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“processing time”

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Method / resourceobservedoriginal resultmeasured

Claim · model-written summary

The computer procedure increased processing time.

Taxon: Plantt0
Outcome: time requirements for processinge0
Direction: increasede0

Not reportedComparatorValue

Verbatim

e0
“time requirements for processing were increased”
✓ located verbatim in the source · abstract, characters 834–881

t0
“Plant”
✓ located verbatim in the source · title, characters 31–36

Title

Image Processing Technique for Plant Canopy Cover Evaluation

D. L. Thomas; F. J. K. daSilva; W. A. Cromer Transactions of the ASAE 1988. doi:10.13031/2013.30726

Other claims from this paper (6)

Method / resource The computer procedure produced acceptable canopy cover estimates against leaf area measurements.
Method / resource Visual inspection estimates were larger than leaf area measurement results.
Method / resource The computer procedure worked well in field situations.
Method / resource The computer method processed 43 of 50 slides.
Method / resource The computer procedure reduced human error.
Observation System cost limits wide use unless computer and camera have other applications.

Method / resourceobservedoriginal resultmeasured

Claim · model-written summary

The algorithm was precise and reduced processing time.

Taxon: TobaccoNicotiana tabacumt0
Outcome: processing timee0
Direction: decreasede0

Not reportedComparatorValue

Verbatim

e0
“The algorithm is precious and the processing time is decreased.”
✓ located verbatim in the source · abstract, characters 586–649

t0
“Tobacco”
✓ located verbatim in the source · title, characters 33–40

Title

A Classification Algorithm for a Tobacco Leaf Structure with Image Processing

Shang Qing-qing Journal of Donghua University 2007

Other claims from this paper (4)

Method / resource The algorithm adapted to tobacco images of varying quality.
Method / resource The algorithm ran in real time.
Method / resource The algorithm worked efficiently and stably in an automated classification system.
Method / resource Practical results supported feasibility and satisfactory performance.

Method / resourceobservedoriginal resultpositivemeasured

Claim · model-written summary

Processing time decreased by 10 ms.

Outcome: processing timee0
Direction: reducede0
Value: 10 mse0

Not reportedTaxon

Verbatim

e0
“processing time was reduced by 10 ms”
✓ located verbatim in the source · abstract, characters 1080–1116

Title

YOLOv5s-T: A Lightweight Small Object Detection Method for Wheat Spikelet Counting

Lei Shi; Jiayue Sun; Yuanbo Dang; et al. Agriculture 2023. doi:10.3390/agriculture13040872

Other claims from this paper (10)

Method / resource Predicted and true flowering stage spikelet counts had R2 of 0.97.
Method / resource Predicted and true filling stage spikelet counts had R2 of 0.85.
Method / resource Predicted and true mature stage spikelet counts had R2 of 0.78.
Method / resource Across all three stages, R2 was 0.87.
Method / resource Across all stages, RMSE was 0.70.
Method / resource YOLOv5s-T spikelet detection and counting performance did not decline versus original YOLOv5s.
Method / resource Model size decreased 36.8% to 9.1 M.
Method / resource Training GPU memory use decreased by 0.82 GB.
Method / resource Inference time decreased by 2.3 ms.
Method / resource Calculation amount also decreased.

Method / resourceobservedoriginal resultpositivemeasured

Claim · model-written summary

LLC averaged 1.66 milliseconds processing time.

Outcome: processing timee0
Value: 1.66 millisecondse0

Not reportedTaxon

Verbatim

e0
“average 1.66 milliseconds of processing time”
✓ located verbatim in the source · abstract, characters 663–707

Title

Leaf lesion classification (LLC) algorithm based on artificial bee colony (ABC)

Faudziah Ahmad; Ku Ruhana Ku‐Mahamud; Mohd Shamrie Sainin; et al. Universiti Utara Malaysia Institutional Repository (Universiti Utara Malaysia) 2015

Other claims from this paper (2)

Method / resource LLC achieved average accuracy of 96.83% for leaf lesion classification.
Method / resource LLC performed better than Otsu, Canny, Roberts and Sobel.

Method / resourceobservedoriginal resultmeasured

Claim · model-written summary

EfficientDet D0 required more processing time.

