoculated with beneficial fungus shared some similarities in defense gene responses. However, pathogen infection alone had unique sets of genes encoding pathogenesis-related (PR) proteins, phenylpropanoid pathway/lignin biosynthesis, flavonoid biosynthesis, chalcone/stilbene biosynthesis, ethylene signaling pathway, JA signaling pathway, cell remodeling and growth, transporters, and fungal recognition. On the other hand, beneficial fungus inoculation repressed the expression of PR proteins, and other defense-related genes such as laccases, chalcone/stilbene synthases, terpene synthases, cytochrome P450s. The co-inoculated plants did not equally enhance the induction of PR genes, chalcone/ stilbene biosynthesis, however genes related to cell wall growth, water and nutrient transporters, phenylpropanoid/ lignin biosynthesis/flavonoid biosynthesis, and hormone signaling were induced.

Conclusion: S. luteus promoted mutualistic interaction by suppressing plant defense responses. Pre-inoculation of Scots pine seedlings with beneficial fungus S. luteus prior to pathogen challenge promoted primary root growth, as well as had a balancing buffering role in plant defense responses and cell growth at transcriptome level.

How To Cite this Article

© The Author(s) 2025. Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.

Research Article

ArticleID :JARA/2025/11/01/05

Apnet: Lightweight network for apricot tree disease and pest detection in real-world complex backgrounds

Issue No. 01 (Jan-Mar)

Authors: Minglang Li, Zhiyong Tao, Wentao Yan, Sen Lin, Kaihao Feng, Zeyi Zhang and Yurong Jing

Apricot trees, serving as critical agricultural resources, hold a significant role within the agricultural domain. Conven tional methods for detecting pests and diseases in these trees are notably labor-intensive. Many conditions affect ing apricot trees manifest distinct visual symptoms that are ideally suited for precise identification and classification via deep learning techniques. Despite this, the academic realm currently lacks extensive, realistic datasets and deep learning strategies specifically crafted for apricot trees. This study introduces ATZD01, a publicly accessible dataset encompassing 11 categories of apricot tree pests and diseases, meticulously compiled under genuine field condi tions. Furthermore, we introduce an innovative detection algorithm founded on convolutional neural networks, specifically devised for the management of apricot tree pests and diseases. To enhance the accuracy of detection, we have developed a novel object detection framework, APNet, alongside a dedicated module, the Adaptive Thresh olding Algorithm (ATA), tailored for the detection of apricot tree afflictions. Experimental evaluations reveal that our proposed algorithm attains an accuracy rate of 87.1% on ATZD01, surpassing the performance of all other leading algorithms tested, thereby affirming the effectiveness of our dataset and model. The code and dataset will be made available at https:// github. com/ meanl ang/ ATZD01.

How To Cite this Article

© The Author(s) 2025. Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativeco mmons.org/ licenses/ by- nc- nd/4.0/.

Research Article

ArticleID :JARA/2025/11/02/01

Determining water status of walnut orchards using the crop water stress index and canopy temperature measurements

Issue No. 02 (Apr-June)

Authors: Lian Mao, Sen Lu, Linqi Liu, Zhipeng Li, Baoqing Wang, Dong Pei and Yongchao Bai

Background:  Accurately evaluating the water status of walnuts in different growth stages is fundamental to implementing deficit irrigation strategies and improving the yield of walnuts. The crop water stress index (CWSI) based on the canopy temperature is one of the most commonly used tools for current research on plant water monitoring. However, the suitability and effectiveness of using the CWSI as an indicator of the walnut water status under field conditions are still unclear. This paper focuses on walnut orchards in Northwest China using synchronous monitoring of the canopy temperature, meteorological parameters, and water physiological parameters of walnut trees under both full irrigation and deficit irrigation treatments. The aim is to test the effectiveness of the simplified crop water stress index (CWSIs) and the theoretical crop water stress index (CWSIt) in tracking the diurnal and daily variations of the water conditions in walnut orchards.

