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Pasture Conditions at the Initiation of Grazing to Optimize Forage Productivity: A Progress Report 20

Show all 20 →

Associationobservedoriginal resultmeasured

Claim · model-written summary

cumulative SH increased with soil P

Outcome: SHe0
Direction: increasede0
Value: r2=.5049e0
Qualifier: soil Pe0
Setting: southern Iowae1

Not reportedTaxon

Verbatim

e0
“In linear regressions, cumulative and seasonal SH increased with greater soil P (r2=.5049 and .5417), soil K (r2=.4675 and .4397), and initial forage mass (r2=.1984 and .2801).”
✓ located verbatim in the source · abstract, characters 1154–1330

e1
“southern Iowa”
✓ located verbatim in the source · abstract, characters 273–286

Title

Pasture Conditions at the Initiation of Grazing to Optimize Forage Productivity: A Progress Report

Sonja M. Kremer; James R. Russell; Daryl R. Strohbehn; et al. Iowa State University Digital Repository (Iowa State University) 2000

Other claims from this paper (19)

Association seasonal SH increased with soil P
Association cumulative SH increased with soil K
Association seasonal SH increased with soil K
Association cumulative SH increased with initial forage mass
Association seasonal SH increased with initial forage mass
Association Seasonal SH increased with earlier grazing dates.
Association Seasonal SH increased with fewer degree-days.
Association Cumulative FA increased with earlier initial grazing dates.
Association seasonal FA increased with earlier initial grazing dates.
Association Cumulative FA increased with lower soil temperatures.
Association seasonal FA increased with lower soil temperatures.
Association Cumulative FA increased with greater soil P.
Association seasonal FA increased with greater soil P.
Association Cumulative FA increased with greater soil K.
Association cumulative SH correlated with soil P in quadratic regressions.
Association seasonal SH correlated with soil P in quadratic regressions.
Association cumulative SH correlated with soil K in quadratic regressions.
Association seasonal SH correlated with soil K in quadratic regressions.
Association Cumulative and seasonal FA were correlated with degree days and initial grazing date in quadratic regressions.

Associationobservedoriginal resultmeasured

Claim · model-written summary

cumulative SH increased with soil K

Outcome: SHe0
Direction: increasede0
Value: r2=.4675e0
Qualifier: soil Ke0
Setting: southern Iowae1

Not reportedTaxon

Verbatim

e0
“In linear regressions, cumulative and seasonal SH increased with greater soil P (r2=.5049 and .5417), soil K (r2=.4675 and .4397), and initial forage mass (r2=.1984 and .2801).”
✓ located verbatim in the source · abstract, characters 1154–1330

e1
“southern Iowa”
✓ located verbatim in the source · abstract, characters 273–286

Title

Pasture Conditions at the Initiation of Grazing to Optimize Forage Productivity: A Progress Report

Sonja M. Kremer; James R. Russell; Daryl R. Strohbehn; et al. Iowa State University Digital Repository (Iowa State University) 2000

Other claims from this paper (19)

Association cumulative SH increased with soil P
Association seasonal SH increased with soil P
Association seasonal SH increased with soil K
Association cumulative SH increased with initial forage mass
Association seasonal SH increased with initial forage mass
Association Seasonal SH increased with earlier grazing dates.
Association Seasonal SH increased with fewer degree-days.
Association Cumulative FA increased with earlier initial grazing dates.
Association seasonal FA increased with earlier initial grazing dates.
Association Cumulative FA increased with lower soil temperatures.
Association seasonal FA increased with lower soil temperatures.
Association Cumulative FA increased with greater soil P.
Association seasonal FA increased with greater soil P.
Association Cumulative FA increased with greater soil K.
Association cumulative SH correlated with soil P in quadratic regressions.
Association seasonal SH correlated with soil P in quadratic regressions.
Association cumulative SH correlated with soil K in quadratic regressions.
Association seasonal SH correlated with soil K in quadratic regressions.
Association Cumulative and seasonal FA were correlated with degree days and initial grazing date in quadratic regressions.

Associationobservedoriginal resultmeasured

Claim · model-written summary

Cumulative FA increased with lower soil temperatures.

Outcome: FAe0
Direction: increasede0
Value: r2=.2617e0
Qualifier: lower soil temperaturese0
Setting: southern Iowae1

Not reportedTaxon

Verbatim

e0
“Cumulative and seasonal FA increased with earlier initial grazing dates (r2=.2106 and .3744), lower soil temperatures (r2=.2617 and.2874), and greater soil P (r2=.3489 and .2598).”
✓ located verbatim in the source · abstract, characters 1444–1623

e1
“southern Iowa”
✓ located verbatim in the source · abstract, characters 273–286

Title

Pasture Conditions at the Initiation of Grazing to Optimize Forage Productivity: A Progress Report

Sonja M. Kremer; James R. Russell; Daryl R. Strohbehn; et al. Iowa State University Digital Repository (Iowa State University) 2000

Other claims from this paper (19)

Association cumulative SH increased with soil P
Association seasonal SH increased with soil P
Association cumulative SH increased with soil K
Association seasonal SH increased with soil K
Association cumulative SH increased with initial forage mass
Association seasonal SH increased with initial forage mass
Association Seasonal SH increased with earlier grazing dates.
Association Seasonal SH increased with fewer degree-days.
Association Cumulative FA increased with earlier initial grazing dates.
Association seasonal FA increased with earlier initial grazing dates.
Association seasonal FA increased with lower soil temperatures.
Association Cumulative FA increased with greater soil P.
Association seasonal FA increased with greater soil P.
Association Cumulative FA increased with greater soil K.
Association cumulative SH correlated with soil P in quadratic regressions.
Association seasonal SH correlated with soil P in quadratic regressions.
Association cumulative SH correlated with soil K in quadratic regressions.
Association seasonal SH correlated with soil K in quadratic regressions.
Association Cumulative and seasonal FA were correlated with degree days and initial grazing date in quadratic regressions.

Effects of Early Fruiting Branch Removal on Growth,Development and Senescence Processes in Transgenic Bt Cotton(Gossypium hirsutum L.) 19

Show all 19 →

Intervention effectobservedoriginal resultsignificant (as stated)measured

Claim · model-written summary

Removing two or three basal fruiting branches (R2 and R3) significantly lowered the total number of fruiting branches relative to CK.

Taxon: cottonGossypium hirsutume0
Treatment: R2 and R3e2
Outcome: total number of fruiting branchese2
Direction: lowere2
Comparator: CKe2

Verbatim

e0
“Bt-transgenic cotton plants with two different sowing dates were treated normally(CK),or by removing one(R1),two(R2) or three basal fruiting branches(R3)”
✓ located verbatim in the source · abstract, characters 0–153

e2
“there was no difference between R1 and CK in the total number of fruiting branches,but R2 and R3 were significantly lower than CK”
✓ located verbatim in the source · abstract, characters 389–518

Title

Effects of Early Fruiting Branch Removal on Growth,Development and Senescence Processes in Transgenic Bt Cotton(Gossypium hirsutum L.)

Lichao Zhai; Zhang Yongjiang; Liantao Liu; et al. 2012

Other claims from this paper (18)

Intervention effect Removal of early fruiting branches delayed the process of growth and development in cotton.
Intervention effect Removing one basal fruiting branch (R1) gave no difference from CK in total number of fruiting branches.
Intervention effect Early fruiting branch removal had no obvious effect on the total fruiting nodes.
Intervention effect Early fruiting branch removal significantly increased the leaf area index (LAI) in plants of the first sowing date.
Intervention effect Removing one basal fruiting branch (R1) increased dry matter weight by 13.5% over CK in plants of the first sowing date.
Intervention effect Removing two basal fruiting branches (R2) increased dry matter weight by 17.5% over CK in plants of the first sowing date.
Intervention effect Removing three basal fruiting branches (R3) increased dry matter weight by 19.0% over CK in plants of the first sowing date.
Intervention effect Removing one basal fruiting branch (R1) decreased the abscission rate by 5.9% in plants of the first sowing date.
Intervention effect Removing two basal fruiting branches (R2) decreased the abscission rate by 6.4% in plants of the first sowing date.
Intervention effect Removing three basal fruiting branches (R3) decreased the abscission rate by 9.5% in plants of the first sowing date.
Intervention effect Removing one basal fruiting branch (R1) decreased the rotten boll rate by 3.8% in plants of the first sowing date.
Intervention effect Removing two basal fruiting branches (R2) decreased the rotten boll rate by 3.1% in plants of the first sowing date.
Intervention effect Removing three basal fruiting branches (R3) decreased the rotten boll rate by 4.3% in plants of the first sowing date.
Intervention effect Removing one basal fruiting branch (R1) enhanced seed cotton yield by 9.6%.
Intervention effect Removing two basal fruiting branches (R2) enhanced seed cotton yield by 8.1%.
Intervention effect In plants of the second sowing date, early fruiting branch removal gave no significant yield increase.
Intervention effect In plants of the second sowing date, some treatments even decreased yield.
Intervention effect In plants of the second sowing date, early fruiting branch removal gave no significant difference in dry matter accumulation.

Intervention effectobservedoriginal resultpositivemeasured

Claim · model-written summary

Removing two basal fruiting branches (R2) enhanced seed cotton yield by 8.1%.

