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"disease severity"
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Early Detection of Powdery Mildew Disease and Accurate Quantification of Its Severity Using Hyperspectral Images in Wheat
Early detection of the crop disease using agricultural remote sensing is crucial as a precaution against its spread. However, the traditional method, relying on the disease symptoms, is lagging. Here, an early detection model using machine learning with hyperspectral images is presented. This study first extracted the normalized difference texture indices (NDTIs) and vegetation indices (VIs) to enhance the difference between healthy and powdery mildew wheat. Then, a partial least-squares linear discrimination analysis was applied to detect powdery mildew with the combined optimal features (i.e., VIs & NDTIs). Further, a regression model on the partial least-squares regression was developed to estimate disease severity (DS). The results show that the discriminant model with the combined VIs & NDTIs improved the ability for early identification of the infected leaves, with an overall accuracy value and Kappa coefficient over 82.35% and 0.56 respectively, and with inconspicuous symptoms which were difficult to identify as symptoms of the disease using the traditional method. Furthermore, the calibrated and validated DS estimation model reached good performance as the coefficient of determination (R2) was over 0.748 and 0.722, respectively. Therefore, this methodology for detection, as well as the quantification model, is promising for early disease detection in crops.
Journal Article
An Approach for Rice Bacterial Leaf Streak Disease Segmentation and Disease Severity Estimation
Rice bacterial leaf streak (BLS) is a serious disease in rice leaves and can seriously affect the quality and quantity of rice growth. Automatic estimation of disease severity is a crucial requirement in agricultural production. To address this, a new method (termed BLSNet) was proposed for rice and BLS leaf lesion recognition and segmentation based on a UNet network in semantic segmentation. An attention mechanism and multi-scale extraction integration were used in BLSNet to improve the accuracy of lesion segmentation. We compared the performance of the proposed network with that of DeepLabv3+ and UNet as benchmark models used in semantic segmentation. It was found that the proposed BLSNet model demonstrated higher segmentation and class accuracy. A preliminary investigation of BLS disease severity estimation was carried out based on our BLS segmentation results, and it was found that the proposed BLSNet method has strong potential to be a reliable automatic estimator of BLS disease severity.
Journal Article
Contrasting effects of mammal grazing on foliar fungal diseases
• Plant pathogens and their hosts often coexist with mammal grazers. However, the direction and strength of grazing effects on foliar fungal diseases can be idiosyncratic, varying among host plant species and pathogen types.
• We combined a 6 yr yak-grazing experiment, a clipping experiment simulating different mammal consumption patterns (leaf damage vs whole-leaf removal), and a meta-analysis of 63 comparisons to evaluate how grazing impacts foliar fungal diseases across plant growth types (grass vs forb) and pathogen life histories (biotroph vs necrotroph).
• In the yak-grazing experiment, grazing had no significant effect on disease severity, and grasses experienced a higher disease severity than forbs; there was a significant interaction between pathogen type and grazing. In both the yak-grazing experiment and meta-analysis, grazing decreased biotrophic pathogens (mainly rusts and powdery mildew), but did not affect necrotrophic pathogens (mainly leaf spots). The clipping experiment suggested that grazers might promote infection by necrotrophic pathogens by producing wounds on leaves, but inhibit biotrophic pathogens via leaf removal.
• In conclusion, our three-part approach revealed that intrinsic properties of both plants and pathogens shape patterns of disease in natural ecosystems, greatly improving our ability to predict disease severity under mammal grazing.
