By organism › Green plants › Ranunculaceae › Consolida regalis

Observationobservedoriginal resultmeasured

Claim · model-written summary

Lebas treated candelabrum as a subtype of D. consolida rather than accepting it at species rank.

Taxon: D. consolida L.Consolida regalise0
Outcome: not accepting this subtype at species ranke0

Verbatim

e0
“He indicated this to be a subtype of D. consolida L. (“le sous-type candelabrum”), comprising a number of dwarf races that he grouped under the name Delphinium consolida [unranked] candelabrum, thus not accepting this subtype at species rank”
✓ located verbatim in the source · abstract, characters 2658–2899

Title

(3064) Proposal to conserve the name Delphinium candelabrum Ostenf. against D. candelabrum (B.K. Bliss & S.B. Bliss) Neubert (Ranunculaceae)

Purushottam Kumar Deroliya; Sriman Lal Meena Taxon 2025. doi:10.1002/tax.13305

Other claims from this paper (6)

Method / resourceobservedoriginal resultpositivemeasured

Claim · model-written summary

Yolov5-Large gave the best precision (82.7%) in detecting and classifying weed growth stages.

Taxon: Consolida RegalisConsolida regalise0
Outcome: precisione1
Direction: beste1
Value: 82.7%e1

Verbatim

e0
“In this paper, UAV was used for collecting a dataset, which consists of four weed (Consolida Regalis) growth stages”
✓ located verbatim in the source · abstract, characters 514–629

e1
“The results report that the best precision (82.7%) is generated by the Yolov5-Large model in detecting and classifying the weed’s growth stages”
✓ located verbatim in the source · abstract, characters 828–971

Title

Real Time Deep Learning Algorithm for Counting Weed’s Growth Stages

Abeer M. Almalky; Khaled R. Ahmed 2023 IEEE 15th International Symposium on Autonomous Decentralized System (ISADS) 2023. doi:10.1109/isads56919.2023.10092053

Other claims from this paper (2)

Method / resource Yolov5-sma11 gave the best recall (79.4%) for weed growth stages.
Method / resource Yolov5-sma11 counted weed instances per growth stage at 0.033 millisecond per frame in real time.

Method / resourceobservedoriginal resultpositivemeasured

Claim · model-written summary

Yolov5-sma11 gave the best recall (79.4%) for weed growth stages.

Taxon: Consolida RegalisConsolida regalise0
Outcome: recalle2
Direction: beste2
Value: 79.4%e2

Verbatim

e0
“In this paper, UAV was used for collecting a dataset, which consists of four weed (Consolida Regalis) growth stages”
✓ located verbatim in the source · abstract, characters 514–629

e2
“According to the best performance in terms of recall, Yolov5-sma11 model has the best recall of 79.4%”
✓ located verbatim in the source · abstract, characters 973–1074

Title

Real Time Deep Learning Algorithm for Counting Weed’s Growth Stages

Abeer M. Almalky; Khaled R. Ahmed 2023 IEEE 15th International Symposium on Autonomous Decentralized System (ISADS) 2023. doi:10.1109/isads56919.2023.10092053

Other claims from this paper (2)

Method / resource Yolov5-Large gave the best precision (82.7%) in detecting and classifying weed growth stages.
Method / resource Yolov5-sma11 counted weed instances per growth stage at 0.033 millisecond per frame in real time.

Method / resourceobservedoriginal resultmeasured

Claim · model-written summary

Yolov5-sma11 counted weed instances per growth stage at 0.033 millisecond per frame in real time.