Taxon: cottonGossypium hirsutume0
Treatment: R2e1
Outcome: seed cotton yielde1
Direction: enhancede1
Value: 8.1%e1
Qualifier: plants of the first sowing datee7

Not reportedComparator

Verbatim

e0
“Bt-transgenic cotton plants with two different sowing dates were treated normally(CK),or by removing one(R1),two(R2) or three basal fruiting branches(R3)”
✓ located verbatim in the source · abstract, characters 0–153

e7
“For plants of the first sowing date,early fruiting branch removal significantly decreased the abscission and rotten boll rates and promoted seed yield over the two year period”
✓ located verbatim in the source · abstract, characters 903–1078

e1
“the R1 and R2 treatments enhanced the seed cotton yield by 9.6% and 8.1%,respectively”
✓ located verbatim in the source · abstract, characters 1204–1289

Title

Effects of Early Fruiting Branch Removal on Growth,Development and Senescence Processes in Transgenic Bt Cotton(Gossypium hirsutum L.)

Lichao Zhai; Zhang Yongjiang; Liantao Liu; et al. 2012

Other claims from this paper (18)

Intervention effect Removal of early fruiting branches delayed the process of growth and development in cotton.
Intervention effect Removing one basal fruiting branch (R1) gave no difference from CK in total number of fruiting branches.
Intervention effect Removing two or three basal fruiting branches (R2 and R3) significantly lowered the total number of fruiting branches relative to CK.
Intervention effect Early fruiting branch removal had no obvious effect on the total fruiting nodes.
Intervention effect Early fruiting branch removal significantly increased the leaf area index (LAI) in plants of the first sowing date.
Intervention effect Removing one basal fruiting branch (R1) increased dry matter weight by 13.5% over CK in plants of the first sowing date.
Intervention effect Removing two basal fruiting branches (R2) increased dry matter weight by 17.5% over CK in plants of the first sowing date.
Intervention effect Removing three basal fruiting branches (R3) increased dry matter weight by 19.0% over CK in plants of the first sowing date.
Intervention effect Removing one basal fruiting branch (R1) decreased the abscission rate by 5.9% in plants of the first sowing date.
Intervention effect Removing two basal fruiting branches (R2) decreased the abscission rate by 6.4% in plants of the first sowing date.
Intervention effect Removing three basal fruiting branches (R3) decreased the abscission rate by 9.5% in plants of the first sowing date.
Intervention effect Removing one basal fruiting branch (R1) decreased the rotten boll rate by 3.8% in plants of the first sowing date.
Intervention effect Removing two basal fruiting branches (R2) decreased the rotten boll rate by 3.1% in plants of the first sowing date.
Intervention effect Removing three basal fruiting branches (R3) decreased the rotten boll rate by 4.3% in plants of the first sowing date.
Intervention effect Removing one basal fruiting branch (R1) enhanced seed cotton yield by 9.6%.
Intervention effect In plants of the second sowing date, early fruiting branch removal gave no significant yield increase.
Intervention effect In plants of the second sowing date, some treatments even decreased yield.
Intervention effect In plants of the second sowing date, early fruiting branch removal gave no significant difference in dry matter accumulation.

Intervention effectobservedoriginal resultsignificant (as stated)measured

Claim · model-written summary

Removing two basal fruiting branches (R2) increased dry matter weight by 17.5% over CK in plants of the first sowing date.

Taxon: cottonGossypium hirsutume0
Treatment: R2e5
Outcome: dry matter weighte5
Direction: increasede5
Value: 17.5%e5
Qualifier: plants of the first sowing datee4
Comparator: CKe5

Verbatim

e0
“Bt-transgenic cotton plants with two different sowing dates were treated normally(CK),or by removing one(R1),two(R2) or three basal fruiting branches(R3)”
✓ located verbatim in the source · abstract, characters 0–153

e4
“Early fruiting branch removal significantly increased the LAI and dry matter accumulation in plants of the first sowing date over two years”
✓ located verbatim in the source · abstract, characters 575–714

e5
“R1,R2 and R3 increased the dry matter weight on average by 13.5%,17.5% and 19.0%,respectively,compared with CK”
✓ located verbatim in the source · abstract, characters 715–825

Title

Effects of Early Fruiting Branch Removal on Growth,Development and Senescence Processes in Transgenic Bt Cotton(Gossypium hirsutum L.)

Lichao Zhai; Zhang Yongjiang; Liantao Liu; et al. 2012

Other claims from this paper (18)

Intervention effect Removal of early fruiting branches delayed the process of growth and development in cotton.
Intervention effect Removing one basal fruiting branch (R1) gave no difference from CK in total number of fruiting branches.
Intervention effect Removing two or three basal fruiting branches (R2 and R3) significantly lowered the total number of fruiting branches relative to CK.
Intervention effect Early fruiting branch removal had no obvious effect on the total fruiting nodes.
Intervention effect Early fruiting branch removal significantly increased the leaf area index (LAI) in plants of the first sowing date.
Intervention effect Removing one basal fruiting branch (R1) increased dry matter weight by 13.5% over CK in plants of the first sowing date.
Intervention effect Removing three basal fruiting branches (R3) increased dry matter weight by 19.0% over CK in plants of the first sowing date.
Intervention effect Removing one basal fruiting branch (R1) decreased the abscission rate by 5.9% in plants of the first sowing date.
Intervention effect Removing two basal fruiting branches (R2) decreased the abscission rate by 6.4% in plants of the first sowing date.
Intervention effect Removing three basal fruiting branches (R3) decreased the abscission rate by 9.5% in plants of the first sowing date.
Intervention effect Removing one basal fruiting branch (R1) decreased the rotten boll rate by 3.8% in plants of the first sowing date.
Intervention effect Removing two basal fruiting branches (R2) decreased the rotten boll rate by 3.1% in plants of the first sowing date.
Intervention effect Removing three basal fruiting branches (R3) decreased the rotten boll rate by 4.3% in plants of the first sowing date.
Intervention effect Removing one basal fruiting branch (R1) enhanced seed cotton yield by 9.6%.
Intervention effect Removing two basal fruiting branches (R2) enhanced seed cotton yield by 8.1%.
Intervention effect In plants of the second sowing date, early fruiting branch removal gave no significant yield increase.
Intervention effect In plants of the second sowing date, some treatments even decreased yield.
Intervention effect In plants of the second sowing date, early fruiting branch removal gave no significant difference in dry matter accumulation.

Estimating Maize Leaf Area Index Using Multi-Source Features Derived from UAV Multispectral Imagery and Machine Learning Models 19

Show all 19 →

Method / resourceobservedoriginal resultmeasured

Claim · model-written summary

Adding texture features to VIs, PLSR combined with RF estimated maize LAI with an R2 of 0.717.

Taxon: MaizeZea mayst0
Treatment: texture features (TFs) incorporatione2
Outcome: R2e2
Value: 0.717e2
Qualifier: partial least squares regression (PLSR) combined with RF, SVM and GBDTe0

Verbatim

e0
“partial least squares regression (PLSR) combined with RF, SVM and GBDT to estimate maize LAI”
✓ located verbatim in the source · abstract, characters 1017–1109

e2
“After texture features (TFs) incorporation, the R2 increases to 0.717, 0.794, and 0.801, and the RMSE decreases to 0.587, 0.500, and 0.492”
✓ located verbatim in the source · abstract, characters 1260–1398

t0
“Estimating Maize Leaf Area Index Using Multi-Source Features Derived from UAV Multispectral Imagery and Machine Learning Models”
✓ located verbatim in the source · title, characters 0–127

Title

Estimating Maize Leaf Area Index Using Multi-Source Features Derived from UAV Multispectral Imagery and Machine Learning Models

Hongyan Li; Caixia Huang; Yuze Zhang; et al. Plants 2025. doi:10.3390/plants14223534

Other claims from this paper (18)

Method / resource Using only VIs, PLSR combined with RF estimated maize LAI with an R2 of 0.653.
Method / resource Using only VIs, PLSR combined with SVM estimated maize LAI with an R2 of 0.697.
Method / resource Using only VIs, PLSR combined with GBDT estimated maize LAI with an R2 of 0.634.
Method / resource Using only VIs, PLSR combined with RF gave an RMSE of 0.650 for maize LAI.
Method / resource Using only VIs, PLSR combined with SVM gave an RMSE of 0.608 for maize LAI.
Method / resource Using only VIs, PLSR combined with GBDT gave an RMSE of 0.668 for maize LAI.
Method / resource Adding texture features to VIs, PLSR combined with SVM estimated maize LAI with an R2 of 0.794.
Method / resource Adding texture features to VIs, PLSR combined with GBDT estimated maize LAI with an R2 of 0.801.
Method / resource Adding texture features to VIs, PLSR combined with RF gave an RMSE of 0.587 for maize LAI.
Method / resource Adding texture features to VIs, PLSR combined with SVM gave an RMSE of 0.500 for maize LAI.
Method / resource Adding texture features to VIs, PLSR combined with GBDT gave an RMSE of 0.492 for maize LAI.
Method / resource Adding texture indices to VIs and TFs, PLSR combined with RF estimated maize LAI with an R2 of 0.789.
Method / resource Adding texture indices to VIs and TFs, PLSR combined with SVM estimated maize LAI with an R2 of 0.804.
Method / resource Adding texture indices to VIs and TFs, PLSR combined with GBDT estimated maize LAI with an R2 of 0.844.
Method / resource Adding texture indices to VIs and TFs, PLSR combined with RF gave an RMSE of 0.506 for maize LAI.
Method / resource Adding texture indices to VIs and TFs, PLSR combined with SVM gave an RMSE of 0.489 for maize LAI.
Method / resource Adding texture indices to VIs and TFs, PLSR combined with GBDT gave an RMSE of 0.436 for maize LAI.
Method / resource The multi-model fusion framework PLSR+GBDT with VIs, TFs and TIs estimated maize LAI with an R2 of 0.859 and 0.794 in independent test sets under contrasting conditions.