Results: The CWSIs can reflect the diurnal and daily changes in the water status of walnut orchards. It was found that the CWSIs at 12:00 local time had the best performance in tracking the daily changes in the water status. Compared to the daily averaged CWSI calculated using the measured transpiration (CWSITr_day), the correlation coefficient, index of agreement, and root mean squared error between the CWSIs and CWSITr_day were 0.82, 0.94, and 0.11, respectively. However, due to the calculation errors of the aerodynamic resistance in walnut trees, the CWSIt was unable to track the diurnal variations in the water status in walnut orchards and the degree of water stress was underestimated. In addition, the variations in minimum canopy resistance in the various growth stages of walnut orchards may also affect the accuracy of the CWSIt in terms of indicating the seasonal changes in the water status.

Conclusions: The CWSIs provides a non-destructive, quickly and effective method for monitoring the water status of walnuts. However, the results of this study suggest that the effects of aerodynamic resistance parameterization and variations in minimum canopy resistance in the various growth stages of walnut orchards in the CWSIt calculation should be noted.

How To Cite this Article

© The Author(s) 2025. Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creatvecommons.org/licenses/by-nc-nd/4.0/.

Research Article

ArticleID :JARA/2025/11/02/02

A novel approach integrating multispectral imaging and machine learning to identify seed maturity and vigor in smooth bromegrass

Issue No. 02 (Apr-June)

Authors: Chengming Ou, Zhicheng Jia, Shiqiang Zhao, Shoujiang Sun, Ming Sun, Jingyu Liu, Manli Li, Shangang Jia and Peisheng Mao

Smooth bromegrass (Bromus inermis) was adopted as experiment materials for identifying the seed maturity using a combination of multispectral imaging and machine learning. The trials were conducted to investigate the effects of three nitrogen application levels (0, 100 and 200 kg N ha− 1, defined as CK, N1 and N2 respectively) and two spikelet grain positions: superior grain (SG) at the basal position and inferior grain (IG) at the upper position, on smooth bromegrass seeds. The germination characteristics of the seeds revealed that the variations in nitrogen application and grain positions significantly influenced seeds vigor. The seed vigor of increased gradually with their maturity, reaching a high level at 30 and 36 days after anthesis. A stacking ensemble learning approach was employed to identify the seed maturity based on multispectral imaging and autofluorescence imaging. The results demonstrated that the Ensemble model outperformed Support Vector Machine, Bayesian, XGBoost and Random Forest across all evaluated metrics in different scenarios. The model accuracy in CK, N1 and N2 were 89%, 87% and 93%, respectively. Furthermore, the SHapley Additive exPlanations method was selected to interpret the Ensemble model, identifying important features such as 405, 430, 540, 630, 645, 690, 850, 880 and 970 nm. These features exhibited a significant correlation with fresh weight, shoot length and vigor index. These findings showed the high accuracy and generalizability of the Ensemble model for identifying the maturity and quality of smooth bromegrass seeds. Therefore, a new strategy would be offered for evaluating seed maturity and vigor level.

How To Cite this Article

© The Author(s) 2025. Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by-nc-nd/4.0/.

Research Article

ArticleID :JARA/2025/11/02/03

A compact deep learning approach integrating depthwise convolutions and spatial attention for plant disease classification

Issue No. 02 (Apr-June)

Authors: Amreen Batool, Jisoo Kim and Yung‑Cheol Byun

Plant leaf diseases significantly threaten agricultural productivity and global food security, emphasizing the importance of early and accurate detection and effective crop health management. Current deep learning models, often used for plant disease classification, have limitations in capturing intricate features such as texture, shape, and color of plant leaves. Furthermore, many of these models are computationally expensive and less suitable for deployment in resource‑constrained environments such as farms and rural areas. We propose a novel Lightweight Deep Learning model, Depthwise Separable Convolution with Spatial Attention (LWDSC‑SA), designed to address limitations and enhance feature extraction while maintaining computational efficiency. By integrating spatial attention and depthwise separable convolution, the LWDSC‑SA model improves the ability to detect and classify plant diseases. In our comprehensive evaluation using the PlantVillage dataset, which consists of 38 classes and 55,000 images from 14 plant species, the LWDSC‑SA model achieved 98.7% accuracy. It presents a substantial improvement over MobileNet by 5.25%, MobileNetV2 by 4.50%, AlexNet by 7.40%, and VGGNet16 by 5.95%. Furthermore, to validate its robustness and generalizability, we employed K‑fold cross‑validation K=5, which demonstrated consistently high performance, with an average accuracy of 98.58%, precision of 98.30%, recall of 98.90%, and F1 score of 98.58%. These results highlight the superior performance of the proposed model, demonstrating its ability to outperform state of‑the‑art models in terms of accuracy while remaining lightweight and efficient. This research offers a promising solution for real‑world agricultural applications, enabling effective plant disease detection in resource‑limited settings and contributing to more sustainable agricultural practices.

