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“estimation accuracy”

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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 / resource Ellipse features predicted melon weight with modified R2 of 0.94.
Method / resource Yield estimation had an average error of 16% against ground truth.
Method / resource Best ellipse-fitted melons had only 4% estimation error.

Method / resourceobservedoriginal resultpositivemeasured

Claim · model-written summary

The random forest model had the highest cotton LAI estimation accuracy among the LAI estimation models tested.

Taxon: cottonGossypium hirsutumt0
Outcome: estimation accuracye0
Direction: higheste0
Comparator: among the LAI estimation modelse0

Verbatim

t0
“cotton leaf area prediction”
✓ located verbatim in the source · title, characters 59–86

e0
“The RF model had the highest estimation accuracy among the LAI estimation models”
✓ located verbatim in the source · abstract, characters 821–901

Title

The fusion of vegetation indices increases the accuracy of cotton leaf area prediction

Xianglong Fan; Pan Gao; Mengli Zhang; et al. Frontiers in Plant Science 2024. doi:10.3389/fpls.2024.1357193

Other claims from this paper (5)

Method / resource The RF model fusing seven vegetation indices had the highest estimation accuracy among multi-VI fusion models, with R2 of 0.90.
Method / resource The RF model fusing seven vegetation indices had an RMSE of 0.50 for cotton LAI estimation.
Method / resource The RF model fusing seven vegetation indices had an NRMSE of 0.14 for cotton LAI estimation.
Method / resource The RF model fusing seven vegetation indices had an MAE of 0.26 for cotton LAI estimation.
Method / resource Models fusing multiple vegetation indices had higher cotton LAI estimation accuracy than models based on single indices.

Method / resourceobservedoriginal resultpositivemeasured

Claim · model-written summary

Estimation accuracy reached 91.9%.

Taxon: appleMalus domesticat0
Outcome: estimation accuracye0
Value: 91.9%e0

Verbatim

e0
“estimation accuracy is reached 91.9%”
✓ located verbatim in the source · abstract, characters 1445–1481

t0
“Apple Florescence Canopy”
✓ located verbatim in the source · title, characters 58–82

Title

Estimating Nitrogen Content based on Hyperspectrum of the Apple Florescence Canopy

Zhu Xicun -; Jiang Yuanmao -; Zhao Gengxing -; et al. Journal of Convergence Information Technology 2013. doi:10.4156/jcit.vol8.issue10.34

Other claims from this paper (6)

Association Original and reciprocal, logarithmic, and square root reflectance had weak correlations with apple canopy nitrogen content.
Association First and second derivative reflectance strengthened correlation with apple canopy nitrogen content.
Method / resource The logarithmic reflectance variable yielded the best nitrogen estimation model.
Method / resource The best model had a measured versus estimated fitting correlation of 0.7325.
Method / resource The best model had minimum RMSE of 2.3802.
Method / resource The best model had minimum relative error of 8.1%.

Method / resourceobservedoriginal resultmeasured

Claim · model-written summary

ASD estimation accuracy exceeded HSL accuracy.

Taxon: maizeZea mayst0
Outcome: estimating accuracye0
Direction: highere0
Comparator: HSLe0

Verbatim

e0
“the estimating accuracy of ASD was higher than that of HSL.”
✓ located verbatim in the source · abstract, characters 1008–1067

t0
“Maize Photosynthesis Traits”
✓ located verbatim in the source · title, characters 14–41

Title

Estimation of Maize Photosynthesis Traits Using Hyperspectral Lidar Backscattered Intensity

Kaiyi Bi; Zheng Niu; Shunfu Xiao; et al. Remote Sensing 2021. doi:10.3390/rs13214203

Other claims from this paper (4)

Method / resource The reflectance-based method estimated J more accurately than the trait-based method.
Method / resource The reflectance-based method estimated Vcmax more accurately than the trait-based method.
Method / resource The Lambertian–Beckmann model corrected incident angle across wavelength bands with R2 of 0.74 to 0.92.
Method / resource Spatial distributions of photosynthesis traits were displayed for two maize plants.

Observationobservedoriginal resultmeasured

Claim · model-written summary

Yield estimation accuracy varied among districts.

Outcome: estimation accuracye0
Setting: district to districte0

Not reportedTaxon

Verbatim

e0
“a variation in estimation accuracy from district to district could be observed”
✓ located verbatim in the source · abstract, characters 1333–1411

Title

Rapid Rice Yield Estimation Using Integrated Remote Sensing and Meteorological Data and Machine Learning

Md Didarul Islam; Liping Di; Faisal Mueen Qamer; et al. Remote Sensing 2023. doi:10.3390/rs15092374

Other claims from this paper (7)

Method / resource NDVI alone was insufficient for accurate rice yield estimation.
Method / resource Adding meteorological variables markedly improved yield estimation accuracy.
Association Rainfall, soil moisture and evapotranspiration, along with NDVI, were strongly associated with rice yield.
Method / resource Stacked tree regression models outperformed linear regression and individual ML models in yield accuracy.
Method / resource The workflow reached 92% average yield estimation accuracy.
Method / resource The workflow had RMSE of 328.06 kg/ha.
Method / resource The workflow had MAE of 317.21 kg/ha.

Method / resourceobservedoriginal resultpositivemeasured

Claim · model-written summary

Branch segmentation and occlusion correction improved coffee yield estimation accuracy.

