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Method / resourceobservedoriginal resultnegativemeasured

Claim · model-written summary

Measured temperature sensor accuracy was below rated accuracy.

Outcome
temperatureaccuracye0
Direction
less thane0
Comparator
rated accuracye0

Verbatim

e0

“temperatureaccuracy was less than rated accuracy”

✓ located verbatim in the source · abstract, characters 1534–1582

Title

ACCURACY OF GRAIN MOISTURE CONTENT PREDICTION USING TEMPERATURE AND RELATIVE HUMIDITY SENSORS

M. S. Uddin; P. R. Armstrong; N. Zhang Applied Engineering in Agriculture 2006. doi:10.13031/2013.20273

Other claims from this paper (6)
Associationobservedoriginal resultnegativemeasured

Claim · model-written summary

Prediction accuracy across generations was poorer than within-generation accuracy.

Outcome
Accuracy across generationse4
Direction
poorere4
Comparator
within-generation accuracye4

Verbatim

t0

“Prospects for Genomic Selection in Cassava Breeding”

✓ located verbatim in the source · title, characters 0–51
e4

“Accuracy across generations was poorer than within-generation accuracy”

✓ located verbatim in the source · abstract, characters 976–1046

Title

Prospects for Genomic Selection in Cassava Breeding

Marnin D. Wolfe; Dunia Pino Del Carpio; Olumide Alabi; et al. The Plant Genome 2017. doi:10.3835/plantgenome2017.03.0015

Other claims from this paper (7)
Intervention effectobservedoriginal resultpositivemeasured

Claim · model-written summary

Sub-pixel level disparity accuracy improves matching accuracy compared with pixel level disparity accuracy.

Taxon
spinach, tomato, pepper, and pumpkine0
Treatment
disparity accuracy at sub-pixel levele2
Outcome
matching accuracye2
Direction
improvee2
Comparator
disparity accuracy at pixel levele2

Verbatim

e0

“spinach, tomato, pepper, and pumpkin were considered for experiment”

✓ located verbatim in the source · abstract, characters 535–602
e2

“compared with the disparity accuracy at pixel level, the disparity accuracy at sub-pixel level can remarkably improve the matching accuracy”

✓ located verbatim in the source · abstract, characters 862–1001

Title

PlantStereo: A High Quality Stereo Matching Dataset for Plant Reconstruction

Qingyu Wang; Dihua Wu; Wei Liu; et al. Agriculture 2023. doi:10.3390/agriculture13020330

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Other claims from this paper (8)
Associationobservedoriginal resultmeasured

Claim · model-written summary

Yield accuracy depended strongly on ellipse estimation accuracy.

Taxon
melonCucumis melot0
Outcome
The yield accuracy is highly dependent on the ellipse estimation accuracye0
Comparator
ellipse estimation accuracye0

Not reportedDirection

Verbatim

e0

“The yield accuracy is highly dependent on the ellipse estimation accuracy”

✓ located verbatim in the source · abstract, characters 711–784
t0

“melon yield”

✓ located verbatim in the source · title, characters 11–22

Title

Estimating melon yield for breeding processes by machine-vision processing of UAV images

A. Kalantar; A. Dashuta; Y. Edan; et al. Precision agriculture '19 2019. doi:10.3920/978-90-8686-888-9_47

Other claims from this paper (3)
Method / resourceauthor interpretationoriginal resultpositivemeasured

Claim · model-written summary

Adaptive width multiplier strategies balanced accuracy improvement per additional parameter with overall model accuracy.

Outcome
accuracy improvement per additional parametere0
Comparator
overall model accuracye0

Not reportedValue

Verbatim

e0

“balance accuracy improvement per additional parameter with overall model accuracy”

✓ located verbatim in the source · abstract, characters 1384–1465

Title

Enhancing Smart Agriculture With Lightweight Object Detection: MobileNetv3-YOLOv4 and Adaptive Width Multipliers

I-An Lin; Yuan-Wei Cheng; Trong-Yen Lee IEEE Sensors Journal 2024. doi:10.1109/jsen.2024.3478810

Other claims from this paper (9)
Method / resourceobservedoriginal resultpositivemeasured

Claim · model-written summary

Overall accuracy and classification accuracy of the models were higher than the DenseNet-121 network.