How To Cite this Article

© The Author(s) 2025. Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.

Others

ArticleID :JARA/2025/11/02/04

Dual discriminator GAN-based synthetic crop disease image generation for precise crop disease identification

Issue No. 02 (Apr-June)

Authors: Chao Wang, Yuting Xia, Lunlong Xia, Qingyong Wang and Lichuan Gu

Deep learning-based computer vision technology significantly improves the accuracy and efficiency of crop disease detection. However, the scarcity of crop disease images leads to insufficient training data, limiting the accuracy of dis ease recognition and the generalization ability of deep learning models. Therefore, increasing the number and diver sity of high-quality disease images is crucial for enhancing disease monitoring performance. We design a frequency domain and wavelet image augmentation network with a dual discriminator structure (FHWD). The first discriminator distinguishes between real and generated images, while the second high-frequency discriminator is specifically used to distinguish between the high-frequency components of both. High-frequency details play a crucial role in the sharpness, texture, and fine-grained structures of an image, which are essential for realistic image generation. During training, we combine the proposed wavelet loss and Fast Fourier Transform loss functions. These loss functions guide the model to focus on image details through multi-band constraints and frequency domain transformation, improving the authenticity of lesions and textures, thereby enhancing the visual quality of the generated images. We compare the generation performance of different models on ten crop diseases from the PlantVillage dataset. The experimental results show that the images generated by FHWD contain more realistic leaf disease lesions, with higher image quality that better aligns with human visual perception. Additionally, in classification tasks involving nine types of tomato leaf diseases from the PlantVillage dataset, FHWD-enhanced data improve classification accuracy by an average of 7.25% for VGG16, GoogleNet, and ResNet18 models.Our results show that FHWD is an effective image augmentation tool that effectively addresses the scarcity of crop disease images and provides more diverse and enriched training data for disease recognition models.

How To Cite this Article

© The Author(s) 2025. Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http:// creativecommons.org/licenses/by-nc-nd/4.0/.

Research Article

ArticleID :JARA/2025/11/03/01

A simple and low-cost method for fluoride analysis of plant materials using alkali extraction and ion-selective electrode

Issue No. 03 (July-Sept)

Authors: Chenyu Zhang, Mark G.M. Aarts and Antony van der Ent

Backgrounds: Existing methods for fluoride (F-) determination in plant material require expensive equipment and specialized reagents. This study aimed to develop a simple and cost-effective method for fluoride analysis in plant samples.

Results:  Using an orthogonal assay design with certified reference material, this study optimized a sodium hydroxide extraction method (5 mol·L-1) with heating at 120 °C for 0.5 h, followed by the addition of potassium acetate, ionic strength adjustment, and measurement via an ion-selective electrode. The method achieved a limit of detection (LOD) and limit of quantification (LOQ) of 1.41 and 4.71 mg·kg⁻¹, respectively. Recovery rates ranged from 84.74 to 89.34% in Arabidopsis thaliana (intraday relative standard deviation [RSD] ≤ 2.31%, inter-day RSD ≤ 4.17%) and from 83.53 to 91.55% in Camellia sinensis (intraday RSD ≤ 3.11%, inter-day RSD ≤ 4.98%). In A. thaliana cultivated in NaF-dosed (500 µM) nutrient solution, the fluoride concentration in the shoot was 16.00 mg·kg-1; In C. sinensis grown under 250 µM NaF treatment, the shoot fluoride concentration was 292.71 mg·kg-1. Moreover, the fluoride concentration in Tea products purchased from local supermarkets ranged from 16.28 to 61.78 mg kg-1.

