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“processing time”

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Method / resourceobservedoriginal resultpositivemeasured

Claim · model-written summary

The best processing parameters for Arctium lappa were 300 ℃ drying temperature and 4-5 minutes processing time.

Taxon: Arctium LappaArctium lappae0
Outcome: best processing parameterse0
Value: 300 ℃e0
Qualifier: 4-5 minutes of processing timee0

Verbatim

e0
“the best processing parameters for Arctium Lappa consisted of 300 ℃ of the drying tempera-ture and 4-5 minutes of processing time”
✓ located verbatim in the source · abstract, characters 332–461

Title

Processing for Arctium Lappa with orthogonal test

Jianxin Wang Zhongchengyao 2010

Other claims from this paper (1)

Recommendation The authors concluded that the processing method was reasonable, stable and reliable for commercial production.

Method / resourceobservedoriginal resultmeasured

Claim · model-written summary

The pH differential method had the shortest processing time.

Outcome: Processing time (incubation time)e0
Direction: loweste0
Comparator: pH differential methode0

Not reportedTaxon

Verbatim

e0
“Processing time (incubation time) was lowest in the pH differential method”
✓ located verbatim in the source · abstract, characters 1221–1295

t0
“Strawberries”
✓ located verbatim in the source · title, characters 81–93

Title

Anthocyanin Extraction Method and Sample Preparation Affect Anthocyanin Yield of Strawberries

Toktam Taghavi; Hiral Patel; Reza Rafie Natural Product Communications 2022. doi:10.1177/1934578x221099970

Other claims from this paper (7)

Intervention effect Sample preparation did not affect anthocyanin yield.
Association Extraction method affected anthocyanin yield.
Intervention effect The two chloroform methods gave the highest anthocyanin yield.
Intervention effect One methanol:water:HCl method gave intermediate anthocyanin yield.
Intervention effect The pH differential method gave one of the lowest anthocyanin yields.
Intervention effect The methanol:water:HCl (80:20:1) method gave one of the lowest anthocyanin yields.
Method / resource Haze from the pH differential method may interfere with anthocyanin spectrophotometry.

Observationobservedoriginal resultmeasured

Claim · model-written summary

Hand picking working time for processing tomato reached 142.4 hrs/10a.

Taxon: Processing Tomatot0
Outcome: working timee0
Value: 142.4hrs/10ae0

Verbatim

t0
“Processing Tomato”
✓ located verbatim in the source · title, characters 57–74

e0
“working time has reached 142.4hrs/10a”
✓ located verbatim in the source · abstract, characters 1243–1280

Title

Studies on the Development of a Mechanical Harvester for Processing Tomato

Hiroshi ARIMA Japanese Journal of Farm Work Research 1979. doi:10.4035/jsfwr.1979.36_1

Other claims from this paper (4)

Observation Hand picking accounts for 52.2% of the total working time for processing tomato culture.
Intervention effect Simplifying the hand picking motion does not increase picking efficiency significantly.
Intervention effect The once-over harvest produced a yield of about 60-70% of the traditional hand picking method.
Intervention effect The combination of hand selection and once-over method produced a yield of about 75-85% of the traditional hand picking method.

Method / resourceobservedoriginal resultpositivemeasured

Claim · model-written summary

Mean image processing time was 0.17s.

Taxon: RamieBoehmeria niveat0
Outcome: average processing timee0
Value: 0.17se0

Verbatim

e0
“the average processing time is 0.17s”
✓ located verbatim in the source · abstract, characters 942–978

t0
“Ramie”
✓ located verbatim in the source · title, characters 38–43

Title

Navigation Line Extraction Method for Ramie Combine Harvester Based on U-Net

Zhengwei Chen; Runxin Niu; Jie Wang; et al. 2021 6th Asia-Pacific Conference on Intelligent Robot Systems (ACIRS) 2021. doi:10.1109/acirs52449.2021.9519315

Other claims from this paper (4)

Method / resource The algorithm accurately segmented ramie area in a 300-image test set.
Method / resource The algorithm obtained harvester navigation lines in the 300-image test set.
Method / resource Mean angle error for extracted navigation lines was 1.16°.
Method / resource The authors judged the algorithm able to provide reliable real-time navigation for an automatic ramie harvester.

Observationobservedoriginal resultpositivemeasured

Claim · model-written summary

The segmentation approach has low inference time for near real-time processing.

Taxon: plant rootse1
Outcome: inference timee5
Qualifier: near real-time processinge5

Verbatim

e1
“generate realistic and high resolution images of plant roots”
✓ located verbatim in the source · abstract, characters 207–267

e5
“low inference time for near real-time processing”
✓ located verbatim in the source · abstract, characters 1083–1131

Title

Plant Root Phenotyping Using Deep Conditional GANs and Binary Semantic Segmentation

Vaishnavi Thesma; Javad Mohammadpour Velni Sensors 2022. doi:10.3390/s23010309

Other claims from this paper (5)

Method / resource The segmentation model reaches a testing accuracy of over 99% on root images.
Method / resource The segmentation model has a cross entropy error of less than 2%.
Method / resource The segmentation model reaches a Dice score near 0.80 on root images.
Observation The cGAN produces realistic and high resolution root images.
Observation The cGAN reduces pixel-wise class imbalance in the root dataset.