Method / resourceobservedoriginal resultmeasured

Claim · model-written summary

Adding texture features to VIs, PLSR combined with SVM estimated maize LAI with an R2 of 0.794.

Taxon: MaizeZea mayst0
Treatment: texture features (TFs) incorporatione2
Outcome: R2e2
Value: 0.794e2
Qualifier: partial least squares regression (PLSR) combined with RF, SVM and GBDTe0

Verbatim

e0
“partial least squares regression (PLSR) combined with RF, SVM and GBDT to estimate maize LAI”
✓ located verbatim in the source · abstract, characters 1017–1109

e2
“After texture features (TFs) incorporation, the R2 increases to 0.717, 0.794, and 0.801, and the RMSE decreases to 0.587, 0.500, and 0.492”
✓ located verbatim in the source · abstract, characters 1260–1398

t0
“Estimating Maize Leaf Area Index Using Multi-Source Features Derived from UAV Multispectral Imagery and Machine Learning Models”
✓ located verbatim in the source · title, characters 0–127

Title

Estimating Maize Leaf Area Index Using Multi-Source Features Derived from UAV Multispectral Imagery and Machine Learning Models

Hongyan Li; Caixia Huang; Yuze Zhang; et al. Plants 2025. doi:10.3390/plants14223534

Other claims from this paper (18)

Method / resource Using only VIs, PLSR combined with RF estimated maize LAI with an R2 of 0.653.
Method / resource Using only VIs, PLSR combined with SVM estimated maize LAI with an R2 of 0.697.
Method / resource Using only VIs, PLSR combined with GBDT estimated maize LAI with an R2 of 0.634.
Method / resource Using only VIs, PLSR combined with RF gave an RMSE of 0.650 for maize LAI.
Method / resource Using only VIs, PLSR combined with SVM gave an RMSE of 0.608 for maize LAI.
Method / resource Using only VIs, PLSR combined with GBDT gave an RMSE of 0.668 for maize LAI.
Method / resource Adding texture features to VIs, PLSR combined with RF estimated maize LAI with an R2 of 0.717.
Method / resource Adding texture features to VIs, PLSR combined with GBDT estimated maize LAI with an R2 of 0.801.
Method / resource Adding texture features to VIs, PLSR combined with RF gave an RMSE of 0.587 for maize LAI.
Method / resource Adding texture features to VIs, PLSR combined with SVM gave an RMSE of 0.500 for maize LAI.
Method / resource Adding texture features to VIs, PLSR combined with GBDT gave an RMSE of 0.492 for maize LAI.
Method / resource Adding texture indices to VIs and TFs, PLSR combined with RF estimated maize LAI with an R2 of 0.789.
Method / resource Adding texture indices to VIs and TFs, PLSR combined with SVM estimated maize LAI with an R2 of 0.804.
Method / resource Adding texture indices to VIs and TFs, PLSR combined with GBDT estimated maize LAI with an R2 of 0.844.
Method / resource Adding texture indices to VIs and TFs, PLSR combined with RF gave an RMSE of 0.506 for maize LAI.
Method / resource Adding texture indices to VIs and TFs, PLSR combined with SVM gave an RMSE of 0.489 for maize LAI.
Method / resource Adding texture indices to VIs and TFs, PLSR combined with GBDT gave an RMSE of 0.436 for maize LAI.
Method / resource The multi-model fusion framework PLSR+GBDT with VIs, TFs and TIs estimated maize LAI with an R2 of 0.859 and 0.794 in independent test sets under contrasting conditions.

Method / resourceobservedoriginal resultmeasured

Claim · model-written summary

Adding texture features to VIs, PLSR combined with GBDT estimated maize LAI with an R2 of 0.801.

Taxon: MaizeZea mayst0
Treatment: texture features (TFs) incorporatione2
Outcome: R2e2
Value: 0.801e2
Qualifier: partial least squares regression (PLSR) combined with RF, SVM and GBDTe0

Verbatim

e0
“partial least squares regression (PLSR) combined with RF, SVM and GBDT to estimate maize LAI”
✓ located verbatim in the source · abstract, characters 1017–1109

e2
“After texture features (TFs) incorporation, the R2 increases to 0.717, 0.794, and 0.801, and the RMSE decreases to 0.587, 0.500, and 0.492”
✓ located verbatim in the source · abstract, characters 1260–1398

t0
“Estimating Maize Leaf Area Index Using Multi-Source Features Derived from UAV Multispectral Imagery and Machine Learning Models”
✓ located verbatim in the source · title, characters 0–127

Title

Estimating Maize Leaf Area Index Using Multi-Source Features Derived from UAV Multispectral Imagery and Machine Learning Models

Hongyan Li; Caixia Huang; Yuze Zhang; et al. Plants 2025. doi:10.3390/plants14223534

Other claims from this paper (18)

Method / resource Using only VIs, PLSR combined with RF estimated maize LAI with an R2 of 0.653.
Method / resource Using only VIs, PLSR combined with SVM estimated maize LAI with an R2 of 0.697.
Method / resource Using only VIs, PLSR combined with GBDT estimated maize LAI with an R2 of 0.634.
Method / resource Using only VIs, PLSR combined with RF gave an RMSE of 0.650 for maize LAI.
Method / resource Using only VIs, PLSR combined with SVM gave an RMSE of 0.608 for maize LAI.
Method / resource Using only VIs, PLSR combined with GBDT gave an RMSE of 0.668 for maize LAI.
Method / resource Adding texture features to VIs, PLSR combined with RF estimated maize LAI with an R2 of 0.717.
Method / resource Adding texture features to VIs, PLSR combined with SVM estimated maize LAI with an R2 of 0.794.
Method / resource Adding texture features to VIs, PLSR combined with RF gave an RMSE of 0.587 for maize LAI.
Method / resource Adding texture features to VIs, PLSR combined with SVM gave an RMSE of 0.500 for maize LAI.
Method / resource Adding texture features to VIs, PLSR combined with GBDT gave an RMSE of 0.492 for maize LAI.
Method / resource Adding texture indices to VIs and TFs, PLSR combined with RF estimated maize LAI with an R2 of 0.789.
Method / resource Adding texture indices to VIs and TFs, PLSR combined with SVM estimated maize LAI with an R2 of 0.804.
Method / resource Adding texture indices to VIs and TFs, PLSR combined with GBDT estimated maize LAI with an R2 of 0.844.
Method / resource Adding texture indices to VIs and TFs, PLSR combined with RF gave an RMSE of 0.506 for maize LAI.
Method / resource Adding texture indices to VIs and TFs, PLSR combined with SVM gave an RMSE of 0.489 for maize LAI.
Method / resource Adding texture indices to VIs and TFs, PLSR combined with GBDT gave an RMSE of 0.436 for maize LAI.
Method / resource The multi-model fusion framework PLSR+GBDT with VIs, TFs and TIs estimated maize LAI with an R2 of 0.859 and 0.794 in independent test sets under contrasting conditions.

Statistical model for evolving a croplogging technique in banana 25 18

Show all 18 →

Method / resourceobservedoriginal resultpositivesignificant (as stated)measured

Claim · model-written summary

At 250 DAP, height and girth predicted yield with R2 of 90%.

Taxon: bananat0
Outcome: plant height and plant girthe0
Value: 90%e0
Qualifier: 250 DAPe0

Verbatim

e0
“at 250 DAP, plant height and plant girth (R2 = 90%) with optimum values as 159.21 cm and 67.8 cm”
✓ located verbatim in the source · abstract, characters 854–950

t0
“banana”
✓ located verbatim in the source · title, characters 58–64

Title

Statistical model for evolving a croplogging technique in banana 25

R. Venugopalan; K.S. Shamasundaran Tropical Agriculture 2016

Other claims from this paper (17)

Method / resource At 70 DAP, leaf number and plant girth predicted yield with R2 of 88%.
Observation At 70 DAP, optimum leaf number was 8 leaves.
Observation At 70 DAP, optimum girth was 15.07 cm.
Method / resource At 126 DAP, girth and leaf number were significant yield indicators with R2 of 89%.
Observation At 126 DAP, optimum plant girth was 34.5 cm.
Observation At 126 DAP, optimum leaf number was 12 leaves.
Method / resource At 185 DAP, height and leaf length predicted yield with R2 of 92%.
Observation At 185 DAP, optimum height was 138.9 cm.
Observation At 185 DAP, optimum leaf length was 127.3 cm.
Observation At 250 DAP, optimum height was 159.21 cm.
Observation At 250 DAP, optimum girth was 67.8 cm.
Method / resource At 315 DAP, leaf breadth and length predicted yield with R2 of 81%.
Observation At 315 DAP, optimum leaf breadth was 67.2 cm.
Observation At 315 DAP, optimum leaf length was 164.1 cm.
Method / resource At harvest, fingers and hands per bunch predicted yield with R2 of 99%.
Observation At harvest, optimum finger number was 26 fingers hand-1.
Observation At harvest, optimum hand number was 13 hands bunch-1.

Method / resourceobservedoriginal resultpositivesignificant (as stated)measured

Claim · model-written summary

At 126 DAP, girth and leaf number were significant yield indicators with R2 of 89%.

