Using aerial imagery coupled with machine learning to assess Goss's Wilt disease severity in field corn

2021 ◽  
Keyword(s):  
Machine Learning ◽  
Disease Severity ◽  
Wilt Disease ◽  
Aerial Imagery ◽  
Field Corn

scholarly journals Unsupervised Phenotypic Clustering for Determining Clinical Status in Children with Cystic Fibrosis

2021 ◽  
pp. 2002881
Author(s):  
Nicole Filipow ◽  
Gwyneth Davies ◽  
Eleanor Main ◽  
Neil J. Sebire ◽  
Colin Wallis ◽  
...  
Keyword(s):  
Machine Learning ◽  
Cystic Fibrosis ◽  
Lung Function ◽  
Disease Severity ◽  
Learning Algorithm ◽  
Severe Disease ◽  
Clinical Status ◽  
Oral Antibiotics ◽  
Multisystem Disease ◽  
Street Hospital

BackgroundCystic Fibrosis (CF) is a multisystem disease in which assessing disease severity based on lung function alone may not be appropriate. The aim of the study was to develop a comprehensive machine-learning algorithm to assess clinical status independent of lung function in children.MethodsA comprehensive prospectively collected clinical database (Toronto, Canada) was used to apply unsupervised cluster analysis. The defined clusters were then compared by current and future lung function, risk of future hospitalisation, and risk of future pulmonary exacerbation (PEx) treated with oral antibiotics. A K-Nearest Neighbours (KNN) algorithm was used to prospectively assign clusters. The methods were validated in a paediatric clinical CF dataset from Great Ormond Street Hospital (GOSH).ResultsThe optimal cluster model identified four (A-D) phenotypic clusters based on 12 200 encounters from 530 individuals. Two clusters (A,B) consistent with mild disease were identified with high FEV1, and low risk of both hospitalisation and PEx treated with oral antibiotics. Two clusters (C,D) consistent with severe disease were also identified with low FEV1. Cluster D had the shortest time to both hospitalisation and PEx treated with oral antibiotics. The outcomes were consistent in 3124 encounters from 171 children at GOSH. The KNN cluster allocation error rate was low, at 2.5% (Toronto), and 3.5% (GOSH).ConclusionMachine learning derived phenotypic clusters can predict disease severity independent of lung function and could be used in conjunction with functional measures to predict future disease trajectories in CF patients.

Machine learning for lung texture analysis on thin-section CT: Capability for assessments of disease severity and therapeutic effect for connective tissue disease patients in comparison with expert panel evaluations

2021 ◽  
pp. 028418512110449
Author(s):  
Yoshiharu Ohno ◽  
Kota Aoyagi ◽  
Daisuke Takenaka ◽  
Takeshi Yoshikawa ◽  
Yasuko Fujisawa ◽  
...  
Keyword(s):  
Machine Learning ◽  
Texture Analysis ◽  
Disease Severity ◽  
Thin Section ◽  
Ct Scans ◽  
Normal Lung ◽  
Quantitative Index ◽  
Serial Ct ◽  
Ct Texture Analysis

Background The need for quantitative assessment of interstitial lung involvement on thin-section computed tomography (CT) has arisen in interstitial lung diseases including connective tissue disease (CTD). Purpose To evaluate the capability of machine learning (ML)-based CT texture analysis for disease severity and treatment response assessments in comparison with qualitatively assessed thin-section CT for patients with CTD. Material and Methods A total of 149 patients with CTD-related ILD (CTD-ILD) underwent initial and follow-up CT scans (total 364 paired serial CT examinations), pulmonary function tests, and serum KL-6 level tests. Based on all follow-up examination results, all paired serial CT examinations were assessed as “Stable” (n = 188), “Worse” (n = 98) and “Improved” (n = 78). Next, quantitative index changes were determined by software, and qualitative disease severity scores were assessed by consensus of two radiologists. To evaluate differences in each quantitative index as well as in disease severity score between paired serial CT examinations, Tukey's honestly significant difference (HSD) test was performed among the three statuses. Stepwise regression analyses were performed to determine changes in each pulmonary functional parameter and all quantitative indexes between paired serial CT scans. Results Δ% normal lung, Δ% consolidation, Δ% ground glass opacity, Δ% reticulation, and Δdisease severity score showed significant differences among the three statuses ( P < 0.05). All differences in pulmonary functional parameters were significantly affected by Δ% normal lung, Δ% reticulation, and Δ% honeycomb (0.16 ≤r2 ≤0.42; P < 0.05). Conclusion ML-based CT texture analysis has better potential than qualitatively assessed thin-section CT for disease severity assessment and treatment response evaluation for CTD-ILD.

