Plant Methods最新文献

{"title":"A compact deep learning approach integrating depthwise convolutions and spatial attention for plant disease classification.","authors":"Amreen Batool, Jisoo Kim, Yung-Cheol Byun","doi":"10.1186/s13007-025-01325-4","DOIUrl":"10.1186/s13007-025-01325-4","url":null,"abstract":"<p><p>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.</p>","PeriodicalId":20100,"journal":{"name":"Plant Methods","volume":"21 1","pages":"48"},"PeriodicalIF":4.7,"publicationDate":"2025-04-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11966804/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143772975","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Determining water status of walnut orchards using the crop water stress index and canopy temperature measurements.","authors":"Lian Mao, Sen Lu, Linqi Liu, Zhipeng Li, Baoqing Wang, Dong Pei, Yongchao Bai","doi":"10.1186/s13007-025-01364-x","DOIUrl":"10.1186/s13007-025-01364-x","url":null,"abstract":"<p><strong>Background: </strong>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 (CWSI_s) and the theoretical crop water stress index (CWSI_t) in tracking the diurnal and daily variations of the water conditions in walnut orchards.</p><p><strong>Results: </strong>The CWSI_s can reflect the diurnal and daily changes in the water status of walnut orchards. It was found that the CWSI_s 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 (CWSI_{Tr_day}), the correlation coefficient, index of agreement, and root mean squared error between the CWSI_s and CWSI_{Tr_day} were 0.82, 0.94, and 0.11, respectively. However, due to the calculation errors of the aerodynamic resistance in walnut trees, the CWSI_t 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 CWSI_t in terms of indicating the seasonal changes in the water status.</p><p><strong>Conclusions: </strong>The CWSI_s 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 CWSI_t calculation should be noted.</p>","PeriodicalId":20100,"journal":{"name":"Plant Methods","volume":"21 1","pages":"47"},"PeriodicalIF":4.7,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11959715/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143764593","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Dual discriminator GAN-based synthetic crop disease image generation for precise crop disease identification.","authors":"Chao Wang, Yuting Xia, Lunlong Xia, Qingyong Wang, Lichuan Gu","doi":"10.1186/s13007-025-01361-0","DOIUrl":"10.1186/s13007-025-01361-0","url":null,"abstract":"<p><p>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 disease recognition and the generalization ability of deep learning models. Therefore, increasing the number and diversity 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.</p>","PeriodicalId":20100,"journal":{"name":"Plant Methods","volume":"21 1","pages":"46"},"PeriodicalIF":4.7,"publicationDate":"2025-03-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11955132/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143754150","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A novel approach integrating multispectral imaging and machine learning to identify seed maturity and vigor in smooth bromegrass.","authors":"Chengming Ou, Zhicheng Jia, Shiqiang Zhao, Shoujiang Sun, Ming Sun, Jingyu Liu, Manli Li, Shangang Jia, Peisheng Mao","doi":"10.1186/s13007-025-01359-8","DOIUrl":"10.1186/s13007-025-01359-8","url":null,"abstract":"<p><p>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.</p>","PeriodicalId":20100,"journal":{"name":"Plant Methods","volume":"21 1","pages":"45"},"PeriodicalIF":4.7,"publicationDate":"2025-03-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11938725/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143710914","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"GrainNet: efficient detection and counting of wheat grains based on an improved YOLOv7 modeling.","authors":"Xin Wang, Changchun Li, Chenyi Zhao, Yinghua Jiao, Hengmao Xiang, Xifang Wu, Huabin Chai","doi":"10.1186/s13007-025-01363-y","DOIUrl":"10.1186/s13007-025-01363-y","url":null,"abstract":"<p><strong>Background: </strong>Seed testing plays a crucial role in improving crop yields.In actual seed testing processes, factors such as grain sticking and complex imaging environments can significantly affect the accuracy of wheat grain counting, directly impacting the effectiveness of seed testing. However, most existing methods primarily focus on simple counting tasks and lack general applicability.