Taxon: bananat0
Outcome: plant girth and number of leavese0
Value: 89%e0
Qualifier: 126 DAPe0

Verbatim

e0
“at 126 DAP, plant girth and number of leaves (R2 = 89%) with optimum values as 34.5 cm and 12 leaves”
✓ located verbatim in the source · abstract, characters 581–681

t0
“banana”
✓ located verbatim in the source · title, characters 58–64

Title

Statistical model for evolving a croplogging technique in banana 25

R. Venugopalan; K.S. Shamasundaran Tropical Agriculture 2016

Other claims from this paper (17)

Method / resource At 70 DAP, leaf number and plant girth predicted yield with R2 of 88%.
Observation At 70 DAP, optimum leaf number was 8 leaves.
Observation At 70 DAP, optimum girth was 15.07 cm.
Observation At 126 DAP, optimum plant girth was 34.5 cm.
Observation At 126 DAP, optimum leaf number was 12 leaves.
Method / resource At 185 DAP, height and leaf length predicted yield with R2 of 92%.
Observation At 185 DAP, optimum height was 138.9 cm.
Observation At 185 DAP, optimum leaf length was 127.3 cm.
Method / resource At 250 DAP, height and girth predicted yield with R2 of 90%.
Observation At 250 DAP, optimum height was 159.21 cm.
Observation At 250 DAP, optimum girth was 67.8 cm.
Method / resource At 315 DAP, leaf breadth and length predicted yield with R2 of 81%.
Observation At 315 DAP, optimum leaf breadth was 67.2 cm.
Observation At 315 DAP, optimum leaf length was 164.1 cm.
Method / resource At harvest, fingers and hands per bunch predicted yield with R2 of 99%.
Observation At harvest, optimum finger number was 26 fingers hand-1.
Observation At harvest, optimum hand number was 13 hands bunch-1.

Method / resourceobservedoriginal resultpositivesignificant (as stated)measured

Claim · model-written summary

At 185 DAP, height and leaf length predicted yield with R2 of 92%.

Taxon: bananat0
Outcome: plant height and leaf lengthe0
Value: 92%e0
Qualifier: 185 DAPe0

Verbatim

e0
“at 185 DAP, plant height and leaf length (R2 = 92%) with optimum values as 138.9 cm and 127.3 cm were significant crop-logging parameters.”
✓ located verbatim in the source · abstract, characters 686–824

t0
“banana”
✓ located verbatim in the source · title, characters 58–64

Title

Statistical model for evolving a croplogging technique in banana 25

R. Venugopalan; K.S. Shamasundaran Tropical Agriculture 2016

Other claims from this paper (17)

Method / resource At 70 DAP, leaf number and plant girth predicted yield with R2 of 88%.
Observation At 70 DAP, optimum leaf number was 8 leaves.
Observation At 70 DAP, optimum girth was 15.07 cm.
Method / resource At 126 DAP, girth and leaf number were significant yield indicators with R2 of 89%.
Observation At 126 DAP, optimum plant girth was 34.5 cm.
Observation At 126 DAP, optimum leaf number was 12 leaves.
Observation At 185 DAP, optimum height was 138.9 cm.
Observation At 185 DAP, optimum leaf length was 127.3 cm.
Method / resource At 250 DAP, height and girth predicted yield with R2 of 90%.
Observation At 250 DAP, optimum height was 159.21 cm.
Observation At 250 DAP, optimum girth was 67.8 cm.
Method / resource At 315 DAP, leaf breadth and length predicted yield with R2 of 81%.
Observation At 315 DAP, optimum leaf breadth was 67.2 cm.
Observation At 315 DAP, optimum leaf length was 164.1 cm.
Method / resource At harvest, fingers and hands per bunch predicted yield with R2 of 99%.
Observation At harvest, optimum finger number was 26 fingers hand-1.
Observation At harvest, optimum hand number was 13 hands bunch-1.

Implementation of machine learning for predicting maize crop yields using multiple linear regression and backward elimination / Stephen Gbenga Fashoto … [et al.] 18

Show all 18 →

Method / resourceobservedoriginal resultmeasured

Claim · model-written summary

combined model after normalization at 70:30 had adjusted R² 1.00.

Taxon: maizeZea mayst0
Outcome: adjusted R²e0
Value: 1.00e0
Qualifier: 70:30e0

Verbatim

e0
“adjusted R² values were 0.966 (at 70:30), 0.972 (at 80:20), and 0.978 (at 90:10) before being normalized, 1.00 across the board after normalization, and 0.967 (at 70:30), 0.973 (at 80:20), and 0.978 (at 90:10) after backward elimination.”
✓ located verbatim in the source · abstract, characters 1464–1701

t0
“maize crop yields”
✓ located verbatim in the source · title, characters 50–67

Title

Implementation of machine learning for predicting maize crop yields using multiple linear regression and backward elimination / Stephen Gbenga Fashoto … [et al.]

Stephen Gbenga Fashoto; Elliot Mbunge; Gabriel Ogunleye; et al. UiTM Institutional Repositories (Universiti Teknologi MARA) 2021

Other claims from this paper (17)

Method / resource open-source model before being normalized at 70:30 had adjusted R² 0.784.
Method / resource open-source model before being normalized at 80:20 had adjusted R² 0.849.
Method / resource open-source model before being normalized at 90:10 had adjusted R² 0.878.
Method / resource open-source model after normalization at 70:30 had adjusted R² 1.00.
Method / resource open-source model after normalization at 80:20 had adjusted R² 1.00.
Method / resource open-source model after normalization at 90:10 had adjusted R² 1.00.
Method / resource open-source model after backward elimination at 70:30 had adjusted R² 0.846.
Method / resource open-source model after backward elimination at 80:20 had adjusted R² 0.886.
Method / resource open-source model after backward elimination at 90:10 had adjusted R² 0.885.
Method / resource combined model before being normalized at 70:30 had adjusted R² 0.966.
Method / resource combined model before being normalized at 80:20 had adjusted R² 0.972.
Method / resource combined model before being normalized at 90:10 had adjusted R² 0.978.
Method / resource combined model after normalization at 80:20 had adjusted R² 1.00.
Method / resource combined model after normalization at 90:10 had adjusted R² 1.00.
Method / resource combined model after backward elimination at 70:30 had adjusted R² 0.967.
Method / resource combined model after backward elimination at 80:20 had adjusted R² 0.973.
Method / resource combined model after backward elimination at 90:10 had adjusted R² 0.978.

Method / resourceobservedoriginal resultmeasured

Claim · model-written summary

combined model after normalization at 80:20 had adjusted R² 1.00.

Taxon: maizeZea mayst0
Outcome: adjusted R²e0
Value: 1.00e0
Qualifier: 80:20e0

Verbatim

e0
“adjusted R² values were 0.966 (at 70:30), 0.972 (at 80:20), and 0.978 (at 90:10) before being normalized, 1.00 across the board after normalization, and 0.967 (at 70:30), 0.973 (at 80:20), and 0.978 (at 90:10) after backward elimination.”
✓ located verbatim in the source · abstract, characters 1464–1701

t0
“maize crop yields”
✓ located verbatim in the source · title, characters 50–67

Title

Implementation of machine learning for predicting maize crop yields using multiple linear regression and backward elimination / Stephen Gbenga Fashoto … [et al.]

Stephen Gbenga Fashoto; Elliot Mbunge; Gabriel Ogunleye; et al. UiTM Institutional Repositories (Universiti Teknologi MARA) 2021

Other claims from this paper (17)

Method / resource open-source model before being normalized at 70:30 had adjusted R² 0.784.
Method / resource open-source model before being normalized at 80:20 had adjusted R² 0.849.
Method / resource open-source model before being normalized at 90:10 had adjusted R² 0.878.
Method / resource open-source model after normalization at 70:30 had adjusted R² 1.00.
Method / resource open-source model after normalization at 80:20 had adjusted R² 1.00.
Method / resource open-source model after normalization at 90:10 had adjusted R² 1.00.
Method / resource open-source model after backward elimination at 70:30 had adjusted R² 0.846.
Method / resource open-source model after backward elimination at 80:20 had adjusted R² 0.886.
Method / resource open-source model after backward elimination at 90:10 had adjusted R² 0.885.
Method / resource combined model before being normalized at 70:30 had adjusted R² 0.966.
Method / resource combined model before being normalized at 80:20 had adjusted R² 0.972.
Method / resource combined model before being normalized at 90:10 had adjusted R² 0.978.
Method / resource combined model after normalization at 70:30 had adjusted R² 1.00.
Method / resource combined model after normalization at 90:10 had adjusted R² 1.00.
Method / resource combined model after backward elimination at 70:30 had adjusted R² 0.967.
Method / resource combined model after backward elimination at 80:20 had adjusted R² 0.973.
Method / resource combined model after backward elimination at 90:10 had adjusted R² 0.978.

Method / resourceobservedoriginal resultmeasured

Claim · model-written summary

combined model after normalization at 90:10 had adjusted R² 1.00.

Taxon: maizeZea mayst0
Outcome: adjusted R²e0
Value: 1.00e0
Qualifier: 90:10e0

Verbatim

e0
“adjusted R² values were 0.966 (at 70:30), 0.972 (at 80:20), and 0.978 (at 90:10) before being normalized, 1.00 across the board after normalization, and 0.967 (at 70:30), 0.973 (at 80:20), and 0.978 (at 90:10) after backward elimination.”
✓ located verbatim in the source · abstract, characters 1464–1701

t0
“maize crop yields”
✓ located verbatim in the source · title, characters 50–67

Title

Implementation of machine learning for predicting maize crop yields using multiple linear regression and backward elimination / Stephen Gbenga Fashoto … [et al.]

