Search claims · PlantClaims

Associationobservedoriginal resultsignificant (as stated)measured

Claim · model-written summary

Low-temperature severity was significantly correlated with drought severity.

Taxon: maizeZea mayse2
Treatment: low-temperature severitye2
Outcome: drought severitye2

Not reportedDirection

Verbatim

e2
“low-temperature severity and maize yield loss rate, low-temperature severity, and drought severity had significant correlation”
✓ located verbatim in the source · abstract, characters 934–1060

Title

Dynamic vulnerability assessment of maize under low temperature and drought concurrent stress in Songliao Plain

Yunmeng Zhao; Mula Na; Ying Guo; et al. Agricultural Water Management 2023. doi:10.1016/j.agwat.2023.108400

Other claims from this paper (7)

Method / resourceobservedoriginal resultmeasured

Claim · model-written summary

Spatial analysis identified low to high fire severity and estimated burned area by severity class.

Taxon: peatland vegetationt0
Outcome: fire severitye0
Qualifier: burned area estimation for each fire severity classe0

Not reportedComparatorValue

Verbatim

e0
“Spatial analyses indicated low to high fire severity with burned area estimation for each fire severity class.”
✓ located verbatim in the source · abstract, characters 1132–1242

t0
“Fire severity assessment on peatland vegetation diversity”
✓ located verbatim in the source · title, characters 0–57

Title

Fire severity assessment on peatland vegetation diversity

Lailan Syaufina; Imas S Sitanggang; Aisyah Anggraini; et al. IOP Conference Series Earth and Environmental Science 2022. doi:10.1088/1755-1315/1025/1/012014

Other claims from this paper (5)

Association Tree species diversity was significantly lower in burned than pristine peat swamp forest.
Association The magnitude of tree diversity change varied with fire severity and pre-fire condition.
Method / resource Field assessment gives detailed estimates of fire damage and loss.
Method / resource Spatial assessment gives broader fire severity estimates.
Recommendation The authors propose field assessment to verify spatial fire severity assessment.

Observationobservedoriginal resultmeasured

Claim · model-written summary

High-severity burned Q. ilex regenerated faster than low-severity burned trees.

Taxon: Q. ilexQuercus ilexe0
Treatment: high severitye0
Outcome: short-term regeneratione0
Direction: fastere0
Comparator: low severitye0

Verbatim

e0
“Q. ilex burned with high severity have a faster short-term regeneration than those burned with low severity”
✓ located verbatim in the source · abstract, characters 978–1085

Title

Burn Severity Effect on the Short-Term Functional Response of Quercus ilex after Fire

Antonio Parra; M. Belén Hinojosa Fire 2023. doi:10.3390/fire6080286

Other claims from this paper (5)

Method / resourceobservedoriginal resultpositivemeasured

Claim · model-written summary

The leaf wilt severity method classified severity with 97% accuracy.

Taxon: Coconutt0
Outcome: classifies the severitye0
Value: 97%e0

Verbatim

e0
“the proposed WCWLD disease severity identification method classifies the severity with an accuracy of 97%.”
✓ located verbatim in the source · abstract, characters 1211–1317

t0
“Coconut Leaf Wilt Disease”
✓ located verbatim in the source · title, characters 52–77

Title

Early Diagnosis and Severity Assessment of Weligama Coconut Leaf Wilt Disease and Coconut Caterpillar Infestation Using Deep Learning-Based Image Processing Techniques

Samitha Vidhanaarachchi; Janaka L. Wijekoon; W. A. Shanaka P. Abeysiriwardhana; et al. IEEE Access 2025. doi:10.1109/access.2025.3537664

Other claims from this paper (5)

Method / resource The method identified Weligama Coconut Leaf Wilt Disease with 90% accuracy.
Method / resource The method identified coconut caterpillar infestation with 95% accuracy.
Method / resource YOLOv5 counted caterpillars in leaflets with 96.87% accuracy.
Method / resource YOLOv8 counted caterpillars in leaflets with 96.1% accuracy.
Method / resource YOLO11 counted caterpillars in leaflets with 95.9% accuracy.