Conclusion: This study presents a simple, reliable, and cost-effective method for fluoride analysis in plant materials, which can be further validated through inter-laboratory testing to establish a standardized approach

How To Cite this Article

Zhang, C., Aarts, M.G. & van der Ent, A. A simple and low-cost method for fluoride analysis of plant materials using alkali extraction and ion-selective electrode. Plant Methods 21, 98 (2025). https://doi.org/10.1186/s13007-025-01412-6

Research Article

ArticleID :JARA/2025/11/03/02

pSIG plasmids, MoClo-compatible vectors for efficient production of chimeric double-stranded RNAs in Escherichia coli HT115 (DE3) strain

Issue No. 03 (July-Sept)

Authors: Ching‑Feng Wu, Li‑Pang Chang, Chan Lee, Ioannis Stergiopoulos and Li‑Hung Chen

Background: Spray‑induced gene silencing (SIGS) is a promising strategy for controlling plant diseases caused by pests, fungi, and viruses. The method involves spraying on plant surfaces double‑stranded RNAs (dsRNAs) that target pathogen genes and inhibit pathogen growth via activation of the RNA interference machinery. Despite its potential, significant challenges remain in the application of SIGS, including producing large quantities of dsRNAs for field applications. While industrial‑scale dsRNA production is feasible, most research laboratories still rely on costly and labor‑intensive in vitro transcription kits that are difficult to scale up for field trials. Therefore, there is a critical need for highly efficient and scalable methods for producing diverse dsRNAs in research laboratories.

Results: This study introduces pSIG plasmids, MoClo‑compatible vectors designed for efficient dsRNA production in the Escherichia coli RNase III‑deficient strain HT115 (DE3). The pSIG vectors enable the assembly of multiple DNA fragments in a single reaction using highly efficient Golden Gate cloning, thereby allowing the production of chimeric dsRNAs to simultaneously silence multiple genes in target pests and pathogens. To demonstrate the efficacy of this system, we generated 12 dsRNAs targeting essential genes in Botrytis cinerea. The results revealed that silencing the Bcerg1, Bcerg2, and Bcerg27 genes involved in the ergosterol biosynthesis pathway, significantly reduced fungal infection in plant leaves. Furthermore, we synthesized a chimeric dsRNA, Bcergi, that incorporates target fragments from Bcerg1, Bcerg2, and Bcerg27. Nevertheless, the Bcerg1 dsRNA alone achieved greater disease suppression than the chimeric Bcergi dsRNA.

Conclusions: Here, we developed a highly efficient and scalable method for producing chimeric dsRNAs in E. coli HT115 (DE3) in research laboratories using our homemade pSIG plasmid vectors. This approach addresses key challenges in SIGS research, including the need to produce large quantities of dsRNA and identify effective dsRNAs, thus enhancing the feasibility of SIGS as a sustainable strategy for controlling plant diseases and pests in crops.

How To Cite this Article

Wu, CF., Chang, LP., Lee, C. et al. pSIG plasmids, MoClo-compatible vectors for efficient production of chimeric double-stranded RNAs in Escherichia coli HT115 (DE3) strain. Plant Methods 21, 96 (2025). https://doi.org/10.1186/s13007-025-01413-5

Research Article

ArticleID :JARA/2025/11/03/03

Utilizing X-ray radiography for non-destructive assessment of paddy rice grain quality traits

Issue No. 03 (July-Sept)

Authors: Murugesan Tharanya, Debarati Chakraborty, Anand Pandravada, Raman Babu, Mahantesh Gangashetti, Swapna Paidi, Sunita Choudhary, Kaliamoorthy Sivasakthi, Krithika Anbazhagan, Bhavani Vaditandra.

Background: Agricultural systems are under extreme pressure to meet the global food demand, hence necessitating faster crop improvement. Rapid evaluation of the crops using novel imaging technologies coupled with robust image analysis could accelerate crops research and improvement. This proof-of-concept study investigated the feasibility of using X-ray imaging for non-destructive evaluation of rice grain traits. By analyzing 2D X-ray images of paddy grains, we aimed to approximate their key physical Traits (T) important for rice production and breeding: (1) T1 chaffiness, (2) T2 chalky rice kernel percentage (CRK%), and (3) T3 head rice recovery percentage (HRR%). In the future, the integration of X-ray imaging and data analysis into the rice research and breeding process could accelerate the improvement of global agricultural productivity.

Results: The study indicated, computer-vision based methods (X-ray image segmentation, features-based multi-linear models and thresholding) can predict the physical rice traits (chaffiness, CRK%, HRR%). We showed the feasibility to predict all three traits with reasonable accuracy (chaffiness: R2 = 0.9987, RMSE = 1.302; CRK%: R2 = 0.9397, RMSE = 8.91; HRR%: R2 = 0.7613, RMSE = 6.83) using X-ray radiography and image-based analytics via PCA based prediction models on individual grains.