Stephen Gbenga Fashoto; Elliot Mbunge; Gabriel Ogunleye; et al. UiTM Institutional Repositories (Universiti Teknologi MARA) 2021

Other claims from this paper (17)

Method / resource open-source model before being normalized at 70:30 had adjusted R² 0.784.
Method / resource open-source model before being normalized at 80:20 had adjusted R² 0.849.
Method / resource open-source model before being normalized at 90:10 had adjusted R² 0.878.
Method / resource open-source model after normalization at 70:30 had adjusted R² 1.00.
Method / resource open-source model after normalization at 80:20 had adjusted R² 1.00.
Method / resource open-source model after normalization at 90:10 had adjusted R² 1.00.
Method / resource open-source model after backward elimination at 70:30 had adjusted R² 0.846.
Method / resource open-source model after backward elimination at 80:20 had adjusted R² 0.886.
Method / resource open-source model after backward elimination at 90:10 had adjusted R² 0.885.
Method / resource combined model before being normalized at 70:30 had adjusted R² 0.966.
Method / resource combined model before being normalized at 80:20 had adjusted R² 0.972.
Method / resource combined model before being normalized at 90:10 had adjusted R² 0.978.
Method / resource combined model after normalization at 70:30 had adjusted R² 1.00.
Method / resource combined model after normalization at 80:20 had adjusted R² 1.00.
Method / resource combined model after backward elimination at 70:30 had adjusted R² 0.967.
Method / resource combined model after backward elimination at 80:20 had adjusted R² 0.973.
Method / resource combined model after backward elimination at 90:10 had adjusted R² 0.978.

Maize Leaf Area Index Estimation Based on Machine Learning Algorithm and Computer Vision 18

Show all 18 →

Method / resourceobservedoriginal resultpositivemeasured

Claim · model-written summary

VisLAI attained R2 0.84 at big trumpet.

Taxon: MaizeZea mayst0
Organ / stage: big trumpete0
Outcome: R2e0
Value: 0.84e0

Verbatim

t0
“Maize”
✓ located verbatim in the source · title, characters 0–5

e0
“The proposed LAI estimation model, named VisLAI, based solely on visible light imagery, demonstrated high accuracy, with R2 values of 0.84, 0.75, and 0.50, and RMSE values of 0.24, 0.35, and 0.44 across the big trumpet, tasseling–silking, and grain filling stages, respectively.”
✓ located verbatim in the source · abstract, characters 811–1089

Title

Maize Leaf Area Index Estimation Based on Machine Learning Algorithm and Computer Vision

Wanna Fu; Zhen Chen; Qian Cheng; et al. Agriculture 2025. doi:10.3390/agriculture15121272

Other claims from this paper (18)

Method / resource VisLAI attained RMSE 0.24 at big trumpet.
Method / resource VisLAI attained R2 0.75 at tasseling–silking.
Method / resource VisLAI attained RMSE 0.35 at tasseling–silking.
Method / resource VisLAI attained R2 0.50 at grain filling.
Method / resource VisLAI attained RMSE 0.44 at grain filling.
Method / resource HSV-optimized VisLAI attained R2 0.92 at big trumpet.
Method / resource HSV-optimized VisLAI attained RMSE 0.19 at big trumpet.
Method / resource HSV-optimized VisLAI attained R2 0.90 at tasseling–silking.
Method / resource HSV-optimized VisLAI attained RMSE 0.23 at tasseling–silking.
Method / resource HSV-optimized VisLAI attained R2 0.85 at grain filling.
Method / resource HSV-optimized VisLAI attained RMSE 0.22 at grain filling.
Method / resource GB attained R2 0.88 at big trumpet.
Method / resource GB attained R2 0.88 at tasseling–silking.
Method / resource GB attained R2 0.65 at grain filling.
Method / resource GB attained RMSE 0.22 at big trumpet.
Method / resource GB attained RMSE 0.25 at tasseling–silking.
Method / resource GB attained RMSE 0.34 at grain filling.
Method / resource VisLAI outperformed all machine learning models, especially at grain filling.

Method / resourceobservedoriginal resultpositivemeasured

Claim · model-written summary

VisLAI attained R2 0.75 at tasseling–silking.

Taxon: MaizeZea mayst0
Organ / stage: tasseling–silkinge0
Outcome: R2e0
Value: 0.75e0

Verbatim

t0
“Maize”
✓ located verbatim in the source · title, characters 0–5

e0
“The proposed LAI estimation model, named VisLAI, based solely on visible light imagery, demonstrated high accuracy, with R2 values of 0.84, 0.75, and 0.50, and RMSE values of 0.24, 0.35, and 0.44 across the big trumpet, tasseling–silking, and grain filling stages, respectively.”
✓ located verbatim in the source · abstract, characters 811–1089

Title

Maize Leaf Area Index Estimation Based on Machine Learning Algorithm and Computer Vision

Wanna Fu; Zhen Chen; Qian Cheng; et al. Agriculture 2025. doi:10.3390/agriculture15121272

Other claims from this paper (18)

Method / resource VisLAI attained R2 0.84 at big trumpet.
Method / resource VisLAI attained RMSE 0.24 at big trumpet.
Method / resource VisLAI attained RMSE 0.35 at tasseling–silking.
Method / resource VisLAI attained R2 0.50 at grain filling.
Method / resource VisLAI attained RMSE 0.44 at grain filling.
Method / resource HSV-optimized VisLAI attained R2 0.92 at big trumpet.
Method / resource HSV-optimized VisLAI attained RMSE 0.19 at big trumpet.
Method / resource HSV-optimized VisLAI attained R2 0.90 at tasseling–silking.
Method / resource HSV-optimized VisLAI attained RMSE 0.23 at tasseling–silking.
Method / resource HSV-optimized VisLAI attained R2 0.85 at grain filling.
Method / resource HSV-optimized VisLAI attained RMSE 0.22 at grain filling.
Method / resource GB attained R2 0.88 at big trumpet.
Method / resource GB attained R2 0.88 at tasseling–silking.
Method / resource GB attained R2 0.65 at grain filling.
Method / resource GB attained RMSE 0.22 at big trumpet.
Method / resource GB attained RMSE 0.25 at tasseling–silking.
Method / resource GB attained RMSE 0.34 at grain filling.
Method / resource VisLAI outperformed all machine learning models, especially at grain filling.

Method / resourceobservedoriginal resultpositivemeasured

Claim · model-written summary

VisLAI attained R2 0.50 at grain filling.

Taxon: MaizeZea mayst0
Organ / stage: grain fillinge0
Outcome: R2e0
Value: 0.50e0

Verbatim

t0
“Maize”
✓ located verbatim in the source · title, characters 0–5

e0
“The proposed LAI estimation model, named VisLAI, based solely on visible light imagery, demonstrated high accuracy, with R2 values of 0.84, 0.75, and 0.50, and RMSE values of 0.24, 0.35, and 0.44 across the big trumpet, tasseling–silking, and grain filling stages, respectively.”
✓ located verbatim in the source · abstract, characters 811–1089

Title

Maize Leaf Area Index Estimation Based on Machine Learning Algorithm and Computer Vision

Wanna Fu; Zhen Chen; Qian Cheng; et al. Agriculture 2025. doi:10.3390/agriculture15121272

Other claims from this paper (18)

Method / resource VisLAI attained R2 0.84 at big trumpet.
Method / resource VisLAI attained RMSE 0.24 at big trumpet.
Method / resource VisLAI attained R2 0.75 at tasseling–silking.
Method / resource VisLAI attained RMSE 0.35 at tasseling–silking.
Method / resource VisLAI attained RMSE 0.44 at grain filling.
Method / resource HSV-optimized VisLAI attained R2 0.92 at big trumpet.
Method / resource HSV-optimized VisLAI attained RMSE 0.19 at big trumpet.
Method / resource HSV-optimized VisLAI attained R2 0.90 at tasseling–silking.
Method / resource HSV-optimized VisLAI attained RMSE 0.23 at tasseling–silking.
Method / resource HSV-optimized VisLAI attained R2 0.85 at grain filling.
Method / resource HSV-optimized VisLAI attained RMSE 0.22 at grain filling.
Method / resource GB attained R2 0.88 at big trumpet.
Method / resource GB attained R2 0.88 at tasseling–silking.
Method / resource GB attained R2 0.65 at grain filling.
Method / resource GB attained RMSE 0.22 at big trumpet.
Method / resource GB attained RMSE 0.25 at tasseling–silking.
Method / resource GB attained RMSE 0.34 at grain filling.
Method / resource VisLAI outperformed all machine learning models, especially at grain filling.

Effects of Leaf Potassium Content on Growth Dynamics of Muskmelon and Developing a Coupling Model of Nitrogen and Potassium 17

Show all 17 →

Method / resourceobservedoriginal resultmeasured

Claim · model-written summary

The potassium model had r2 of 0.85 for soluble solids.