Method / resourceobservedoriginal resultmeasured

Claim · model-written summary

Image-based severity evaluation correlated with standard severity measurements at R2 0.84.

Taxon: potatoSolanum tuberosumt0
Outcome: R2 correlatione0
Value: 0.84e0
Comparator: standard measurements of severitye0

Verbatim

e0
“Defect severity level evaluation had an R2 correlation of 0.84 against standard measurements of severity”
✓ located verbatim in the source · abstract, characters 1575–1679

t0
“potato”
✓ located verbatim in the source · title, characters 50–56

Title

External defects and severity level evaluation of potato using single and multispectral imaging in near infrared region

Dimas Firmanda Al Riza; Slamet Widodo; Kazuya Yamamoto; et al. Information Processing in Agriculture 2022. doi:10.1016/j.inpa.2022.09.001

Other claims from this paper (4)

Observation Common scab and mechanical damage appeared brighter at 1600 nm.
Method / resource Pseudo-color conversion gave more surface-characteristic information than grayscale imaging.
Method / resource Pseudo-color segmentation detected common scab and mechanical damage, excluding soil, at Dice 0.64.
Method / resource Single-image segmentation at 1600 nm attained Dice 0.72 but also segmented thick soil.

Observationobservedoriginal resultpositivemeasured

Claim · model-written summary

The ResNet50 framework classified plant disease severity with high accuracy across severity levels.

Taxon: plant leafe0
Outcome: disease severitye1
Qualifier: high accuracye1

Verbatim

e0
“process plant leaf images”
✓ located verbatim in the source · abstract, characters 349–374

e1
“classify disease severity with high accuracy”
✓ located verbatim in the source · abstract, characters 423–467

e2
“distinguishing between varying levels of disease progression”
✓ located verbatim in the source · abstract, characters 687–747

Title

Enhancing Crop Health Monitoring: A ResNet50 Approach to Automated Plant Disease Severity Prediction

Gunavathi H S; Ishani Priya; Kothakota Bindhu Sree; et al. 2025 IEEE International Students' Conference on Electrical, Electronics and Computer Science (SCEECS) 2025. doi:10.1109/sceecs64059.2025.10940498

Method / resourceobservedoriginal resultmeasured

Claim · model-written summary

TomatoMTL achieved 77.66% severity prediction accuracy.

Taxon: TomatoSolanum lycopersicumt0
Outcome: severity prediction accuracye1
Value: 77.66%e1

Verbatim

t0
“Tomato leaf disease and severity prediction”
✓ located verbatim in the source · title, characters 0–43

e1
“77.66% severity prediction accuracy”
✓ located verbatim in the source · abstract, characters 1273–1308

Title

Tomato leaf disease and severity prediction using multi-task learning

Anusri Kadam; Parnika Jain; Srishti Tripathi; et al. BMC Plant Biology 2026. doi:10.1186/s12870-026-09296-x

Other claims from this paper (8)

Method / resource TomatoMTL achieved 97.85% disease classification accuracy.
Method / resource TomatoMTL outperformed single-task classifiers (EfficientNetV2-S, ViT-B/16, ConvNeXt-Tiny) in accuracy.
Method / resource TomatoMTL outperformed existing multi-task learning baselines (Cross-Stitch Networks, MTAN) in accuracy.
Intervention effect Ablation studies confirmed individual contributions of CBAM, MixUp and CutMix augmentation, and the cross-task attention mechanism.
Method / resource Statistical analysis over five independent runs yielded p-values less than 0.001 for the reported improvements.
Method / resource Statistical analysis over five independent runs yielded Cohen's d greater than 14 for the reported improvements.
Method / resource TomatoMTL achieved 89.4% Pointing Game accuracy in localizing disease regions.
Method / resource Attention maps focus on biologically meaningful disease regions.