Conclusions: Our study demonstrated the feasibility to predict multiple key physical grain traits important in rice research and breeding (such as chaffiness, CRK%, and HRR%) from single 2D X-ray images of whole paddy grains. Such a non-destructive rice grain trait inference is expected to improve the robustness of paddy rice evaluation, as well as to reduce time and possibly costs for rice grain trait analysis. Furthermore, the described approach can also be transferred and adapted to other grain crops.

How To Cite this Article

Tharanya, M., Chakraborty, D., Pandravada, A. et al. Utilizing X-ray radiography for non-destructive assessment of paddy rice grain quality traits. Plant Methods 21, 94 (2025). https://doi.org/10.1186/s13007-025-01405-5

Research Article

ArticleID :JARA/2025/11/03/04

Development of a multi-targeted real-time PCR assay for the detection of the grapevine pathogen Xylophilus ampelinus

Issue No. 03 (July-Sept)

Authors: Aleksander Benčič, Alexandra Bogožalec Košir, Janja Matičič, Manca Pirc, Neža Turnšek and Tanja Dreo

Background:  Xylophilus ampelinus is a plant pathogenic bacterium that causes bacterial blight in grapevines, which can lead to severe yield losses and economic damage. Owing to its fastidious growth on culture media, detection is primarily based on molecular methods. However, existing tests have produced inconsistent results, particularly when used to detect latent infections and non-validated matrices. There is a risk of false-positive results, with economic consequences such as restrictions on international trade. To enhance the diagnostics of X. ampelinus, a genome-informed approach was utilised to identify new potential targets for specific detection. On the basis of these sequences, multiple real-time PCR assays were designed, and their specificity and sensitivity were assessed, as well as their performance validated across three different grapevine tissues, including leaves, roots, and xylem. 

Results:  The newly designed real-time PCR assays were evaluated via high throughput testing for specificity and sensitivity and compared with a reference assay. The most promising assays were selected and validated in different grapevine tissues and included in a test performance study to validate their reproducibility and robustness. Three new assays (Xamp_BA_2, TXmp22.4, and Xamp_BA_7) demonstrated high specificity and sensitivity for X. ampelinus detection. The Xamp_BA_2 assay exhibited the best overall performance, offering high diagnostic sensitivity and robustness across diverse plant matrices. Importantly, the assays exhibited no cross-reactivity with non-target bacterial species and maintained high detection accuracy across diverse grapevine tissue types. 

Conclusions: The newly developed real-time PCR assays provide an enhanced diagnostic framework for the detection of X. ampelinus in various plant matrices, significantly improving the applicability of molecular testing. The Xamp_BA_2 assay demonstrates superior performance and is recommended for routine diagnostics, with other validated assays being employed for confirmation of identification. The development of these new assays represents a significant expansion of our toolkit for the precise detection of X. ampelinus in grapevines, with the potential to contribute to the mitigation of grapevine bacterial blight, the prevention of yield losses, and the protection of international trade in grapevine material. Further implementation of these assays will support regulatory and phytosanitary efforts to mitigate the spread of X. ampelinus.

How To Cite this Article

Benčič, A., Bogožalec Košir, A., Matičič, J. et al. Development of a multi-targeted real-time PCR assay for the detection of the grapevine pathogen Xylophilus ampelinus. Plant Methods 21, 99 (2025). https://doi.org/10.1186/s13007-025-01422-4

Research Article

ArticleID :JARA/2025/11/03/05

Stomata morphology measurement with interactive machine learning: accuracy, speed, and biological relevance?

Issue No. 03 (July-Sept)

Authors: Tomke S. Wacker, Abraham G. Smith, Signe M. Jensen, Theresa Pflüger, Viktor G. Hertz, Eva Rosenqvist, Fulai Liu and Dorte B. Dresbøll