Taxon: Consolida RegalisConsolida regalise0
Outcome: counting timee3
Value: 0.033 millisecond per framee3

Verbatim

e0
“In this paper, UAV was used for collecting a dataset, which consists of four weed (Consolida Regalis) growth stages”
✓ located verbatim in the source · abstract, characters 514–629

e3
“For counting the instances of weeds per the four growth stages in real-time, Yolov5-sma11 model showes counting time of 0.033 millisecond per frame”
✓ located verbatim in the source · abstract, characters 1076–1223

Title

Real Time Deep Learning Algorithm for Counting Weed’s Growth Stages

Abeer M. Almalky; Khaled R. Ahmed 2023 IEEE 15th International Symposium on Autonomous Decentralized System (ISADS) 2023. doi:10.1109/isads56919.2023.10092053

Other claims from this paper (2)

Method / resource Yolov5-Large gave the best precision (82.7%) in detecting and classifying weed growth stages.
Method / resource Yolov5-sma11 gave the best recall (79.4%) for weed growth stages.

Method / resourceobservedoriginal resultpositivemeasured

Claim · model-written summary

YOLOv5-small detected and classified weed stages in real time with recall 0.794.

Taxon: Consolida regalisConsolida regalist0
Material: Yolov5-smalle0
Outcome: recalle0
Direction: higheste0
Value: 0.794e0

Verbatim

e0
“Yolov5-small model succeeds in detecting weeds and classifying weed growth stages in real time with the highest recall of 0.794”
✓ located verbatim in the source · abstract, characters 1183–1310

t0
“Consolida regalis Weeds”
✓ located verbatim in the source · title, characters 65–88

Title

Deep Learning for Detecting and Classifying the Growth Stages of Consolida regalis Weeds on Fields

Abeer M. Almalky; Khaled R. Ahmed Agronomy 2023. doi:10.3390/agronomy13030934

Other claims from this paper (5)

Method / resource RetinaNet with ResNet-101-FPN attained testing average precision 87.457.
Method / resource YOLOv5-large had the highest precision for almost all stages.
Method / resource YOLOv5-large missed some objects in tested images.
Method / resource RetinaNet with ResNet-101-FPN showed high precision overall.
Method / resource YOLOv5-small had the shortest real-time inference time.

Method / resourceobservedoriginal resultpositivemeasured

Claim · model-written summary

RetinaNet with ResNet-101-FPN attained testing average precision 87.457.

Taxon: Consolida regalisConsolida regalist0
Outcome: average precisione0
Value: 87.457e0
Qualifier: testing phasee0

Verbatim

e0
“RetinaNet with ResNet-101-FPN backbone shows accurate results in the testing phase (average precision of 87.457)”
✓ located verbatim in the source · abstract, characters 1312–1424

t0
“Consolida regalis Weeds”
✓ located verbatim in the source · title, characters 65–88

Title

Deep Learning for Detecting and Classifying the Growth Stages of Consolida regalis Weeds on Fields

Abeer M. Almalky; Khaled R. Ahmed Agronomy 2023. doi:10.3390/agronomy13030934

Other claims from this paper (5)

Method / resource YOLOv5-small detected and classified weed stages in real time with recall 0.794.
Method / resource YOLOv5-large had the highest precision for almost all stages.
Method / resource YOLOv5-large missed some objects in tested images.
Method / resource RetinaNet with ResNet-101-FPN showed high precision overall.
Method / resource YOLOv5-small had the shortest real-time inference time.

Method / resourceobservedoriginal resultpositivemeasured

Claim · model-written summary

YOLOv5-large had the highest precision for almost all stages.

Taxon: Consolida regalisConsolida regalist0
Outcome: precisione0
Direction: higheste0

Not reportedComparatorValue

Verbatim

e0
“Yolov5-large showed the highest precision in classifying almost all weed growth stages”
✓ located verbatim in the source · abstract, characters 1435–1521

t0
“Consolida regalis Weeds”
✓ located verbatim in the source · title, characters 65–88

Title

Deep Learning for Detecting and Classifying the Growth Stages of Consolida regalis Weeds on Fields

Abeer M. Almalky; Khaled R. Ahmed Agronomy 2023. doi:10.3390/agronomy13030934

Other claims from this paper (5)

Method / resource YOLOv5-small detected and classified weed stages in real time with recall 0.794.
Method / resource RetinaNet with ResNet-101-FPN attained testing average precision 87.457.
Method / resource YOLOv5-large missed some objects in tested images.
Method / resource RetinaNet with ResNet-101-FPN showed high precision overall.
Method / resource YOLOv5-small had the shortest real-time inference time.