Journal Article
Spatial scale-dependent dilution effects of biodiversity on plant diseases in grasslands
The rapid biodiversity losses of the Anthropocene have motivated ecologists to understand how biodiversity affects infectious diseases. Spatial scale is thought to moderate negative biodiversity–disease relationships (i.e., dilution effects) in zoonotic diseases, whereas evidence from plant communities for an effect of scale remains limited, especially at local scales where the mechanisms (e.g., encounter reduction) underlying dilution effects actually work. Here, we tested how spatial scale affects the direction and magnitude of biodiversity–disease relationships. We utilized a 10-year-old nitrogen addition experiment in a Tibetan alpine meadow, with 0, 5, 10, and 15 g/m² nitrogen addition treatments. Within the treatment plots, we arranged a total of 216 quadrats (of either 0.125 × 0.125 m, 0.25 × 0.25 m or 0.5 × 0.5 m size) to test how the sample area affects the relationship between plant species richness and foliar fungal disease severity. We found that the dilution effects were stronger in the 0.125 × 0.125 m and 0.25 × 0.25 m quadrats, compared with 0.5 × 0.5 m quadrats. There was a significant interaction between species richness and nitrogen addition in the 0.125 × 0.125 m and 0.25 × 0.25 m quadrats, indicating that a dilution effect was more easily observed under higher levels of nitrogen addition. Based on multigroup structural equation models, we found that even accounting for the direct impact of nitrogen addition (i.e., “nitrogen-disease hypothesis”), the dilution effect still worked at the 0.125 × 0.125 m scale. Overall, these findings suggest that spatial scale directly determines the occurrence of dilution effects, and can partly explain the observed variation in biodiversity–disease relationships in grasslands. Next-generation frameworks for predicting infectious diseases under rapid biodiversity loss scenarios need to incorporate spatial information.
Journal Article
Automatic Estimation of Crop Disease Severity Levels Based on Vegetation Index Normalization
The timely monitoring of crop disease development is very important for precision agriculture applications. Remote sensing-based vegetation indices (VIs) can be good indicators of crop disease severity, but current methods are mainly dependent on manual ground survey results. Based on VI normalization, an automated crop disease severity grading method without the use of ground surveys was proposed in this study. This technique was applied to two cotton fields infested with different levels of cotton root rot in south Texas in the United States, where airborne hyperspectral imagery was collected. Six typical VIs were calculated from the hyperspectral imagery and their histograms indicated that VI normalization could eliminate the influences of variable field conditions and the VI value range variations, allowing a potentially broader scope of application. According to the analysis of the obtained results from the spectral dimension, spatial dimension and descriptive statistics, the disease grading results were in general agreement with previous ground survey results, proving the validity of the disease severity grading method. Although satisfactory results could be achieved from different types of VI, there is still room for further improvement through the exploration of more VIs. With the advantages of independence of ground surveys and potential universal applicability, the newly proposed crop disease grading method will be of great significance for crop disease monitoring over large geographical areas.
Journal Article
Clinical Aspects and Disease Severity of Streptococcus dysgalactiae Subspecies equisimilis Bacteremia, Finland
We conducted a prospective study of 159 cases of Streptococcus dysgalactiae subspecies equisimilis (SDSE) bacteremia in 157 patients at 2 hospitals in Finland during November 2015–November 2019. Cellulitis was associated with nonsevere disease (p = 0.008); necrotizing fasciitis was associated with severe disease (p = 0.004). Fifty percent of patients had >1 clinical characteristic associated with risk for death. The case-fatality rate was 6%, and 7% of patients were treated in an intensive care unit. Blood leukocyte counts on days 2 (p = 0.032) and 3 (p = 0.020) and C-reactive protein levels on days 3 (p = 0.030) and 4 (p = 0.009) after admission were predictors of severe disease. The Pitt bacteremia score was an accurate predictor of death. Using the Pitt bacteremia score, leukocyte counts, and CRP responses during initial treatment can improve treatment strategies and survival for patients with SDSE.