Observationobservedoriginal resultmeasured

Claim · model-written summary

High-severity burned Q. ilex had higher water potential than low-severity or unburned trees.

Taxon: Q. ilexQuercus ilexe0
Treatment: high severity (HB+)e0
Outcome: water potentiale0
Direction: highere0
Comparator: low severity (LB+) or unburned (B−)e0

Verbatim

e0
“The Q. ilex burned with high severity (HB+) showed higher water potential and shoot growth than those burned with low severity (LB+) or unburned (B−)”
✓ located verbatim in the source · abstract, characters 439–588

Title

Burn Severity Effect on the Short-Term Functional Response of Quercus ilex after Fire

Antonio Parra; M. Belén Hinojosa Fire 2023. doi:10.3390/fire6080286

Other claims from this paper (5)

Observation High-severity burned Q. ilex had higher shoot growth than low-severity or unburned trees.
Observation Both high- and low-severity resprouting plants had higher summer carbon assimilation than unburned plants.
Association HB+ plants had higher specific leaf area.
Association HB+ plants had lower water use efficiency.
Observation High-severity burned Q. ilex regenerated faster than low-severity burned trees.

Observationobservedoriginal resultmeasured

Claim · model-written summary

High-severity burned Q. ilex had higher shoot growth than low-severity or unburned trees.

Taxon: Q. ilexQuercus ilexe0
Treatment: high severity (HB+)e0
Outcome: shoot growthe0
Direction: highere0
Comparator: low severity (LB+) or unburned (B−)e0

Verbatim

e0
“The Q. ilex burned with high severity (HB+) showed higher water potential and shoot growth than those burned with low severity (LB+) or unburned (B−)”
✓ located verbatim in the source · abstract, characters 439–588

Title

Burn Severity Effect on the Short-Term Functional Response of Quercus ilex after Fire

Antonio Parra; M. Belén Hinojosa Fire 2023. doi:10.3390/fire6080286

Other claims from this paper (5)

Observationobservedoriginal resultpositivemeasured

Claim · model-written summary

High severity increased profitability relative to low severity.

Taxon: cornZea mays subsp. mayst0
Outcome: profitabilitye0
Direction: greatere0
Comparator: low severity conditionse0

Verbatim

e0
“greater profitability relative to low severity conditions”
✓ located verbatim in the source · abstract, characters 1295–1352

t0
“Hybrid Corn”
✓ located verbatim in the source · title, characters 130–141

t1
“Hybrid Corn”
✓ located verbatim in the source · title, characters 130–141

Title

Investigations into Economic Returns Resulting from Foliar Fungicides and Application Timing on Management of Tar Spot in Indiana Hybrid Corn

Tiffanna J. Ross; Tom W. Allen; Sujoung Shim; et al. Plant Disease 2023. doi:10.1094/pdis-05-23-0932-re

Other claims from this paper (12)

Intervention effect All tested fungicides suppressed corn tar spot relative to the untreated control.
Intervention effect All tested fungicides increased corn canopy greenness relative to untreated control.
Intervention effect Mefentrifluconazole plus pyraclostrobin was among the most effective for disease severity, greenness, yield and returns.
Intervention effect Metconazole plus pyraclostrobin was among the most effective at reducing disease severity.
Intervention effect Cyproconazole plus picoxystrobin at tassel was among the most effective at reducing disease severity.
Intervention effect Propiconazole combination between tassel and dough was among most effective at reducing disease severity.
Observation Fungicide products differed in yield protection under low and high disease severity.
Observation High disease severity conditions consistently brought positive yield increases with fungicides.
Observation Under high severity, average yield increases across products were 544.6 kg/ha greater.
Observation Under high severity, yield increases across timing treatments were 1,020.7 kg/ha greater.
Observation Under high severity, partial returns for products were $92.6/ha greater.
Observation Under high severity, partial returns for timed applications were $173.5/ha greater.

“severity”

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