Stomatal morphology plays a critical role in regulating plant gas exchange influencing water use efficiency and ecological adaptability. While traditional methods for analyzing stomatal traits rely on labor-intensive manual measurements, machine learning (ML) tools offer a promising alternative. In this study, we evaluate the suitability of a U-Net-based interactive ML software with corrective annotation for stomatal morphology phenotyping. The approach enables non-ML experts to efficiently segment stomatal structures across diverse datasets, including images from different plant species, magnifications, and imprint methods. We trained a single model based on images from five datasets and tested its performance on unseen data, achieving high accuracy for stomatal density (R2 = 0.98) and size (R2 = 0.90). Thresholding approaches applied to the U-Net segmentations further improved accuracy, particularly for density measurements. Despite significant variability between datasets, our findings demonstrate the feasibility of training a single segmentation model to analyze diverse stomatal data sets. Validation approaches showed that a semi-automatic approach involving correcting segmentations was five times faster than manual annotation while maintaining comparable accuracy. Our results also illustrate that ML metrics, such as the F1 score, correlate with accuracy in the statistical analysis of trait measurements with improvements diminishing after 2:30 h model training. The final model achieved high precision, allowing the detection of highly significant biological differences in stomatal morphology within plant, between genotypes and across growing environments. This study highlights interactive ML with corrective annotation as a robust and accessible tool for accelerating phenotyping in plant sciences, reducing technical barriers and promoting high-throughput analysis.

How To Cite this Article

Wacker, T.S., Smith, A.G., Jensen, S.M. et al. Stomata morphology measurement with interactive machine learning: accuracy, speed, and biological relevance?. Plant Methods 21, 95 (2025). https://doi.org/10.1186/s13007-025-01416-2

Research Article

ArticleID :JARA/2025/11/04/01

A lightweight convolutional neural network for tea leaf disease and pest recognition

Issue No. 04 (Oct-Dec)

Authors: Xiaojie Wen, Qi Liu, Xuanyuan Tang, Fusheng Yu and Jing Chen

The tea industry plays a vital role in China’s green economy. Tea trees (Melaleuca alternifolia) are susceptible to numerous diseases and pest threats, making timely pathogen detection and precise pest identification critical requirements for agricultural productivity. Current diagnostic limitations primarily arise from data scarcity and insufficient discriminative feature representation in existing datasets. This study presents a new tea disease and pest dataset (TDPD, 23-class taxonomy). Five lightweight convolutional neural networks (LCNNs) were systematically evaluated through two optimizers, three learning rate configurations and six distinct scheduling strategies. Additionally, an enhanced MnasNet variant was developed through the integration of SimAM attention mechanisms, which improved feature discriminability and increased the accuracy of tea leaf disease and pest classification. Model validation employs both our proprietary TDPD dataset and an open-access dataset, with performance evaluation metrics including average accuracy, F1 score, recall, and parameter size. The experimental results demonstrated the superior classification performance of the model, which achieved accuracies of 98.03% based on TDPD and 84.58% based on the public dataset. This research outlines an effective paradigm for automated tea disease and pest detection, with direct applications in precision agriculture through integration with UAV-mounted imaging systems and mobile diagnostic platforms. This study provides practical implementation pathways for intelligent tea plantation management.

How To Cite this Article

Wen, X., Liu, Q., Tang, X. et al. A lightweight convolutional neural network for tea leaf disease and pest recognition. Plant Methods 21, 129 (2025). https://doi.org/10.1186/s13007-025-01452-y

Research Article

ArticleID :JARA/2025/11/04/02

Correlative microscopy for in-depth analysis of calcium oxalate crystals in plant tissues

Issue No. 04 (Oct-Dec)

Authors: Martin Niedermeier, Sebastian J. Antreich and Notburga Gierlinger

Background: Calcium oxalate (CaOx) crystals are commonly found in many plant species. These crystals vary in distribution and morphology and to elucidate their role in plants multiple methods have been applied. Raman imaging and polarized light microscopy (PLM) easily visualize the crystals within plant tissues, but both methods are limited in spatial resolution by the diffraction of light. To unravel the distinctive shape and morphology of CaOx crystals down to the nanoscale and how they are embedded within cells, high resolution scanning electron microscopy is needed. To grasp the full potential of multiple methods in CaOx studies, a novel and easy-to-build correlative sampling approach is presented on different nut species (pecan (Carya illinoinensis), Turkish hazel (Corylus colurna) and black walnut (Juglans nigra)), including soft tissues (young developmental stages) as well as hard tissues (mature nutshells)

Result: Young seed coat tissues as well as mature nutshells included distinct morphological CaOx features, like druses and prismatic crystals. By Raman imaging the chemical composition of all investigated crystals was verified as calcium oxalate monohydrate (COM) and Raman band intensity changed according to crystal plane orientation with respect to incident laser polarisation. Calcium oxalate dihydrate (COD) was only found in the young C. illinoinensis seed coat and was restricted to a few pixels adjacent to cell walls. These thin cell walls were identified as pectin-rich, while in the mature nutshells the crystals were surrounded by thicker and highly lignified cell walls. The Raman and light microscopy results were correlated with SEM images, which gave additional information on crystal surface structure and/or internal porosity on the nanoscale.