Journal Article
Data-Driven Models for Objective Grading Improvement of Parkinson’s Disease
Parkinson’s disease (PD) is a progressive disorder of the central nervous system that causes motor dysfunctions in affected patients. Objective assessment of symptoms can support neurologists in fine evaluations, improving patients’ quality of care. Herein, this study aimed to develop data-driven models based on regression algorithms to investigate the potential of kinematic features to predict PD severity levels. Sixty-four patients with PD (PwPD) and 50 healthy subjects of control (HC) were asked to perform 13 motor tasks from the MDS-UPDRS III while wearing wearable inertial sensors. Simultaneously, the clinician provided the evaluation of the tasks based on the MDS-UPDRS scores. One hundred-ninety kinematic features were extracted from the inertial motor data. Data processing and statistical analysis identified a set of parameters able to distinguish between HC and PwPD. Then, multiple feature selection methods allowed selecting the best subset of parameters for obtaining the greatest accuracy when used as input for several predicting regression algorithms. The maximum correlation coefficient, equal to 0.814, was obtained with the adaptive neuro-fuzzy inference system (ANFIS). Therefore, this predictive model could be useful as a decision support system for a reliable objective assessment of PD severity levels based on motion performance, improving patients monitoring over time.
Journal Article
Re-appraisal of the obesity paradox in heart failure: a meta-analysis of individual data
BackgroundHigher body mass index (BMI) is associated with better outcome compared with normal weight in patients with HF and other chronic diseases. It remains uncertain whether the apparent protective role of obesity relates to the absence of comorbidities. Therefore, we investigated the effect of BMI on outcome in younger patients without co-morbidities as compared to older patients with co-morbidities in a large heart failure (HF) population.MethodsIn an individual patient data analysis from pooled cohorts, 5,819 patients with chronic HF and data available on BMI, co-morbidities and outcome were analysed. Patients were divided into four groups based on BMI (i.e. ≤ 18.5 kg/m2, 18.5–25.0 kg/m2; 25.0–30.0 kg/m2; 30.0 kg/m2). Primary endpoints included all-cause mortality and HF hospitalization-free survival.ResultsMean age was 65 ± 12 years, with a majority of males (78%), ischaemic HF and HF with reduced ejection fraction. Frequency of all-cause mortality or HF hospitalization was significantly worse in the lowest two BMI groups as compared to the other two groups; however, this effect was only seen in patients older than 75 years or having at least one relevant co-morbidity, and not in younger patients with HF only. After including medications and N-terminal pro-B-type natriuretic peptide and high-sensitivity cardiac troponin concentrations into the model, the prognostic impact of BMI was largely absent even in the elderly group with co-morbidity.ConclusionsThe present study suggests that obesity is a marker of less advanced disease, but does not have an independent protective effect in patients with chronic HF.Graphic abstractCategories of BMI are only predictive of poor outcome in patients aged > 75 years or with at least one co-morbidity (bottom), but not in those aged < 75 years without co-morbidities (top). The prognostic effect largely disappears in multivariable analyses even for the former group. These findings question the protective effect of obesity in chronic heart failure (HF).
Journal Article
A Cucumber Leaf Disease Severity Grading Method in Natural Environment Based on the Fusion of TRNet and U-Net
Disease severity grading is the primary decision-making basis for the amount of pesticide usage in vegetable disease prevention and control. Based on deep learning, this paper proposed an integrated framework, which automatically segments the target leaf and disease spots in cucumber images using different semantic segmentation networks and then calculates the area of disease spots and the target leaf for disease severity grading. Two independent datasets of leaves and lesions were constructed, which served as the training set for the first-stage diseased leaf segmentation and the second-stage lesion segmentation models. The leaf dataset contains 1140 images, and the lesion data set contains 405 images. The proposed TRNet was composed of a convolutional network and a Transformer network and achieved an accuracy of 93.94% by fusing local features and global features for leaf segmentation. In the second stage, U-Net (Resnet50 as the feature network) was used for lesion segmentation, and a Dice coefficient of 68.14% was obtained. After integrating TRNet and U-Net, a Dice coefficient of 68.83% was obtained. Overall, the two-stage segmentation network achieved an average accuracy of 94.49% and 94.43% in the severity grading of cucumber downy mildew and cucumber anthracnose, respectively. Compared with DUNet and BLSNet, the average accuracy of TUNet in cucumber downy mildew and cucumber anthracnose severity classification increased by 4.71% and 8.08%, respectively. The proposed model showed a strong capability in segmenting cucumber leaves and disease spots at the pixel level, providing a feasible method for evaluating the severity of cucumber downy mildew and anthracnose.
Journal Article