Assessing winter wheat foliage disease severity using aerial imagery acquired from small Unmanned Aerial Vehicle (UAV)

2020 ◽  
Vol 176 ◽  
pp. 105665
Author(s):  
Mahendra Bhandari ◽  
Amir M.H. Ibrahim ◽  
Qingwu Xue ◽  
Jinha Jung ◽  
Anjin Chang ◽  
...  
Keyword(s):  
Winter Wheat ◽  
Disease Severity ◽  
Unmanned Aerial Vehicle ◽  
Aerial Imagery ◽  
Aerial Vehicle

scholarly journals Meta-Analysis of Yield Response of Hybrid Field Corn to Foliar Fungicides in the U.S. Corn Belt

2011 ◽  
Vol 101 (9) ◽  
pp. 1122-1132 ◽  
Author(s):  
P. A. Paul ◽  
L. V. Madden ◽  
C. A. Bradley ◽  
A. E. Robertson ◽  
G. P. Munkvold ◽  
...  
Keyword(s):  
United States ◽  
Disease Severity ◽  
Meta Analysis ◽  
The United States ◽  
Yield Response ◽  
Foliar Disease ◽  
Yield Increase ◽  
Field Corn ◽  
Yield Difference ◽  
Meta Analyses

The use of foliar fungicides on field corn has increased greatly over the past 5 years in the United States in an attempt to increase yields, despite limited evidence that use of the fungicides is consistently profitable. To assess the value of using fungicides in grain corn production, random-effects meta-analyses were performed on results from foliar fungicide experiments conducted during 2002 to 2009 in 14 states across the United States to determine the mean yield response to the fungicides azoxystrobin, pyraclostrobin, propiconazole + trifloxystrobin, and propiconazole + azoxystrobin. For all fungicides, the yield difference between treated and nontreated plots was highly variable among studies. All four fungicides resulted in a significant mean yield increase relative to the nontreated plots (P < 0.05). Mean yield difference was highest for propiconazole + trifloxystrobin (390 kg/ha), followed by propiconazole + azoxystrobin (331 kg/ha) and pyraclostrobin (256 kg/ha), and lowest for azoxystrobin (230 kg/ha). Baseline yield (mean yield in the nontreated plots) had a significant effect on yield for propiconazole + azoxystrobin (P < 0.05), whereas baseline foliar disease severity (mean severity in the nontreated plots) significantly affected the yield response to pyraclostrobin, propiconazole + trifloxystrobin, and propiconazole + azoxystrobin but not to azoxystrobin. Mean yield difference was generally higher in the lowest yield and higher disease severity categories than in the highest yield and lower disease categories. The probability of failing to recover the fungicide application cost (ploss) also was estimated for a range of grain corn prices and application costs. At the 10-year average corn grain price of $0.12/kg ($2.97/bushel) and application costs of $40 to 95/ha, ploss for disease severity <5% was 0.55 to 0.98 for pyraclostrobin, 0.62 to 0.93 for propiconazole + trifloxystrobin, 0.58 to 0.89 for propiconazole + azoxystrobin, and 0.91 to 0.99 for azoxystrobin. When disease severity was >5%, the corresponding probabilities were 0.36 to 95, 0.25 to 0.69, 0.25 to 0.64, and 0.37 to 0.98 for the four fungicides. In conclusion, the high ploss values found in most scenarios suggest that the use of these foliar fungicides is unlikely to be profitable when foliar disease severity is low and yield expectation is high.

scholarly journals Biological management of fusarium wilt on tomato caused by Fusarium oxysporum f. sp. lycospersici by some plant growth-promoting bacteria

2020 ◽  
Author(s):  
Halima Z. Hussein ◽  
Shaker I. Al-Dulaimi
Keyword(s):  
Fusarium Oxysporum ◽  
Disease Severity ◽  
Fusarium Wilt ◽  
Wilt Disease ◽  
Systemic Resistance ◽  
Control Treatment ◽  
Plant Growth Promoting Bacteria ◽  
Tomato Plants ◽  
Fusarium Wilt Disease ◽  
Infection Percentage