Conclusion: The presented correlative approach preserved the structural integrity of crystals and cellular structures during cutting and transferring between microscopes. Analysing exactly the same sample (position) by Raman, polarized light microscopy and SEM opens the view on the distribution within tissues and cells as well as the molecular structure of the crystals and adjacent cell structures. Such a comprehensive in-situ characterization paves the way for a better understanding of mineralization processes of different minerals in all kinds of biological tissues.

How To Cite this Article

Niedermeier, M., Antreich, S.J. & Gierlinger, N. Correlative microscopy for in-depth analysis of calcium oxalate crystals in plant tissues. Plant Methods 21, 136 (2025). https://doi.org/10.1186/s13007-025-01463-9

Research Article

ArticleID :JARA/2025/11/04/03

Cryopreservation of Rubus viruses in raspberry shoot tips via droplet-vitrification: assessment of viral preservation, localization, and post-thaw transmission capacity

Issue No. 04 (Oct-Dec)

Authors: Xiao-Yan Ma, Dag-Ragnar Blystad, Qiao-Chun Wang and Zhibo Hamborg

We report the successful cryopreservation of three economically important Rubus viruses: raspberry bushy dwarf virus (RBDV), black raspberry necrosis virus (BRNV), and Rubus yellow net virus (RYNV), using shoot tip cryopreservation in four raspberry cultivars. Virus-infected shoot tips (approximately 1.0 mm in length) containing 3–4 leaf primordia (LPs) were cryopreserved using the droplet-vitrification technique. In the cultivars ‘Zlatá Královna (ZK)’ and ‘Tulameen (TUM)’, over 90% of shoot tips survived, and more than 90% regenerated into whole shoots. All three viruses were successfully preserved in the cryopreserved tissues, with recovery rates varying depending on virus type and cultivar: RBDV was recovered at rates of 86% in ‘ZK’ and 87% in ‘TUM’; BRNV at 66% in ‘ZK’ and 45% in ‘TUM’; and RYNV at 96%, 94%, and 86% in ‘Fairview’, ‘Stiora’, and ‘ZK’, respectively. To investigate viral localization in shoot tips, in situ hybridization was used. RBDV and RYNV infected a broad range of meristematic tissues, including the apical dome and LPs, whereas BRNV showed a more limited distribution. Virus distribution varied not only among virus species but also across raspberry cultivars, suggesting genotype-specific patterns of virus localization. Post-cryopreservation viral activity was verified using micrografting and aphid transmission assays. RBDV, BRNV, and RYNV were all successfully transmitted to healthy plants via micrografting, indicating the preservation of viral infectivity. Furthermore, BRNV was effectively transmitted by large raspberry aphids from cryopreserved materials, confirming vector-mediated transmission capacity post-thaw. Overall, this study demonstrates that shoot tip cryopreservation via droplet-vitrification is a reliable and effective strategy for preservation of biologically active Rubus viruses. This approach offers a valuable biotechnological tool for virus maintenance in support of diagnostic, breeding, and virology research.

How To Cite this Article

Ma, XY., Blystad, D., Wang, QC. et al. Cryopreservation of Rubus viruses in raspberry shoot tips via droplet-vitrification: assessment of viral preservation, localization, and post-thaw transmission capacity. Plant Methods 21, 137 (2025). https://doi.org/10.1186/s13007-025-01454-w

Research Article

ArticleID :JARA/2025/11/04/04

LDSL framework: a lightweight dual-stream learning framework for wheat disease detection

Issue No. 04 (Oct-Dec)

Authors: Lei Feng, Mingliang Li, Guanshi Ye, Qinghai Wu, Chunyu Ning and You Tang

Background: Wheat diseases significantly impair production efficiency and grain quality in the wheat industry. In recent research, deep learning techniques have been widely applied to plant disease detection. However, wheat disease images collected in field conditions often face complex backgrounds and diverse lesion shapes, making accurate disease classification difficult. In real-world applications, agricultural disease recognition systems must also deal with limited computational resources and edge device constraints, emphasizing the need for lightweight methods. 