Taxon: muskmelonCucumis melot0
Outcome: soluble solids content (SO)e0
Value: 0.85e0

Verbatim

e0
“soluble solids content (SO) of potassium model were 0.93, 0.98, 0.83, 0.96, 0.98, 0.99, 0.94, 0.94, 0.89, 0.85, and 0.90, respectively;”
✓ located verbatim in the source · abstract, characters 1446–1581

t0
“Muskmelon”
✓ located verbatim in the source · title, characters 56–65

Title

Effects of Leaf Potassium Content on Growth Dynamics of Muskmelon and Developing a Coupling Model of Nitrogen and Potassium

Xiaofeng Yang; Lianzhu Chen; Ming Cao; et al. HortScience 2019. doi:10.21273/hortsci13980-19

Other claims from this paper (19)

Observation The critical flowering-stage leaf potassium content for Nanhaimi was 55.0 mg·g −1.
Observation The critical flowering-stage leaf potassium content for Xizhoumi 25 was 46.0 mg·g −1.
Method / resource The potassium model had r2 of 0.93 for LAI.
Method / resource The potassium model had r2 of 0.98 for shoot dry weight.
Method / resource The potassium model had r2 of 0.83 for stem dry weight.
Method / resource The potassium model had r2 of 0.96 for leaf dry weight.
Method / resource The potassium model had r2 of 0.98 for fruit dry weight.
Method / resource The potassium model had r2 of 0.99 for fresh fruit weight.
Method / resource The potassium model had r2 of 0.94 for soluble sugar.
Method / resource The potassium model had r2 of 0.94 for soluble protein.
Method / resource The potassium model had r2 of 0.89 for vitamin C.
Method / resource The multiplicative nitrogen-potassium coupling was selected after comparison with minimum coupling.
Method / resource The coupled model had r2 of 0.78 for LAI.
Method / resource The coupled model had r2 of 0.91 for DWSH.
Method / resource The coupled model had r2 of 0.93 for DWST.
Method / resource The coupled model had r2 of 0.94 for DWL.
Method / resource The coupled model had r2 of 0.83 for DWF.
Method / resource The coupled model had r2 of 0.89 for FWF.
Method / resource The coupled model had r2 of 0.92 for SU.

Method / resourceobservedoriginal resultmeasured

Claim · model-written summary

The potassium model had r2 of 0.89 for vitamin C.

Taxon: muskmelonCucumis melot0
Outcome: vitamin C (Vc)e0
Value: 0.89e0

Verbatim

e0
“vitamin C (Vc), and soluble solids content (SO) of potassium model were 0.93, 0.98, 0.83, 0.96, 0.98, 0.99, 0.94, 0.94, 0.89, 0.85, and 0.90, respectively;”
✓ located verbatim in the source · abstract, characters 1426–1581

t0
“Muskmelon”
✓ located verbatim in the source · title, characters 56–65

Title

Effects of Leaf Potassium Content on Growth Dynamics of Muskmelon and Developing a Coupling Model of Nitrogen and Potassium

Xiaofeng Yang; Lianzhu Chen; Ming Cao; et al. HortScience 2019. doi:10.21273/hortsci13980-19

Other claims from this paper (19)

Observation The critical flowering-stage leaf potassium content for Nanhaimi was 55.0 mg·g −1.
Observation The critical flowering-stage leaf potassium content for Xizhoumi 25 was 46.0 mg·g −1.
Method / resource The potassium model had r2 of 0.93 for LAI.
Method / resource The potassium model had r2 of 0.98 for shoot dry weight.
Method / resource The potassium model had r2 of 0.83 for stem dry weight.
Method / resource The potassium model had r2 of 0.96 for leaf dry weight.
Method / resource The potassium model had r2 of 0.98 for fruit dry weight.
Method / resource The potassium model had r2 of 0.99 for fresh fruit weight.
Method / resource The potassium model had r2 of 0.94 for soluble sugar.
Method / resource The potassium model had r2 of 0.94 for soluble protein.
Method / resource The potassium model had r2 of 0.85 for soluble solids.
Method / resource The multiplicative nitrogen-potassium coupling was selected after comparison with minimum coupling.
Method / resource The coupled model had r2 of 0.78 for LAI.
Method / resource The coupled model had r2 of 0.91 for DWSH.
Method / resource The coupled model had r2 of 0.93 for DWST.
Method / resource The coupled model had r2 of 0.94 for DWL.
Method / resource The coupled model had r2 of 0.83 for DWF.
Method / resource The coupled model had r2 of 0.89 for FWF.
Method / resource The coupled model had r2 of 0.92 for SU.

Method / resourceobservedoriginal resultmeasured

Claim · model-written summary

The potassium model had r2 of 0.94 for soluble protein.

Taxon: muskmelonCucumis melot0
Outcome: soluble protein content (PR)e0
Value: 0.94e0

Verbatim

e0
“soluble protein content (PR), vitamin C (Vc), and soluble solids content (SO) of potassium model were 0.93, 0.98, 0.83, 0.96, 0.98, 0.99, 0.94, 0.94, 0.89, 0.85, and 0.90, respectively;”
✓ located verbatim in the source · abstract, characters 1396–1581

t0
“Muskmelon”
✓ located verbatim in the source · title, characters 56–65

Title

Effects of Leaf Potassium Content on Growth Dynamics of Muskmelon and Developing a Coupling Model of Nitrogen and Potassium

Xiaofeng Yang; Lianzhu Chen; Ming Cao; et al. HortScience 2019. doi:10.21273/hortsci13980-19

Other claims from this paper (19)

Observation The critical flowering-stage leaf potassium content for Nanhaimi was 55.0 mg·g −1.
Observation The critical flowering-stage leaf potassium content for Xizhoumi 25 was 46.0 mg·g −1.
Method / resource The potassium model had r2 of 0.93 for LAI.
Method / resource The potassium model had r2 of 0.98 for shoot dry weight.
Method / resource The potassium model had r2 of 0.83 for stem dry weight.
Method / resource The potassium model had r2 of 0.96 for leaf dry weight.
Method / resource The potassium model had r2 of 0.98 for fruit dry weight.
Method / resource The potassium model had r2 of 0.99 for fresh fruit weight.
Method / resource The potassium model had r2 of 0.94 for soluble sugar.
Method / resource The potassium model had r2 of 0.89 for vitamin C.
Method / resource The potassium model had r2 of 0.85 for soluble solids.
Method / resource The multiplicative nitrogen-potassium coupling was selected after comparison with minimum coupling.
Method / resource The coupled model had r2 of 0.78 for LAI.
Method / resource The coupled model had r2 of 0.91 for DWSH.
Method / resource The coupled model had r2 of 0.93 for DWST.
Method / resource The coupled model had r2 of 0.94 for DWL.
Method / resource The coupled model had r2 of 0.83 for DWF.
Method / resource The coupled model had r2 of 0.89 for FWF.
Method / resource The coupled model had r2 of 0.92 for SU.

Effect of Drought Stress during Soybean R2–R6 Growth Stages on Sucrose Metabolism in Leaf and Seed 17

Show all 17 →

Intervention effectobservedoriginal resultmeasured

Claim · model-written summary

Drought stress increased soluble sugar contents in soybean.

Taxon: SoybeanGlycine maxt0
Treatment: Drought stresse0
Outcome: soluble sugar contentse0
Direction: increasede0

Not reportedComparator

Verbatim

e0
“Drought stress increased soluble sugar contents”
✓ located verbatim in the source · abstract, characters 797–844

t0
“Drought Stress during Soybean”
✓ located verbatim in the source · title, characters 10–39

Title

Effect of Drought Stress during Soybean R2–R6 Growth Stages on Sucrose Metabolism in Leaf and Seed

Yanli Du; Qiang Zhao; Liru Chen; et al. International Journal of Molecular Sciences 2020. doi:10.3390/ijms21020618

Other claims from this paper (16)

Intervention effect Drought stress reduced leaf photosynthetic rate in soybean by 63.93.
Intervention effect Drought stress reduced soybean shoot biomass by 33.53.
Intervention effect Drought stress reduced soybean seed weight by 41.65.
Intervention effect Drought stress increased the activities of sucrose phosphate synthase, sucrose synthase and acid invertase in soybean.
Intervention effect Drought stress up-regulated the expression of GmSPS1, GmSuSy2 and GmA-INV in soybean.
Intervention effect Drought stress decreased leaf starch content by 15.13%.
Intervention effect Drought stress decreased seed starch, fructose and glucose contents during late seed filling.
Intervention effect Drought stress induced sucrose accumulation in seeds during late seed filling.
Intervention effect Drought stress decreased the hexose-to-sucrose ratio in seeds.
Intervention effect Under drought in early seed development, soybean sucrose synthesis and degradation enzyme activities were enhanced.
Intervention effect Under drought in early seed development, expression of soybean genes related to metabolism was enhanced.
Intervention effect Under drought in early seed development, expression of soybean sucrose transporter genes was enhanced.
Intervention effect Under prolonged drought stress, the enzyme activities of sucrose synthesis and degradation decreased.
Mechanism Drought stress enhances the capacity for unloading sucrose into soybean seeds.
Association Sucrose flow from leaves to seeds was diminished in the middle and late seed filling stages.
Mechanism Differential regulation of sucrose allocation, metabolism and transport across seed development stages may be a mechanism for soybean drought resistance.

Intervention effectobservedoriginal resultnegativemeasured

Claim · model-written summary

Drought stress decreased leaf starch content by 15.13%.