Method / resourceobservedoriginal resultnegativemeasured

Claim · model-written summary

YOLOv5-large missed some objects in tested images.

Taxon: Consolida regalisConsolida regalist0
Outcome: detect all objectse0

Not reportedComparatorDirectionValue

Verbatim

e0
“Yolov5-large could not detect all objects in tested images”
✓ located verbatim in the source · abstract, characters 1523–1581

t0
“Consolida regalis Weeds”
✓ located verbatim in the source · title, characters 65–88

Title

Deep Learning for Detecting and Classifying the Growth Stages of Consolida regalis Weeds on Fields

Abeer M. Almalky; Khaled R. Ahmed Agronomy 2023. doi:10.3390/agronomy13030934

Other claims from this paper (5)

Method / resource YOLOv5-small detected and classified weed stages in real time with recall 0.794.
Method / resource RetinaNet with ResNet-101-FPN attained testing average precision 87.457.
Method / resource YOLOv5-large had the highest precision for almost all stages.
Method / resource RetinaNet with ResNet-101-FPN showed high precision overall.
Method / resource YOLOv5-small had the shortest real-time inference time.

Method / resourceobservedoriginal resultpositivemeasured

Claim · model-written summary

RetinaNet with ResNet-101-FPN showed high precision overall.

Taxon: Consolida regalisConsolida regalist0
Outcome: precisione0
Direction: highe0

Not reportedComparatorValue

Verbatim

e0
“RetinaNet with ResNet-101-FPN backbones shows accurate and high precision”
✓ located verbatim in the source · abstract, characters 1592–1665

t0
“Consolida regalis Weeds”
✓ located verbatim in the source · title, characters 65–88

Title

Deep Learning for Detecting and Classifying the Growth Stages of Consolida regalis Weeds on Fields

Abeer M. Almalky; Khaled R. Ahmed Agronomy 2023. doi:10.3390/agronomy13030934

Other claims from this paper (5)

Method / resource YOLOv5-small detected and classified weed stages in real time with recall 0.794.
Method / resource RetinaNet with ResNet-101-FPN attained testing average precision 87.457.
Method / resource YOLOv5-large had the highest precision for almost all stages.
Method / resource YOLOv5-large missed some objects in tested images.
Method / resource YOLOv5-small had the shortest real-time inference time.

Method / resourceobservedoriginal resultpositivemeasured

Claim · model-written summary

YOLOv5-small had the shortest real-time inference time.

Taxon: Consolida regalisConsolida regalist0
Outcome: inference timee0
Direction: shorteste0

Not reportedComparatorValue

Verbatim

e0
“Yolov5-small shows the shortest inference time in real time for detecting a weed and classifying its growth stages”
✓ located verbatim in the source · abstract, characters 1675–1789

t0
“Consolida regalis Weeds”
✓ located verbatim in the source · title, characters 65–88

Title

Deep Learning for Detecting and Classifying the Growth Stages of Consolida regalis Weeds on Fields

Abeer M. Almalky; Khaled R. Ahmed Agronomy 2023. doi:10.3390/agronomy13030934

Other claims from this paper (5)

Method / resource YOLOv5-small detected and classified weed stages in real time with recall 0.794.
Method / resource RetinaNet with ResNet-101-FPN attained testing average precision 87.457.
Method / resource YOLOv5-large had the highest precision for almost all stages.
Method / resource YOLOv5-large missed some objects in tested images.
Method / resource RetinaNet with ResNet-101-FPN showed high precision overall.

Consolida regalis 39 claims

39 claims