AbstractChemical approaches have been applied to combat Fusarium wilt disease for a long time. Even though pesticides are effective in controlling the disease, they continue to damage the environment. Environmental-friendly approaches to manage plant disease are the goal of many studies recently. This study was conducted to assess the efficacy of some bio-agents in induction of systemic resistance in tomato plants as a management approach of Fusarium wilt disease caused by Fusarium oxysporum f.sp. lycopersici (FOL) under condition Plastic house. Results of the plastic house experiments showed that all treatments in decreased Fusarium disease percentage and severity on tomato, two bacterial combinations (Streptomyces sp. (St) and Pseudomonas fluorescence (Pf)) decreased the infection percentage and disease severity with 16.6% and 8.3%, respectively. Treatment with St reduced the infection percentage and disease severity with 33.3% and 22.8%, while the Pf treatment showed 41.6% and 31.2% reduction in infection percentage and disease severity, compared to 100% and 91.6% in the control treatment. Results of induced systemic resistance (ISR) biochemical indicators showed significant differences in tomato plants. Peroxidase and Phenylalanine-Ammonia-Lyase (PAL) activity and the Phenol content increased significantly 14 days after treatments compared to the control treatment, which contains only the fungal pathogen FOL.

scholarly journals Biological control of Ralstonia solanacearum (Smith), the causal pathogen of bacterial wilt disease by using Pantoea spp

2021 ◽  
Vol 31 (1) ◽  
Author(s):  
Kamal A. M. Abo-Elyousr ◽  
Sabry A. Hassan
Keyword(s):  
Biological Control ◽  
Cell Suspension ◽  
Disease Severity ◽  
Culture Filtrate ◽  
Bacterial Wilt ◽  
Ralstonia Solanacearum ◽  
Wilt Disease ◽  
Nucleotide Sequence Analysis ◽  
Tomato Root

Abstract Background Bacterial wilt of tomato (BWP) caused by Ralstonia solanacearum (Smith) is a very important disease. Biological control of this disease is a very important tool to protect the plant and environment from pollution of chemical control. Results Twenty isolates of genus, Pantoea were isolated from healthy tomato root. Out of 20 isolates, 2 strains, PHYTPO1 and PHYTPO2, showed highly antagonistic property to control the growth of R. solanacearum in vitro conditions. They were identified as P. agglomerans by using 16S rRNA nucleotide sequence analysis. The 2 isolates were selected to study their effect (as cell suspension or culture filtrate) on the bacterial wilt under greenhouse conditions. PHYTPO1 inhibited maximum growth reduction of R. solanacearum and formed 2.5 cm2 of inhibition zone, followed by 1.2 cm2 in PHYTOPO2 under in vitro conditions. Treating with both isolates of P. agglomerans was significantly reduced disease severity of tomato wilt disease. The disease severity was reduced to 74.1 when treated as cell suspension, while when treated as culture filtrate, it reduced the disease severity up to 69.4 than infected control. Conclusion The strains of Pantoea can be used as an ecofriendly method to control of the most economic pathogen of tomato under greenhouse conditions. Further study is needed to find an appropriate formulation and approving application of these bacteria under field conditions.

scholarly journals Machine Learning for Predicting Individual Severity of Blepharospasm Using Diffusion Tensor Imaging

2021 ◽  
Vol 15 ◽  
Author(s):  
Gang Liu ◽  
Yanan Gao ◽  
Ying Liu ◽  
Yaomin Guo ◽  
Zhicong Yan ◽  
...  
Keyword(s):  
Machine Learning ◽  
Diffusion Tensor Imaging ◽  
White Matter ◽  
Disease Severity ◽  
Rating Scale ◽  
Diffusion Tensor ◽  
Support Vector ◽  
Learning Scheme ◽  
The Individual ◽  
Jankovic Rating Scale

Accumulating diffusion tensor imaging (DTI) evidence suggests that white matter abnormalities evaluated by local diffusion homogeneity (LDH) or fractional anisotropy (FA) occur in patients with blepharospasm (BSP), both of which are significantly correlated with disease severity. However, whether the individual severity of BSP can be identified using these DTI metrics remains unknown. We aimed to investigate whether a combination of machine learning techniques and LDH or FA can accurately identify the individual severity of BSP. Forty-one patients with BSP were assessed using the Jankovic Rating Scale and DTI. The patients were assigned to non-functionally and functionally limited groups according to their Jankovic Rating Scale scores. A machine learning scheme consisting of beam search and support vector machines was designed to identify non-functionally versus functionally limited outcomes, with the input features being LDH or FA in 68 white matter regions. The proposed machine learning scheme with LDH or FA yielded an overall accuracy of 88.67 versus 85.19% in identifying non-functionally limited versus functionally limited outcomes. The scheme also identified a sensitivity of 91.40 versus 85.87% in correctly identifying functionally limited outcomes, a specificity of 83.33 versus 83.67% in accurately identifying non-functionally limited outcomes, and an area under the curve of 93.7 versus 91.3%. These findings suggest that a combination of LDH or FA measurements and a sophisticated machine learning scheme can accurately and reliably identify the individual disease severity in patients with BSP.

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