</p><p><strong>Results: </strong>To enable fast and accurate counting of wheat grains under severe adhesion and complex scenarios, this study collected images of wheat grains from different varieties, backgrounds, densities, imaging heights, adhesion levels, and other natural conditions using various imaging devices and constructed a comprehensive wheat grain dataset through data enhancement techniques. We propose a wheat grain detection and counting model called GrainNet, which significantly improves the counting performance and detection speed across diverse conditions and adhesion levels by incorporating lightweight and efficient feature fusion modules. Specifically, the model incorporates an Efficient Multi-scale Attention (EMA) mechanism, effectively mitigating the interference of background noise on detection results. Additionally, the ASF-Gather and Distribute (ASF-GD) module optimizes the feature extraction component of the original YOLOv7 network, improving the model's robustness and accuracy in complex scenarios. Ablation experiments validate the effectiveness of the proposed methods.Compared with classic models such as Faster R-CNN, YOLOv5, YOLOv7, and YOLOv8, the GrainNet model achieves better detection performance and computational efficiency in various scenarios and adhesion levels. The mean Average Precision reached 93.15%, the F1 score was 0.946, and the detection speed was 29.10 frames per second (FPS). A comparative analysis with manual counting results revealed that the GrainNet model achieved the highest coefficient of determination and Mean Absolute Error values for wheat grain counting tasks, which were 0.93 and 5.97, respectively, with a counting accuracy of 94.47%.</p><p><strong>Conclusions: </strong>Overall, the GrainNet model presented in this study enables accurate and rapid recognition and quantification of wheat grains, which can provide a reference for effective seed examination of wheat grains in real scenarios. Related content can be accessed through the following link: https://github.com/1371530728/grainnet.git .</p>","PeriodicalId":20100,"journal":{"name":"Plant Methods","volume":"21 1","pages":"44"},"PeriodicalIF":4.7,"publicationDate":"2025-03-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11934480/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143701277","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Efficient mutagenesis and genotyping of maize inbreds using biolistics, multiplex CRISPR/Cas9 editing, and Indel-Selective PCR.","authors":"Maruti Nandan Rai, Brian Rhodes, Stephen Jinga, Praveena Kanchupati, Edward Ross, Shawn R Carlson, Stephen P Moose","doi":"10.1186/s13007-025-01365-w","DOIUrl":"10.1186/s13007-025-01365-w","url":null,"abstract":"<p><p>CRISPR/Cas9 based genome editing has advanced our understanding of a myriad of important biological phenomena. Important challenges to multiplex genome editing in maize include assembly of large complex DNA constructs, few genotypes with efficient transformation systems, and costly/labor-intensive genotyping methods. Here we present an approach for multiplex CRISPR/Cas9 genome editing system that delivers a single compact DNA construct via biolistics to Type I embryogenic calli, followed by a novel efficient genotyping assay to identify desirable editing outcomes. We first demonstrate the creation of heritable mutations at multiple target sites within the same gene. Next, we successfully created individual and stacked mutations for multiple members of a gene family. Genome sequencing found off-target mutations are rare. Multiplex genome editing was achieved for both the highly transformable inbred line H99 and Illinois Low Protein1 (ILP1), a genotype where transformation has not previously been reported. In addition to screening transformation events for deletion alleles by PCR, we also designed PCR assays that selectively amplify deletion or insertion of a single nucleotide, the most common outcome from DNA repair of CRISPR/Cas9 breaks by non-homologous end-joining. The Indel-Selective PCR (IS-PCR) method enabled rapid tracking of multiple edited alleles in progeny populations. The 'end to end' pipeline presented here for multiplexed CRISPR/Cas9 mutagenesis can be applied to accelerate maize functional genomics in a broader diversity of genetic backgrounds.</p>","PeriodicalId":20100,"journal":{"name":"Plant Methods","volume":"21 1","pages":"43"},"PeriodicalIF":4.7,"publicationDate":"2025-03-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11934539/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143701276","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Lightweight highland barley detection based on improved YOLOv5.","authors":"Minghui Cai, Hui Deng, Jianwei Cai, Weipeng Guo, Zhipeng Hu, Dongzheng Yu, Houxi Zhang","doi":"10.1186/s13007-025-01353-0","DOIUrl":"10.1186/s13007-025-01353-0","url":null,"abstract":"<p><p>Accurate and efficient assessment of highland barley (Hordeum vulgare L.) density is crucial for optimizing cultivation and management practices. However, challenges such as overlapping spikes in unmanned aerial vehicle (UAV) images and the computational requirements for high-resolution image analysis hinder real-time detection capabilities. To address these issues, this study proposes an improved lightweight YOLOv5 model for highland barley spike detection. We chose depthwise separable convolution (DSConv) and ghost convolution (GhostConv) for the backbone and neck networks, respectively, to reduce the parameter and computational complexity. In addition, the integration of convolutional block attention module (CBAM) enhances the model's ability to focus on target object in complex