Outcome
overall accuracy and classification accuracye0
Direction
highere0
Comparator
DenseNet-121 networke0

Verbatim

t0

“Apple”

✓ located verbatim in the source · title, characters 0–5
e0

“The overall accuracy and classification accuracy were higher than the DenseNet-121 network”

✓ located verbatim in the source · abstract, characters 1308–1398

Title

Apple Diseases Detection: Based On Deep Learning

B. Sunitha Devi 2023 International Conference on New Frontiers in Communication, Automation, Management and Security (ICCAMS) 2023. doi:10.1109/iccams60113.2023.10525833

Other claims from this paper (2)
Observationauthor interpretationoriginal resultno effectmeasured

Claim · model-written summary

MODIS observation accuracy was not limiting LAI accuracy for broadleaf crops.

Taxon
broadleaf cropse4
Outcome
LAI accuracye4
Qualifier
The accuracy of MODIS observations was not limitinge4

Verbatim

e4

“The accuracy of MODIS observations was not limiting LAI accuracy for broadleaf crops”

✓ located verbatim in the source · abstract, characters 1341–1425

Title

Comparison of different MODIS data product collections over an agricultural area

Alan J. Stern; Paul C. Doraiswamy; E. Raymond Hunt Remote Sensing Letters 2014. doi:10.1080/2150704x.2013.862600

Other claims from this paper (4)
Method / resourceobservedoriginal resultpositivemeasured

Claim · model-written summary

EffResViT-SE FusionNet showed notable accuracy improvement over ResNet50 (94% accuracy).

Taxon
CoffeeCoffeat0
Outcome
accuracye3
Direction
improvementse3
Qualifier
94% accuracye3
Comparator
ResNet50e3

Verbatim

t0

“Coffee Leaf Diseases”

✓ located verbatim in the source · title, characters 88–108
e3

“Comparative analysis confirmed notable improvements over baseline models: ResNet50 (94% accuracy), EfficientNetB3 (95% accuracy), and standalone ViT (97% accuracy)”

✓ located verbatim in the source · abstract, characters 1649–1812

Title

EffResViT‐SE FusionNet: A Hybrid Deep Learning Framework for Accurate Classification of Coffee Leaf Diseases

Bhoomika Mehta; Salil Bharany; Dalia H. Elkamchouchi; et al. Food Science & Nutrition 2025. doi:10.1002/fsn3.71311

Other claims from this paper (5)
Method / resourceobservedoriginal resultpositivemeasured

Claim · model-written summary

EffResViT-SE FusionNet showed notable accuracy improvement over EfficientNetB3 (95% accuracy).

Taxon
CoffeeCoffeat0
Outcome
accuracye3
Direction
improvementse3
Qualifier
95% accuracye3
Comparator
EfficientNetB3e3

Verbatim

t0

“Coffee Leaf Diseases”

✓ located verbatim in the source · title, characters 88–108
e3

“Comparative analysis confirmed notable improvements over baseline models: ResNet50 (94% accuracy), EfficientNetB3 (95% accuracy), and standalone ViT (97% accuracy)”

✓ located verbatim in the source · abstract, characters 1649–1812

Title

EffResViT‐SE FusionNet: A Hybrid Deep Learning Framework for Accurate Classification of Coffee Leaf Diseases

Bhoomika Mehta; Salil Bharany; Dalia H. Elkamchouchi; et al. Food Science & Nutrition 2025. doi:10.1002/fsn3.71311

Other claims from this paper (5)
Method / resourceobservedoriginal resultmeasured

Claim · model-written summary

Validation accuracy was 94.25%, below training accuracy.

Outcome
validation accuracye0
Direction
lowere0
Value
94.25%e0

Verbatim

e0

“the validation accuracy was slightly lower at 94.25%”

✓ located verbatim in the source · abstract, characters 1037–1089
t0

“Cabbage Plant”

✓ located verbatim in the source · title, characters 10–23

Title

Enhancing Cabbage Plant Protection: CNN Algorithm for Insect Identification

Remi Senjaya; Purnawarman Musa; Puji Sularsih; et al. 2024 International Conference on Information Technology Research and Innovation (ICITRI) 2024. doi:10.1109/icitri62858.2024.10698927

Other claims from this paper (4)

Lexical search (BM25). All words must match; if nothing does, any word. Organism names also match every spelling of the same organism. No semantic search yet.