Taxon: coffeeCoffeat0
Outcome: yield estimation accuracye0
Direction: improvee0

Not reportedComparator

Verbatim

e0
“Branch segmentation and occlusion correction improve yield estimation accuracy”
✓ located verbatim in the source · abstract, characters 88–166

t0
“coffee yield estimation”
✓ located verbatim in the source · title, characters 17–40

Title

Field-deployable coffee yield estimation from mobile phone images using branch segmentation and occlusion correction

Romain Gautron; Mathieu Seurin; Eric Rahn; et al. Smart Agricultural Technology 2026. doi:10.1016/j.atech.2026.101901

Other claims from this paper (3)

Observation Data capture modalities affected coffee yield accuracy and ease of collection.
Method / resource At plot level under full background isolation, the model reached R 2 up to 0.96.
Method / resource At plot level under full background isolation, MAPE was as low as 10%.

Method / resourceobservedoriginal resultpositivemeasured

Claim · model-written summary

Maximal diameter estimation accuracy ranged from 16.0 to 21.5% and exceeded fruit-length estimation accuracy.

Taxon: EggplantSolanum melongenat0
Outcome: Estimation accuracy of the fruit maximal diametere0
Direction: highere0
Value: 16.0 to 21.5%e0
Comparator: fruit lengthe0

Verbatim

t0
“Eggplant”
✓ located verbatim in the source · title, characters 28–36

e0
“Estimation accuracy of the fruit maximal diameter was varying from 16.0 to 21.5% and higher than the one of the fruit length”
✓ located verbatim in the source · abstract, characters 893–1017

Title

Machine Vision Algorithm of Eggplant Recognition for Robotic Harvesting.

Shigehiko HAYASHI; Katsunobu GANNO; Yukitsugu ISHII Shokubutsu Kojo Gakkaishi 2000. doi:10.2525/jshita.12.38

Other claims from this paper (5)

Method / resource The vertical-dividing operation excluded objects 60 pixels long.
Method / resource The vertical-dividing operation handled fruit angles up to about 20 degrees.
Method / resource Eggplant fruit recognition rate ranged from 80.0 to 97.5%.
Method / resource Algorithm operating time was approximately one second.
Method / resource Fruit length and maximal diameter were estimated from binary-image geometry.

Method / resourceobservedoriginal resultpositivesignificant (as stated)measured

Claim · model-written summary

Weighted input improved yield estimation accuracy significantly.

Taxon: winter wheatTriticum aestivume1
Outcome: yield estimation accuracye0
Direction: improvese0

Not reportedComparatorValue

Verbatim

e0
“weighted time-series input significantly improves yield estimation accuracy”
✓ located verbatim in the source · abstract, characters 1980–2055

e1
“winter wheat growth”
✓ located verbatim in the source · abstract, characters 461–480

Title

Winter Wheat Yield Estimation Based on Optimal Weighted Vegetation Index and BHT-ARIMA Model

Qiuzhuo Deng; Mengxuan Wu; Haiyang Zhang; et al. Remote Sensing 2022. doi:10.3390/rs14091994

Other claims from this paper (5)

Method / resource Weighted input improved R2 from 0.325 to 0.583.
Method / resource Weighted input reduced yield-estimation RMSE from 492.990 to 323.637 kg/ha.
Method / resource Weighted input reduced MAE from 350.625 to 255.954.
Method / resource Weighted input reduced MAPE from 4.332% to 3.186%.
Method / resource BHT-ARIMA had the best yield prediction accuracy among compared models.

Method / resourceobservedoriginal resultmeasured

Claim · model-written summary

Neck morphometry enabled high accuracy age estimation.

Taxon: OliveOlea europaeat0
Outcome: estimation of high levels of accuracye0

Not reportedComparatorValue

Verbatim

e0
“analysis on its morphometry enables estimation of high levels of accuracy”
✓ located verbatim in the source · abstract, characters 987–1060

t0
“Olive”
✓ located after whitespace/quote normalisation · title, characters 23–28

Title

Determining the age of olive trees through morphometric methods.

Hairi Ismaili; Zaim Veshaj International journal of agriculture innovation and research 2014

Other claims from this paper (3)

Association Circumferential growth increased with age across plot groups.
Observation Kaninjot neck thickening averaged 6.123 mm annually.
Observation U.Bardhe neck thickening averaged 4.222 mm annually.

Method / resourceobservedoriginal resultnegativemeasured

Claim · model-written summary

Tassels reduced LAI estimation accuracy.

Taxon: maizeZea mayse1
Outcome: accuracy of LAI estimatione0
Direction: reducede0

Not reportedComparatorValue

Verbatim

e0
“Tassels reduced the accuracy of LAI estimation”
✓ located verbatim in the source · abstract, characters 1403–1449

e1
“maize tasseling”
✓ located verbatim in the source · abstract, characters 722–737

Title

Estimating leaf area index using unmanned aerial vehicle data: shallow vs. deep machine learning algorithms

Shuaibing Liu; Xiuliang Jin; Chenwei Nie; et al. PLANT PHYSIOLOGY 2021. doi:10.1093/plphys/kiab322

Other claims from this paper (4)

Method / resource Multimodal UAV data accurately estimated maize LAI.
Method / resource The DNN gave the best single growth period LAI estimate, R2 0.89 and rRMSE 12.92%.
Method / resource PLSR gave the best whole growth period LAI estimate, R2 0.70 and rRMSE 12.78%.
Method / resource Soil background improved LAI estimation accuracy by providing image features.

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“estimation accuracy”

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