backgrounds. The results show that the improved YOLOv5 model has a significant improvement in detection performance. Precision and recall increased by 3.1% to 92.2% and 86.2%, respectively, with an F1 score of 0.892. The <math><msub><mtext>AP</mtext> <mrow><mn>0.5</mn></mrow> </msub> </math> reaches 92.7% and 93.5% for highland barley in the growth and maturation stages, respectively, and the overall <math><msub><mtext>mAP</mtext> <mrow><mn>0.5</mn></mrow> </msub> </math> improved to 93.1%. Compared to the baseline YOLOv5n model, the number of parameters and floating-point operations (FLOPs) were reduced by 70.6% and 75.6%, respectively, enabling lightweight deployment without compromising accuracy. In addition,the proposed model outperformed mainstream object detection algorithms such as Faster R-CNN, Mask R-CNN, RetinaNet, YOLOv7, and YOLOv8, in terms of detection accuracy and computational efficiency. Although this study also suffers from limitations such as insufficient generalization under varying lighting conditions and reliance on rectangular annotations, it provides valuable support and reference for the development of real-time highland barley spike detection systems, which can help to improve agricultural management.</p>","PeriodicalId":20100,"journal":{"name":"Plant Methods","volume":"21 1","pages":"42"},"PeriodicalIF":4.7,"publicationDate":"2025-03-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11934575/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143701278","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A rapid and efficient in vivo inoculation method for introducing tree stem canker pathogens onto leaves: suitable for large-scale assessment of resistance in poplar breeding progeny.","authors":"Zheng Li, Bingyu Zhang, Yuchen Fu, Yutian Suo, Yinan Zhang, Jinxia Feng, Long Pan, Wanna Shen, Huixiang Liu, Xiaohua Su, Jiaping Zhao","doi":"10.1186/s13007-025-01360-1","DOIUrl":"10.1186/s13007-025-01360-1","url":null,"abstract":"<p><strong>Background: </strong>Hybrid breeding, a direct and efficient strategy for disease control and management in tree species, is currently limited by the selection method of resist clones: the \"in vitro stem segment inoculation method\". This method, constrained by the availability of inoculating materials, cannot rapidly, efficiently, and cost-effectively screen the resistance of all hybrid clones. To overcome these limitations, we introduce a novel pathogen inoculation method for the resistance assessment of hybrid clones in the poplar-Valsa sordida pathosystem. This method involves inoculating the stem canker pathogen on the host leaf, a unique and promising approach we have successfully validated.</p><p><strong>Results: </strong>Results showed that stem canker pathogen V. sordida induced the extended necrotic lesion and even induced the formation of pycnidium structure and conidia on the leaf surface 5 days after mycelium inoculation; (1) the upper 5-7thleaves exhibited higher resistance than the middle 18-20th leaves; (2) the shading conditions induced more severe symptoms on the leaves than lighting conditions; (3) the poplar leaves were more susceptible to the juvenile mycelium inoculums (4-day-cultured) than the old ones (7-day-cultured). Our results demonstrate the robustness ofthe \"in vivo leaf inoculation method\" in revealing the resistance differentiation in poplar hybrid clones. According to the leaf necrotic area disease index, we divided these poplar clones into seven different resistance groups. The resistance assessed by leaf assessment was validated in 15 selected poplar clones using the \"in vitro stem segment inoculation method\". Results showed that the effectiveness of these two methods was consistent. Moreover, results also revealed the pathogenicity diversity of the pathogen population of tree species using leaf the inoculation method.</p><p><strong>Conclusions: </strong>Compared to the conventional \"in vitro stem segment inoculation method\", the leaf method has the advantages of abundant inoculation materials, easy operation, rapid disease onset, and almost no adverse effect on the host. It is particularly suitable for the resistance screening of all progeny and the early (seedling) phenotypic selection of resistant poplar clones in poplar stem disease resistance breeding. The \"in vivo leaf inoculation method\" holds significant promise in poplar breeding, tree pathology, and molecular biology research on tree stem diseases.