Taxon: PlantsViridiplantaet0
Outcome: processing timee0
Direction: moree0
Comparator: EfficientDet D0 modele0

Verbatim

e0
“EfficientDet D0 model required more processing time”
✓ located verbatim in the source · abstract, characters 864–915

t0
“Plants”
✓ located verbatim in the source · title, characters 56–62

Title

Using Deep Learning Model to Identify Iron Chlorosis in Plants

Munir Majdalawieh; Shafaq Khan; Md. T. Islam IEEE Access 2023. doi:10.1109/access.2023.3273607

Other claims from this paper (4)

Method / resource SSD MobileNet v2 classification accuracy ranged from 93% to 98%.
Method / resource SSD MobileNet v2 took less processing time but had lower accuracy.
Method / resource EfficientDet D0 accuracy ranged from 87% to 98.4%.
Recommendation The authors conclude that both models are useful for real-time classification and EfficientDet D0 may perform better.

Method / resourceobservedoriginal resultpositivemeasured

Claim · model-written summary

Algorithm processing time met real-time tea picking requirements.

Taxon: Teat0
Outcome: processing timee0
Comparator: real-time requirementse0

Verbatim

e0
“the processing time of the algorithm met the real-time requirements for effective tea-picking operations”
✓ located verbatim in the source · abstract, characters 1269–1373

t0
“Tea”
✓ located verbatim in the source · title, characters 46–49

Title

Development and Experiment of Adaptive Oolong Tea Harvesting Robot Based on Visual Localization

Ruidong Yu; Yinhui Xie; Qiming Li; et al. Agriculture 2024. doi:10.3390/agriculture14122213

Other claims from this paper (7)

Method / resource Under ideal conditions the algorithm reached 43.10% inliers.
Method / resource Under ideal conditions model fit reached R2 0.9787 and outperformed RANSAC variants.
Method / resource Under challenging field conditions the algorithm maintained 47.50% inliers.
Method / resource Under challenging field conditions model fit was R2 0.9598.
Method / resource Intact tea rate improved from 79.34% in the first harvest to 81.57% in the second.
Method / resource Usable tea rate stayed around 85%.
Method / resource Robot harvesting efficiency was 260.14 kg/h, above handheld and riding-type pickers.

Method / resourceobservedoriginal resultpositivemeasured

Claim · model-written summary

Processing can be controlled by shaft speed and processing time.

Taxon: Prairie Grassest0
Outcome: Processing can be controllede0

Not reportedComparatorValue

Verbatim

e0
“Processing can be controlled by varying shaft rotation speed and processing time”
✓ located verbatim in the source · abstract, characters 531–611

t0
“Prairie Grasses”
✓ located verbatim in the source · title, characters 77–92

Title

Technical Note: A Rotary Seed Processor for Removing Pubescence from Seed of Prairie Grasses

K. P. Vogel; R. A. Masters; P. J. Callahan; et al. Journal of Range Management 1998. doi:10.2307/4003371

Other claims from this paper (9)

Method / resource The rotary processor efficiently processes small breeder or experimental seed lots for planting.
Method / resource The processor has been used successfully on big bluestem seed.
Method / resource The processor has been used successfully on indiangrass seed.
Method / resource The processor has been used successfully on little bluestem seed.
Method / resource The processor has been used successfully on prairie sandreed seed.
Method / resource The processor has been used successfully on blue grama seed.
Intervention effect Removing appendages and pubescence reduces seed bulk.
Intervention effect Removing appendages and pubescence improves seed density and flow ability.
Intervention effect Processing improves seed germination per unit seed weight.

Method / resourceobservedoriginal resultpositivesignificant (as stated)measured

Claim · model-written summary

Processing time decreased significantly.

Taxon: plantt0
Outcome: processing timee0
Direction: decreasee0

Not reportedComparatorValue

Verbatim

e0
“significant decrease in processing time”
✓ located verbatim in the source · abstract, characters 1110–1149

t0
“plant leaf diseases”
✓ located verbatim in the source · title, characters 84–103

Title

Development of a cutting-edge ensemble pipeline for rapid and accurate diagnosis of plant leaf diseases

S.M. Nuruzzaman Nobel; Maharin Afroj; Md Mohsin Kabir; et al. Artificial Intelligence in Agriculture 2024. doi:10.1016/j.aiia.2024.10.005

Other claims from this paper (5)

Method / resource Different magnifications improved diagnostic agreement and accuracy.
Method / resource Test accuracy on Plantvillage was 99.65 %.
Method / resource Test accuracy on Tomato leaf was 98.96 %.
Method / resource Test accuracy on Appleleaf9 was 98.11 %.
Method / resource XAI visually identified critical leaf areas for disease identification.

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.

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