Method / resourceauthor interpretationoriginal resultmeasured

Claim · model-written summary

The described confocal data processing method reduced processing time.

Taxon: pearPyrus communise1
Organ / stage: pollen and pistile1
Outcome: time needed for processing data in LCSMe0
Direction: decreasede0

Not reportedValue

Verbatim

e0
“the time needed for processing data in LCSM will be largely decreased”
✓ located verbatim in the source · abstract, characters 210–279

e1
“with pollen and pistil of pear”
✓ located verbatim in the source · abstract, characters 164–194

Title

Technolog of data collection and analysis from Laser Confocal Scanning Microscope

Shaoling Zhang Guoshu xuebao 2005

Other claims from this paper (2)

Method / resource The method enabled most data work on a common computer.
Method / resource The authors expected reduced processing expenses.

Method / resourceobservedoriginal resultpositivemeasured

Claim · model-written summary

YOLOv11 required less processing time than YOLOv10.

Taxon: Cropt0
Outcome: processing timee0
Direction: lesse0

Not reportedComparatorValue

Verbatim

t0
“Crop”
✓ located verbatim in the source · title, characters 29–33

e0
“YOLOv11 was more efficient, consuming less computational resources and processing time”
✓ located verbatim in the source · abstract, characters 1384–1470

Title

Deep Learning Approaches for Crop Disease Detection Using Yolov11 and Yolov10

Akansh Vishwakarma; Murugan A 2025 International Conference on Computing Technologies & Data Communication (ICCTDC) 2025. doi:10.1109/icctdc64446.2025.11158725

Other claims from this paper (4)

Method / resource YOLOv11 had higher crop disease detection mAP@ 0.5 than YOLOv10, 0.986 versus 0.985.
Method / resource YOLOv11 had higher precision than YOLOv10, 0.970 versus 0.967.
Method / resource YOLOv11 had higher recall than YOLOv10, 0.982 versus 0.957.
Method / resource YOLOv11 consumed fewer computational resources than YOLOv10.

Observationobservedoriginal resultmeasured

Claim · model-written summary

Hand picking accounts for 52.2% of the total working time for processing tomato culture.

Taxon: Processing Tomatot0
Outcome: total working timee0
Value: 52.2%e0

Verbatim

t0
“Processing Tomato”
✓ located verbatim in the source · title, characters 57–74

e0
“occupies 52.2% of total working time for processing tomato culture”
✓ located verbatim in the source · abstract, characters 1285–1351

Title

Studies on the Development of a Mechanical Harvester for Processing Tomato

Hiroshi ARIMA Japanese Journal of Farm Work Research 1979. doi:10.4035/jsfwr.1979.36_1

Other claims from this paper (4)

Observation Hand picking working time for processing tomato reached 142.4 hrs/10a.
Intervention effect Simplifying the hand picking motion does not increase picking efficiency significantly.
Intervention effect The once-over harvest produced a yield of about 60-70% of the traditional hand picking method.
Intervention effect The combination of hand selection and once-over method produced a yield of about 75-85% of the traditional hand picking method.

Method / resourceobservedoriginal resultpositivesignificant (as stated)measured

Claim · model-written summary

Image processing reduced processing time and significantly improved crop identification.

Taxon: RiceOryza sativat0
Outcome: time of action and enhanced the crop identificatione0
Direction: condensede0

Not reportedComparatorValue

Verbatim

e0
“The performance of image processing condensed the time of action and enhanced the crop identification significantly”
✓ located verbatim in the source · abstract, characters 1238–1353

t0
“Rice Granules”
✓ located verbatim in the source · title, characters 12–25

Title

Analysis of Rice Granules using Image Processing and Neural Network Pattern Recognition Tool

Abirami. S; Neelamegam. P; Kala. H. International Journal of Computer Applications 2014. doi:10.5120/16806-6530

Other claims from this paper (1)

Method / resource Digital imaging extracted rice granule features without contact.

Method / resourceobservedoriginal resultmeasured

Claim · model-written summary

FTIR distinguished Forsythis samples by processing time.

Taxon: Forsythis suspense(Thunb.)Vahle0
Outcome: different processing timee0
Comparator: different processing timee0

Verbatim

e0
“Forsythis suspense(Thunb.)Vahl can be distinguished by different processing methods and different processing time,according to the number,the shape and the relative intensity of the absorption peaks”
✓ located verbatim in the source · abstract, characters 669–867

Title

Identify Different Processing Methods of Forsythis suspense(Thunb.)Vahl by FTIR Spectrometry

Bao Li Chinese Journal of Spectroscopy Laboratory 2011

Other claims from this paper (3)

Observation Forsythis suspense samples had 12 identical absorption peaks.
Method / resource FTIR distinguished Forsythis samples by processing method.
Method / resource The authors conclude FTIR fingerprints can distinguish processing methods and times.

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