</p>","PeriodicalId":20100,"journal":{"name":"Plant Methods","volume":"21 1","pages":"41"},"PeriodicalIF":4.7,"publicationDate":"2025-03-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11934469/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143701275","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A statistical method for high-throughput emergence rate calculation for soybean breeding plots based on field phenotypic characteristics.","authors":"Yan Sun, Mengqi Li, Meiling Liu, Jingyi Zhang, Yingli Cao, Xue Ao","doi":"10.1186/s13007-025-01356-x","DOIUrl":"10.1186/s13007-025-01356-x","url":null,"abstract":"<p><p>In the process of smart breeding, the rapid statistics of soybean emergence rate, as an important part of breeding screening, face challenges under environmental constraints, especially the selection and breeding of soybean varieties in dense environments. Due to the influence of environmental factors, the existing methods have shortcomings, such as low throughput, low efficiency, and insufficient precision. Therefore, an effective and precise statistical method is required. In this study, UAV (Unmanned Aerial Vehicle)-scale data combined with ground measurement data were used as the research object to explore the feasibility of improving the throughput, efficiency, and accuracy of breeding screening under intensive soybean planting. To this end, a set of technical solutions, including background removal, object detection, and accurate counting, were designed. Firstly, a combined background segmentation method based on contrast enhancement filtering combined with ultra-green eigenvalues and the Otsu algorithm was proposed to remove the complex background in remote sensing images and retain the morphological information of soybean seedlings. Secondly, the deep learning object detection model was used to infer and predict the processed images to label soybean seedlings. Then, a soybean seedling counting algorithm was constructed: by establishing a soybean seedling growth model, the idea of \"growth normalization\" was proposed, and the expansion-compression factor was defined to eliminate the influence of soybean seedling growth inconsistency on counting. After statistical and in-depth analysis of the growth and planting characteristics of soybean seedlings under overlapping conditions, the \"inter-seedling occlusion counting algorithm\" was proposed to solve the problem of overlapping counting between seedlings. In order to solve the problem of an overlapping bounding box, a soft strategy is specially designed to avoid the redundant values brought by it. Finally, according to the calculation results, the statistical thematic map of soybean emergence rate based on plot plots was displayed. After experiments, the proposed method can effectively count the number of soybean seedlings in the image, with an overall accuracy of 99.18% and an error rate of 0.82%. In addition, Yolov8n had the best recognition effect in the soybean seedling detection task, with a mAP (0.5-0.95) of 85.15%. The proposed background segmentation method increased the mAP (0.5-0.95) of the detection results by 4.06%. It has been demonstrated through experimental tests and verifications that solid support for the statistical work concerning the soybean emergence rate under the condition of intensive planting is provided by this method. This innovative method has played a facilitating role in accelerating the breeding process and has also provided some new ideas and reference directions for further exploration of efficient screening.</p>","PeriodicalId":20100,"journal":{"name":"Plant Methods","volume":"21 1","pages":"40"},"PeriodicalIF":4.7,"publicationDate":"2025-03-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11931824/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143692954","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A cell P system with membrane division and dissolution rules for soybean leaf disease recognition.","authors":"Hongping Song, Yourui Huang, Tao Han, Shanyong Xu, Quanzeng Liu","doi":"10.1186/s13007-025-01362-z","DOIUrl":"10.1186/s13007-025-01362-z","url":null,"abstract":"<p><p>Rapid and accurate identification of soybean leaf diseases is crucial for optimizing crop health and yield. We propose a cell P system with membrane division and dissolution rules (DDC-P system) for soybean leaf disease identification. Among them, the designed Efficient feature attention (EFA) and the lightweight sandglass structure and efficient feature attention (SGEFA) can focus on disease-specific information while reducing environmental interference. A fuzzy controller was developed to manage the division and dissolution of SGEFA membranes, allowing for adaptive adjustments to the model structure and avoiding redundancy. Experimental results on the homemade soybean disease dataset show that the DDC-P system achieves a recognition rate of 98.43% with an F1 score of 0.9874, while the model size is only 1.41 MB. On the public dataset, the DDC-P system achieves an accuracy of 94.40% with an F1 score of 0.9425. The average recognition time on the edge device is 0.042857 s, with an FPS of 23.3. These outstanding results demonstrate that the DDC-P system not only excels in recognition and generalization but is also ideally suited for deployment on edge devices, revolutionizing the approach to soybean leaf disease management.</p>","PeriodicalId":20100,"journal":{"name":"Plant Methods","volume":"21 1","pages":"39"},"PeriodicalIF":4.7,"publicationDate":"2025-03-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11916566/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143658154","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}