Results: To solve these challenges, this paper introduces a lightweight dual-stream learning (LDSL) framework for wheat disease detection. The framework adopts a unique global–local dual-stream architecture that combines global semantic understanding with local discriminative analysis. The global learning stream extracts comprehensive semantic features and generates saliency maps to highlight key regions, while the local learning stream performs fine-grained inspection of these regions using a novel dynamic-static dual attention (DSDA) mechanism. Additionally, a Kullback–Leibler (KL) divergence perturbation strategy is implemented during training to boost the LDSL framework’s robustness in noisy and complex settings. Experimental results show that the proposed LDSL framework achieves an accuracy of 94.44%, a precision of 94.47%, a recall of 94.44%, and an F1-score of 94.45%, outperforming several mainstream classification models in wheat disease recognition, such as ConvNeXt-T (92.66% accuracy, 92.69% precision, 92.66% recall, and 92.63% F1). The proposed LDSL framework is lightweight, using only 4.41 M parameters and 1.71G FLOPs. On the NVIDIA Jetson Orin Nano, it requires just 15.99 MB of storage, 39.49 MB of peak memory, and achieves an inference latency of 234.76 ms/image, demonstrating good potential for real-world deployment.

Conclusions: This study provides a novel detection framework for wheat disease research, which significantly improves various classification metrics. With low parameter and computation costs, the framework demonstrates good potential for practical deployment

How To Cite this Article

Feng, L., Li, M., Ye, G. et al. LDSL framework: a lightweight dual-stream learning framework for wheat disease detection. Plant Methods 21, 135 (2025). https://doi.org/10.1186/s13007-025-01455-9

Research Article

ArticleID :JARA/2025/11/04/05

SCA-MobiPlant: smartphone-deployed multistage attention fusion model for accurate field detection of chili leaf curl complex

Issue No. 04 (Oct-Dec)

Authors: Samrat Paul, Venu Emmadi, Mehulee Sarkar, Shubhajyoti Das, Anirban Roy and Parimal Sinha

Background: Field-scale assessment of chili leaf curl complex presents a significant diagnostic challenge, as both chili leaf curl virus (ChiLCV) and mite infestations produce visually overlapping symptoms difficult to distinguish by untrained personnel. This diagnostic confusion frequently leads to inappropriate application of either insecticides or acaricides, resulting in economic losses and environmental concerns. To address this issue, we propose SCA-MobiPlant, an improved MobileNetV3-Small model integrated with a novel multistage Squeeze-and-Excitation Coordinate Attention (SCA) fusion mechanism, designed for accurate differentiation of these apparently similar symptoms and precise field assessment of the disease.

Results: The proposed model effectively focuses on subtle diagnostic features including leaf texture, petiole elongation, and irregular curling patterns to achieve reliable classification. The multistage SCA fusion module demonstrated superior performance, achieving 99.64% accuracy, 99.61% precision, 99.64% recall, and 99.62% F1-score through K = 5 cross-validation, outperforming other attention modules such as the Convolutional Block Attention Module (CBAM) and Coordinate Attention (CA). Gradient-Weighted Class Activation Mapping (Grad-CAM) provided visual interpretability of the model’s decision-making process. Comparative evaluation against state-of-the-art architectures, including EfficientNetB0, ResNet50, VGG19 and YOLO advanced series, confirmed the computational efficiency of the proposed model for mobile deployment.

Conclusions: The final system, termed SCA-MobiPlant, has been successfully implemented on smartphones, along with a Disease Incidence (DI) calculation module, enabling rapid and accurate field assessment of the disease. This facilitates appropriate intervention strategies while minimizing unnecessary pesticide use. The study highlights the potential of lightweight, attention-enhanced models for real-world plant disease diagnostics, particularly in resource-constrained agricultural settings.

How To Cite this Article

Paul, S., Emmadi, V., Sarkar, M. et al. SCA-MobiPlant: smartphone-deployed multistage attention fusion model for accurate field detection of chili leaf curl complex. Plant Methods 21, 138 (2025). https://doi.org/10.1186/s13007-025-01453-x