Taxon: SoybeanGlycine maxt0
Organ / stage: leavese0
Treatment: Drought stresse1
Outcome: starch contente0
Direction: decreasede0
Value: 15.13%e0

Not reportedComparator

Verbatim

e0
“decreased starch content by 15.13% in leaves”
✓ located verbatim in the source · abstract, characters 1014–1058

e1
“Drought stress increased soluble sugar contents”
✓ located verbatim in the source · abstract, characters 797–844

t0
“Drought Stress during Soybean”
✓ located verbatim in the source · title, characters 10–39

Title

Effect of Drought Stress during Soybean R2–R6 Growth Stages on Sucrose Metabolism in Leaf and Seed

Yanli Du; Qiang Zhao; Liru Chen; et al. International Journal of Molecular Sciences 2020. doi:10.3390/ijms21020618

Other claims from this paper (16)

Intervention effect Drought stress reduced leaf photosynthetic rate in soybean by 63.93.
Intervention effect Drought stress reduced soybean shoot biomass by 33.53.
Intervention effect Drought stress reduced soybean seed weight by 41.65.
Intervention effect Drought stress increased soluble sugar contents in soybean.
Intervention effect Drought stress increased the activities of sucrose phosphate synthase, sucrose synthase and acid invertase in soybean.
Intervention effect Drought stress up-regulated the expression of GmSPS1, GmSuSy2 and GmA-INV in soybean.
Intervention effect Drought stress decreased seed starch, fructose and glucose contents during late seed filling.
Intervention effect Drought stress induced sucrose accumulation in seeds during late seed filling.
Intervention effect Drought stress decreased the hexose-to-sucrose ratio in seeds.
Intervention effect Under drought in early seed development, soybean sucrose synthesis and degradation enzyme activities were enhanced.
Intervention effect Under drought in early seed development, expression of soybean genes related to metabolism was enhanced.
Intervention effect Under drought in early seed development, expression of soybean sucrose transporter genes was enhanced.
Intervention effect Under prolonged drought stress, the enzyme activities of sucrose synthesis and degradation decreased.
Mechanism Drought stress enhances the capacity for unloading sucrose into soybean seeds.
Association Sucrose flow from leaves to seeds was diminished in the middle and late seed filling stages.
Mechanism Differential regulation of sucrose allocation, metabolism and transport across seed development stages may be a mechanism for soybean drought resistance.

Mechanismauthor interpretationoriginal resultpositivemeasured

Claim · model-written summary

Drought stress enhances the capacity for unloading sucrose into soybean seeds.

Taxon: SoybeanGlycine maxt0
Treatment: drought stresse0
Outcome: capacity for unloading sucrose into seedse0

Verbatim

e0
“These results demonstrated that drought stress enhances the capacity for unloading sucrose into seeds”
✓ located verbatim in the source · abstract, characters 1592–1693

t0
“Drought Stress during Soybean”
✓ located verbatim in the source · title, characters 10–39

Title

Effect of Drought Stress during Soybean R2–R6 Growth Stages on Sucrose Metabolism in Leaf and Seed

Yanli Du; Qiang Zhao; Liru Chen; et al. International Journal of Molecular Sciences 2020. doi:10.3390/ijms21020618

Other claims from this paper (16)

Intervention effect Drought stress reduced leaf photosynthetic rate in soybean by 63.93.
Intervention effect Drought stress reduced soybean shoot biomass by 33.53.
Intervention effect Drought stress reduced soybean seed weight by 41.65.
Intervention effect Drought stress increased soluble sugar contents in soybean.
Intervention effect Drought stress increased the activities of sucrose phosphate synthase, sucrose synthase and acid invertase in soybean.
Intervention effect Drought stress up-regulated the expression of GmSPS1, GmSuSy2 and GmA-INV in soybean.
Intervention effect Drought stress decreased leaf starch content by 15.13%.
Intervention effect Drought stress decreased seed starch, fructose and glucose contents during late seed filling.
Intervention effect Drought stress induced sucrose accumulation in seeds during late seed filling.
Intervention effect Drought stress decreased the hexose-to-sucrose ratio in seeds.
Intervention effect Under drought in early seed development, soybean sucrose synthesis and degradation enzyme activities were enhanced.
Intervention effect Under drought in early seed development, expression of soybean genes related to metabolism was enhanced.
Intervention effect Under drought in early seed development, expression of soybean sucrose transporter genes was enhanced.
Intervention effect Under prolonged drought stress, the enzyme activities of sucrose synthesis and degradation decreased.
Association Sucrose flow from leaves to seeds was diminished in the middle and late seed filling stages.
Mechanism Differential regulation of sucrose allocation, metabolism and transport across seed development stages may be a mechanism for soybean drought resistance.

Identification of SCAR and RAPD markers linked to Rz1 gene in Holly sugar beet using BSA and two genetic distance estimation methods 16

Show all 16 →

Observationobservedoriginal resultmeasured

Claim · model-written summary

R2 was 44.5 cM from Rz1 by Map maker.

Taxon: sugar beetBeta vulgaris subsp. vulgarist0
Material: R2e0
Gene: Rz1e0
Outcome: markers weree0
Value: 44.5e0
Qualifier: repulsion phasee0

Verbatim

e0
“On the other hand, tagging Rz1 gene by Map maker ver.3.0 method showed that R1, R2 and R3 markers were 32.4, 44.5 and 60.1 cM apart in repulsion phase and C3, C2, C4 and C1 markers were 25, 34.7, 46.5 and 57.9 cM apart in coupling phase, respectively.”
✓ located verbatim in the source · abstract, characters 1320–1571

t0
“Identification of SCAR and RAPD markers linked to Rz1 gene in Holly sugar beet using BSA and two genetic distance estimation methods”
✓ located verbatim in the source · title, characters 0–132

Title

Identification of SCAR and RAPD markers linked to Rz1 gene in Holly sugar beet using BSA and two genetic distance estimation methods

Seyed Mohammad Amin Feghhi; Peyman Norouzi; Abbas Saidi; et al. Electronic Journal of Plant Breeding 2012

Other claims from this paper (17)

Observation Nine primer pairs were polymorphic between resistant and susceptible DNA bulks.
Observation R1 was 2.3 cM from Rz1 by recombinant frequency.
Observation R2 was 8.3 cM from Rz1 by recombinant frequency.
Observation R3 was 16.6 cM from Rz1 by recombinant frequency.
Observation C3 was 20 cM from Rz1 by recombinant frequency.
Observation C4 was 21.4 cM from Rz1 by recombinant frequency.
Observation C1 was 27.5 cM from Rz1 by recombinant frequency.
Observation C2 was 32.9 cM from Rz1 by recombinant frequency.
Observation C5 was 43.7 cM from Rz1 by recombinant frequency.
Observation C6 was 51.9 cM from Rz1 by recombinant frequency.
Observation R1 was 32.4 cM from Rz1 by Map maker.
Observation R3 was 60.1 cM from Rz1 by Map maker.
Observation C3 was 25 cM from Rz1 by Map maker.
Observation C2 was 34.7 cM from Rz1 by Map maker.
Observation C4 was 46.5 cM from Rz1 by Map maker.
Observation C1 was 57.9 cM from Rz1 by Map maker.
Method / resource A SCAR marker R1 was generated for marker assisted selection.

Observationobservedoriginal resultmeasured

Claim · model-written summary

R2 was 8.3 cM from Rz1 by recombinant frequency.

Taxon: sugar beetBeta vulgaris subsp. vulgarist0
Material: R2e0
Gene: Rz1e0
Outcome: markers weree0
Value: 8.3e0
Qualifier: repulsion phasee0

Verbatim

e0
“Using frequency of recombinant plants method to tag Rz1 gene, it was found that R1, R2 and R3 markers were 2.3, 8.3 and 16.6 cM apart in repulsion phase and C3, C4, C1, C2, C5, and C6 markers were 20, 21.4, 27.5, 32.9, 43.7, and 51.9 cM apart in coupling phase, respectively.”
✓ located verbatim in the source · abstract, characters 1044–1319

t0
“Identification of SCAR and RAPD markers linked to Rz1 gene in Holly sugar beet using BSA and two genetic distance estimation methods”
✓ located verbatim in the source · title, characters 0–132

Title

Identification of SCAR and RAPD markers linked to Rz1 gene in Holly sugar beet using BSA and two genetic distance estimation methods

Seyed Mohammad Amin Feghhi; Peyman Norouzi; Abbas Saidi; et al. Electronic Journal of Plant Breeding 2012

Other claims from this paper (17)

Observation Nine primer pairs were polymorphic between resistant and susceptible DNA bulks.
Observation R1 was 2.3 cM from Rz1 by recombinant frequency.
Observation R3 was 16.6 cM from Rz1 by recombinant frequency.
Observation C3 was 20 cM from Rz1 by recombinant frequency.
Observation C4 was 21.4 cM from Rz1 by recombinant frequency.
Observation C1 was 27.5 cM from Rz1 by recombinant frequency.
Observation C2 was 32.9 cM from Rz1 by recombinant frequency.
Observation C5 was 43.7 cM from Rz1 by recombinant frequency.
Observation C6 was 51.9 cM from Rz1 by recombinant frequency.
Observation R1 was 32.4 cM from Rz1 by Map maker.
Observation R2 was 44.5 cM from Rz1 by Map maker.
Observation R3 was 60.1 cM from Rz1 by Map maker.
Observation C3 was 25 cM from Rz1 by Map maker.
Observation C2 was 34.7 cM from Rz1 by Map maker.
Observation C4 was 46.5 cM from Rz1 by Map maker.
Observation C1 was 57.9 cM from Rz1 by Map maker.
Method / resource A SCAR marker R1 was generated for marker assisted selection.

Observationobservedoriginal resultmeasured

Claim · model-written summary

C3 was 25 cM from Rz1 by Map maker.

