Search Results (8)

Soybean bacterial blight disease is a threat to soybean production. Multispectral technology has shown good potential in detecting this disease and can overcome the limitations of traditional methods. The aim of this study was to perform field monitoring of the dynamics of this disease in Northeast China in 2022. The correlation between the soybean chlorophyll content index (CCI) and disease grade was obtained using artificial inoculation of the pathogen. The correlation between the soybean CCI, disease grade, green normalized difference vegetation index (GNDVI), and soybean yield was analyzed using a drone-mounted spectrometer platform for image acquisition and preprocessing. The soybean CCI was negatively correlated with the disease grade. The GNDVI declined with disease progression, which allowed for an indirect determination of the disease grade. The soybean yield loss was significant at disease grade 4 for soybean bacterial blight disease. The random forest regression model was more accurate than the regression model in estimating the yield based on the GNDVI. Therefore, the GNDVI could be used to survey the disease class and estimate the yield using the random forest model. This study provides support for field trials of drone-mounted multispectral equipment. This surveillance approach holds the potential to bring about precision plant protection in the future. Full article

The segmentation of plant disease images enables researchers to quantify the proportion of disease spots on leaves, known as disease severity. Current deep learning methods predominantly focus on single diseases, simple lesions, or laboratory-controlled environments. In this study, we established and publicly released image datasets of field scenarios for three diseases: soybean bacterial blight (SBB), wheat stripe rust (WSR), and cedar apple rust (CAR). We developed Plant Disease Segmentation Networks (PDSNets) based on LinkNet with ResNet-18 as the encoder, including three versions: ×1.0, ×0.75, and ×0.5. The ×1.0 version incorporates a 4 × 4 embedding layer to enhance prediction speed, while versions ×0.75 and ×0.5 are lightweight variants with reduced channel numbers within the same architecture. Their parameter counts are 11.53 M, 6.50 M, and 2.90 M, respectively. PDSNetx0.5 achieved an overall F1 score of 91.96%, an Intersection over Union (IoU) of 85.85% for segmentation, and a coefficient of determination (R²) of 0.908 for severity estimation. On a local central processing unit (CPU), PDSNetx0.5 demonstrated a prediction speed of 34.18 images (640 × 640 pixels) per second, which is 2.66 times faster than LinkNet. Our work provides an efficient and automated approach for assessing plant disease severity in field scenarios. Full article

Accurate detection of soybean diseases is a critical component in achieving intelligent agricultural management. However, traditional methods often underperform in complex field scenarios. This paper proposes a diffusion-based object detection model that integrates the endogenous diffusion sub-network and the endogenous diffusion loss function to progressively optimize feature distributions, significantly enhancing detection performance for complex backgrounds and diverse disease regions. Experimental results demonstrate that the proposed method outperforms multiple baseline models, achieving a precision of 94%, recall of 90%, accuracy of 92%, and mAP@50 and mAP@75 of 92% and 91%, respectively, surpassing RetinaNet, DETR, YOLOv10, and DETR v2. In fine-grained disease detection, the model performs best on rust detection, with a precision of 96% and a recall of 93%. For more complex diseases such as bacterial blight and Fusarium head blight, precision and mAP exceed 90%. Compared to self-attention and CBAM, the proposed endogenous diffusion attention mechanism further improves feature extraction accuracy and robustness. This method demonstrates significant advantages in both theoretical innovation and practical application, providing critical technological support for intelligent soybean disease detection. Full article

Soybean bacterial blight, caused by Pseudomonas savastanoi pv. glycine, which is one of the common diseases of soybeans, has a strong harm and a great impact on the yield of soybeans. If the disease is not diagnosed in time and no solution comes up, it will lead to the serious loss of yield after the disease becomes serious. Therefore, this paper proposes the detection of the soybean bacterial blight with the polarization spectroscopic imaging method, derived from the detection principle and mathematical model of polarization bidirectional reflection distribution function on the basis of the Stokes vector analysis method. By synthesizing the spectral lines of the four polarization states and the non-polarization states, it was found that the physical parameters of I (135°, 90°) polarization state were the most suitable for identifying soybean bacterial blight disease, and other polarization states could also supplement the characteristic information. The results show that the polarization spectral image can effectively identify the polarization characteristics of healthy soybean leaves and early bacterial blight in the field, and can distinguish the healthy leaves and the diseased leaves by obtaining the relative polarization reflectance of different areas in soybean leaves. Finally, the soybean disease species can be accurately diagnosed. This paper provides an optical method for the detection of crop diseases and insect pests, which makes up for the deficiency of the traditional detection technology and can provide a scientific basis for the safe non-destructive detection of crop diseases and pests. Full article