Taxon: sugar beetBeta vulgaris subsp. vulgarist0
Material: C3e0
Gene: Rz1e0
Outcome: markers weree0
Value: 25e0
Qualifier: coupling phasee0

Verbatim

e0
“On the other hand, tagging Rz1 gene by Map maker ver.3.0 method showed that R1, R2 and R3 markers were 32.4, 44.5 and 60.1 cM apart in repulsion phase and C3, C2, C4 and C1 markers were 25, 34.7, 46.5 and 57.9 cM apart in coupling phase, respectively.”
✓ located verbatim in the source · abstract, characters 1320–1571

t0
“Identification of SCAR and RAPD markers linked to Rz1 gene in Holly sugar beet using BSA and two genetic distance estimation methods”
✓ located verbatim in the source · title, characters 0–132

Title

Identification of SCAR and RAPD markers linked to Rz1 gene in Holly sugar beet using BSA and two genetic distance estimation methods

Seyed Mohammad Amin Feghhi; Peyman Norouzi; Abbas Saidi; et al. Electronic Journal of Plant Breeding 2012

Other claims from this paper (17)

Observation Nine primer pairs were polymorphic between resistant and susceptible DNA bulks.
Observation R1 was 2.3 cM from Rz1 by recombinant frequency.
Observation R2 was 8.3 cM from Rz1 by recombinant frequency.
Observation R3 was 16.6 cM from Rz1 by recombinant frequency.
Observation C3 was 20 cM from Rz1 by recombinant frequency.
Observation C4 was 21.4 cM from Rz1 by recombinant frequency.
Observation C1 was 27.5 cM from Rz1 by recombinant frequency.
Observation C2 was 32.9 cM from Rz1 by recombinant frequency.
Observation C5 was 43.7 cM from Rz1 by recombinant frequency.
Observation C6 was 51.9 cM from Rz1 by recombinant frequency.
Observation R1 was 32.4 cM from Rz1 by Map maker.
Observation R2 was 44.5 cM from Rz1 by Map maker.
Observation R3 was 60.1 cM from Rz1 by Map maker.
Observation C2 was 34.7 cM from Rz1 by Map maker.
Observation C4 was 46.5 cM from Rz1 by Map maker.
Observation C1 was 57.9 cM from Rz1 by Map maker.
Method / resource A SCAR marker R1 was generated for marker assisted selection.

Effects of the Spatial Resolution of UAV Images on the Prediction and Transferability of Nitrogen Content Model for Winter Wheat 16

Show all 16 →

Method / resourceobservedoriginal resultmeasured

Claim · model-written summary

Upscaling the 0.01 m model to 0.02 m images gave R2 0.84.

Taxon: Winter WheatTriticum aestivumt0
Outcome: R2e0
Value: 0.84e0
Qualifier: 0.02 me0

Verbatim

e0
“For upscaling, the model established with the 0.01 m images had a R2 of 0.84 and 0.89 when applied to images with 0.02 m and 0.05 m resolutions, respectively.”
✓ located verbatim in the source · abstract, characters 1144–1302

t0
“Winter Wheat”
✓ located verbatim in the source · title, characters 116–128

Title

Effects of the Spatial Resolution of UAV Images on the Prediction and Transferability of Nitrogen Content Model for Winter Wheat

Yan Guo; Jia He; Jingyi Huang; et al. Drones 2022. doi:10.3390/drones6100299

Other claims from this paper (18)

Method / resource At 0.01 m resolution, nitrogen prediction R2 was 0.84.
Method / resource At 0.01 m resolution, RMSE was 4.57 g m−2.
Method / resource At 0.01 m resolution, MAE was 2.50 g m−2.
Method / resource At 0.01 m resolution, RPD was 2.34.
Method / resource At 0.02 m resolution, R2 was 0.86.
Method / resource At 0.02 m resolution, RMSE was 4.15 g m−2.
Method / resource At 0.02 m resolution, MAE was 2.82 g m−2.
Method / resource At 0.02 m resolution, RPD was 2.65.
Method / resource At 0.05 m resolution, R2 was 0.92.
Method / resource At 0.05 m resolution, RMSE was 3.17 g m−2.
Method / resource At 0.05 m resolution, MAE was 2.45 g m−2.
Method / resource At 0.05 m resolution, RPD was 2.86.
Method / resource Upscaling the 0.01 m model to 0.05 m images gave R2 0.89.
Method / resource Downscaling the 0.05 m model to 0.01 m images gave R2 0.86.
Method / resource Downscaling the 0.05 m model to 0.02 m images gave R2 0.83.
Method / resource Spatial resolution affected texture features more than spectral features.
Association Resolution effects on performance and transferability decreased as irrigated plant wetness increased.
Method / resource All tested resolutions achieved good winter wheat nitrogen prediction and transferability.

Method / resourceobservedoriginal resultmeasured

Claim · model-written summary

Upscaling the 0.01 m model to 0.05 m images gave R2 0.89.

Taxon: Winter WheatTriticum aestivumt0
Outcome: R2e0
Value: 0.89e0
Qualifier: 0.05 me0

Verbatim

e0
“For upscaling, the model established with the 0.01 m images had a R2 of 0.84 and 0.89 when applied to images with 0.02 m and 0.05 m resolutions, respectively.”
✓ located verbatim in the source · abstract, characters 1144–1302

t0
“Winter Wheat”
✓ located verbatim in the source · title, characters 116–128

Title

Effects of the Spatial Resolution of UAV Images on the Prediction and Transferability of Nitrogen Content Model for Winter Wheat

Yan Guo; Jia He; Jingyi Huang; et al. Drones 2022. doi:10.3390/drones6100299

Other claims from this paper (18)

Method / resource At 0.01 m resolution, nitrogen prediction R2 was 0.84.
Method / resource At 0.01 m resolution, RMSE was 4.57 g m−2.
Method / resource At 0.01 m resolution, MAE was 2.50 g m−2.
Method / resource At 0.01 m resolution, RPD was 2.34.
Method / resource At 0.02 m resolution, R2 was 0.86.
Method / resource At 0.02 m resolution, RMSE was 4.15 g m−2.
Method / resource At 0.02 m resolution, MAE was 2.82 g m−2.
Method / resource At 0.02 m resolution, RPD was 2.65.
Method / resource At 0.05 m resolution, R2 was 0.92.
Method / resource At 0.05 m resolution, RMSE was 3.17 g m−2.
Method / resource At 0.05 m resolution, MAE was 2.45 g m−2.
Method / resource At 0.05 m resolution, RPD was 2.86.
Method / resource Upscaling the 0.01 m model to 0.02 m images gave R2 0.84.
Method / resource Downscaling the 0.05 m model to 0.01 m images gave R2 0.86.
Method / resource Downscaling the 0.05 m model to 0.02 m images gave R2 0.83.
Method / resource Spatial resolution affected texture features more than spectral features.
Association Resolution effects on performance and transferability decreased as irrigated plant wetness increased.
Method / resource All tested resolutions achieved good winter wheat nitrogen prediction and transferability.

Method / resourceobservedoriginal resultmeasured

Claim · model-written summary

Downscaling the 0.05 m model to 0.01 m images gave R2 0.86.

Taxon: Winter WheatTriticum aestivumt0
Outcome: R2e0
Value: 0.86e0
Qualifier: 0.01 me0

Verbatim

e0
“For downscaling, the model established with the 0.05 m image features had a R2 of 0.86 and 0.83 when applied to images of 0.01 m and 0.02 m resolutions.”
✓ located verbatim in the source · abstract, characters 1303–1455

t0
“Winter Wheat”
✓ located verbatim in the source · title, characters 116–128

Title

Effects of the Spatial Resolution of UAV Images on the Prediction and Transferability of Nitrogen Content Model for Winter Wheat

Yan Guo; Jia He; Jingyi Huang; et al. Drones 2022. doi:10.3390/drones6100299

Other claims from this paper (18)

Method / resource At 0.01 m resolution, nitrogen prediction R2 was 0.84.
Method / resource At 0.01 m resolution, RMSE was 4.57 g m−2.
Method / resource At 0.01 m resolution, MAE was 2.50 g m−2.
Method / resource At 0.01 m resolution, RPD was 2.34.
Method / resource At 0.02 m resolution, R2 was 0.86.
Method / resource At 0.02 m resolution, RMSE was 4.15 g m−2.
Method / resource At 0.02 m resolution, MAE was 2.82 g m−2.
Method / resource At 0.02 m resolution, RPD was 2.65.
Method / resource At 0.05 m resolution, R2 was 0.92.
Method / resource At 0.05 m resolution, RMSE was 3.17 g m−2.
Method / resource At 0.05 m resolution, MAE was 2.45 g m−2.
Method / resource At 0.05 m resolution, RPD was 2.86.
Method / resource Upscaling the 0.01 m model to 0.02 m images gave R2 0.84.
Method / resource Upscaling the 0.01 m model to 0.05 m images gave R2 0.89.
Method / resource Downscaling the 0.05 m model to 0.02 m images gave R2 0.83.
Method / resource Spatial resolution affected texture features more than spectral features.
Association Resolution effects on performance and transferability decreased as irrigated plant wetness increased.
Method / resource All tested resolutions achieved good winter wheat nitrogen prediction and transferability.

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Effects of Early Fruiting Branch Removal on Growth,Development and Senescence Processes in Transgenic Bt Cotton(Gossypium hirsutum L.) 19

Estimating Maize Leaf Area Index Using Multi-Source Features Derived from UAV Multispectral Imagery and Machine Learning Models 19

Statistical model for evolving a croplogging technique in banana 25 18

Implementation of machine learning for predicting maize crop yields using multiple linear regression and backward elimination / Stephen Gbenga Fashoto … [et al.] 18

Maize Leaf Area Index Estimation Based on Machine Learning Algorithm and Computer Vision 18

Effects of Leaf Potassium Content on Growth Dynamics of Muskmelon and Developing a Coupling Model of Nitrogen and Potassium 17

Effect of Drought Stress during Soybean R2–R6 Growth Stages on Sucrose Metabolism in Leaf and Seed 17

Identification of SCAR and RAPD markers linked to Rz1 gene in Holly sugar beet using BSA and two genetic distance estimation methods 16

Effects of the Spatial Resolution of UAV Images on the Prediction and Transferability of Nitrogen Content Model for Winter Wheat 16