Bacterial blight of soybean (BBS), caused by Pseudomonas syringae pv. glycinea, is one of the most devastating diseases in soybean with significant yield losses ranging from 4% to 40%. The timely detection of BBS is the foundation for disease control. However, traditional identification methods are inefficient and rely heavily on expert knowledge. Existing automated approaches have not achieved high accuracy in natural environments and often require advanced equipment and extensive training, limiting their practicality and adaptability. To overcome these challenges, we propose LeafDPN, an improved Dual-Path Network model enhanced with Vision Transformer blocks in the forward propagation function and SE blocks in the ConvBNLayer. These enhancements improved the model’s accuracy, receptive field, and feature expression capabilities. Experiments conducted on a self-constructed dataset of 864 expert-labeled images across three disease types demonstrated that LeafDPN achieved a 98.96% identification accuracy and the shorted iteration time in just 24 epochs. It outperformed 14 baseline models like HRNet and EfficientNet in terms of accuracy, training efficiency, and resource consumption. In addition, the proposed LeafDPN model has the potential to be applied in the identification of other plant diseases based on available datasets. Full article

As a follow-up to previous studies, the effects of Thymus vulgaris essential oil on selected virulence factors (growth, sessile cell survival, swimming, swarming, and exopolysaccharide production) were evaluated in phytopathogenic Pseudomonas syringae strains isolated from soybean fields in Argentina; reference strains Pseudomonas savastanoi pv. glycinea B076 and Pseudomonas aeruginosa PAO1. P. syringae are responsible for bacterial blight, a disease that affects crops worldwide. Plant bacterioses are usually treated with antibiotics and copper compounds, which may contribute to the development of resistance in pathogens and damage the environment. For these reasons, eco-friendly alternatives are necessary. Although aromatic plants are a natural source of antimicrobial substances, the effects of these substances on phytopathogenic bacteria remain largely unexplored. Subinhibitory concentrations of the oil significantly reduced the slope and rate of bacterial growth. In addition, biofilm and exopolysaccharide (EPS) production were inhibited, with swimming and swarming motility patterns being affected at all of the oil concentrations tested. Therefore, TEO could potentially be a highly efficient antipseudomonal agent for treating plant infections caused by P. syringae. Full article

Soybeans are a valuable food product, containing 40% protein and a large percentage of unsaturated fatty acids ranging from 17 to 23%. Pseudomonas savastanoi pv. glycinea (Psg) and Curtobacterium flaccumfaciens pv. flaccumfaciens (Cff) are harmful bacterial pathogens of soybean. The bacterial resistance of soybean pathogens to existing pesticides and environmental concerns requires new approaches to control bacterial diseases. Chitosan is a biodegradable, biocompatible and low-toxicity biopolymer with antimicrobial activity that is promising for use in agriculture. In this work, a chitosan hydrolysate and its nanoparticles with copper were obtained and characterized. The antimicrobial activity of the samples against Psg and Cff was studied using the agar diffusion method, and the minimum inhibitory concentration (MIC) and minimum bactericidal concentration (MBC) were determined. The samples of chitosan and copper-loaded chitosan nanoparticles (Cu²⁺ChiNPs) significantly inhibited bacterial growth and were not phytotoxic at the concentrations of the MIC and MBC values. The protective properties of chitosan hydrolysate and copper-loaded chitosan nanoparticles against soybean bacterial diseases were tested on plants in an artificial infection. It was demonstrated that the Cu²⁺ChiNPs were the most effective against Psg and Cff. Treatment of pre-infected leaves and seeds demonstrated that the biological efficiencies of (Cu²⁺ChiNPs) were 71% and 51% for Psg and Cff, respectively. Copper-loaded chitosan nanoparticles are promising as an alternative treatment for bacterial blight and bacterial tan spot and wilt in soybean. Full article

The bacteria Pseudomonas savastanoi pv. glycinea (Coerper, 1919; Gardan et al., 1992) (Psg) and Curtobacterium flaccumfaciens pv. flaccumfaciens (Hedges 1922) (Cff) are harmful pathogens of soybean (Glycine max). Presently, there are several strategies to control these bacteria, and the usage of environmentally friendly approaches is encouraged. In this work, purified essential oils (EOs) from 19 plant species and total aqueous and ethanolic plant extracts (PEs) from 19 plant species were tested in vitro to observe their antimicrobial activity against Psg and Cff (by agar diffusion and broth microdilution method). Tested EOs and PEs produced significant bacterial growth inhibition with technologically acceptable MIC and MBC values. Non-phytotoxic concentrations for Chinese cinnamon and Oregano essential oils and leather bergenia ethanolic extract, which previously showed the lowest MBC values, were determined. Testing of these substances with artificial infection of soybean plants has shown that the essential oils of Chinese cinnamon and oregano have the maximum efficiency against Psg and Cff. Treatment of leaves and seeds previously infected with phytopathogens with these essential oils showed that the biological effectiveness of leaf treatments was 80.6–77.5% and 86.9–54.6%, respectively, for Psg and Cff. GC-MS and GC-FID analyzes showed that the major compounds were 5-Methyl-3-methylenedihydro-2(3H)-furanone (20.32%) in leather bergenia ethanolic extract, cinnamaldehyde (84.25%) in Chinese cinnamon essential oil and carvacrol (62.32%) in oregano essential oil. Full article