Search claims · PlantClaims

Associationobservedoriginal resultnegativemeasured

Claim · model-written summary

Wheat spikes decreased the accuracy of LWC estimation from spectral reflectance.

Taxon: WheatTriticum aestivumt0
Treatment: wheat spikese3
Outcome: accuracy of LWC estimatione3
Direction: decreasede3

Verbatim

t0
“Wheat Canopies”
✓ located verbatim in the source · title, characters 62–76

e3
“The effects of wheat spikes on spectral reflectance mainly occurred in near infrared to shortwave infrared regions, which then decreased the accuracy of LWC estimation”
✓ located verbatim in the source · abstract, characters 1146–1313

Title

Estimating Vertical Distribution of Leaf Water Content within Wheat Canopies after Head Emergence

Weiping Kong; Wenjiang Huang; Lingling Ma; et al. Remote Sensing 2021. doi:10.3390/rs13204125

Other claims from this paper (12)

Observation Leaf water content in wheat plants was highest at the middle layer of the canopy.
Association Middle-layer LWC shows a significant linear correlation with upper-layer LWC (r ≥ 0.92).
Association Middle-layer LWC shows a significant linear correlation with bottom-layer LWC (r ≥ 0.92).
Method / resource Spectral indices at the middle layer outperformed the other two layers in LWC assessment.
Association Spectral indices at the middle layer were less susceptible to wheat spikes effects.
Method / resource The narrow-band WI-4 index tracked changes in middle-layer LWC with R2 = 0.82.
Method / resource The narrow-band NDWSI-4 index tracked changes in middle-layer LWC with R2 = 0.84.
Method / resource Indirect induction models based on WI-4 and NDWSI-4 were more effective than models from the conventional direct estimation method.
Method / resource The indirect induction model based on WI-4 reached R2 of 0.78 for upper-layer LWC estimation.
Method / resource The indirect induction model based on NDWSI-4 reached R2 of 0.81 for upper-layer LWC estimation.
Method / resource The indirect induction model based on WI-4 reached R2 of 0.75 for bottom-layer LWC estimation.
Method / resource The indirect induction model based on NDWSI-4 reached R2 of 0.74 for bottom-layer LWC estimation.

Method / resourceobservedoriginal resultpositivemeasured

Claim · model-written summary

Using full wavelength data gave higher accuracy in watermelon LWC estimation than spectral indices.

Taxon: watermelonCitrullus lanatuse0
Treatment: full wavelength datae3
Outcome: accuracy in LWC estimatione3
Direction: highere3
Comparator: spectral indicese4

Verbatim

e0
“Water is a crucial element for the growth of watermelon plants”
✓ located verbatim in the source · abstract, characters 0–62

e3
“using full wavelength data achieved higher accuracy in LWC estimation”
✓ located verbatim in the source · abstract, characters 1144–1213

e4
“Models for estimating LWC were developed using both spectral indices and full wavelength data”
✓ located verbatim in the source · abstract, characters 953–1046

Title

Evaluation of Leaf Water Content in Watermelon Based on Hyperspectral Reflectance

Dan Wu; Penghui Wang; Bing Chen; et al. Water 2025. doi:10.3390/w17081142

Other claims from this paper (4)

Method / resourceobservedoriginal resultpositivesignificant (as stated)measured

Claim · model-written summary

Ensemble learning significantly outperformed traditional methods in model accuracy for watermelon LWC estimation.

Taxon: watermelonCitrullus lanatuse0
Treatment: Ensemble learninge6
Outcome: model accuracye6
Direction: outperformse6
Comparator: traditional methodse6

Verbatim

e0
“Water is a crucial element for the growth of watermelon plants”
✓ located verbatim in the source · abstract, characters 0–62

e6
“Ensemble learning significantly outperforms traditional methods, providing a substantial improvement in model accuracy”
✓ located verbatim in the source · abstract, characters 1468–1586

Title

Evaluation of Leaf Water Content in Watermelon Based on Hyperspectral Reflectance

Dan Wu; Penghui Wang; Bing Chen; et al. Water 2025. doi:10.3390/w17081142

Other claims from this paper (4)

Associationauthor interpretationoriginal resultpositivemeasured

Claim · model-written summary

Using GRA first improved the estimation accuracy of leaf water content in winter wheat.

Taxon: winter wheatTriticum aestivume0
Treatment: using GRA firstlye3
Outcome: estimation accuracy of LWCe3
Direction: improvede3

Verbatim

e0
“the precision of estimating LWC in winter wheat”
✓ located verbatim in the source · abstract, characters 164–211

e3
“the estimation accuracy of LWC could be improved by using GRA firstly and then by using PLS and SRM-PLS”
✓ located verbatim in the source · abstract, characters 1389–1492

Title

Rmote sensing of leaf water content for winter wheat using grey relational analysis (GRA), stepwise regression method (SRM) and partial least squares (PLS)

Xiuliang Jin; Xinguang Xu 2012 First International Conference on Agro- Geoinformatics (Agro-Geoinformatics) 2012. doi:10.1109/agro-geoinformatics.2012.6311706

Other claims from this paper (5)

Method / resource PLS estimated leaf water content of winter wheat at anthesis with a value of 0.63.
Method / resource SRM-PLS estimated leaf water content of winter wheat at anthesis with a value of 0.46.
Method / resource In validation with 2009 data, the PLS model had a root mean square error of 2.6 %.
Method / resource In validation with 2009 data, the SRM-PLS model had a root mean square error of 3.12 %.
Association The relationships between the first five water vegetation indices and leaf water content were stable under GRA.

Method / resourceobservedoriginal resultpositivemeasured

Claim · model-written summary

The proposed replacement and constrained fusion framework improved leaf water estimation accuracy.

Taxon: winter wheatTriticum aestivumt0
Outcome: LWC estimation accuracye0
Direction: improvede0

Not reportedComparatorValue

Verbatim

e0
“The proposed multi-preprocessing representation replacement and constrained fusion framework improved LWC estimation accuracy”
✓ located verbatim in the source · abstract, characters 2147–2272

t0
“winter wheat leaf water content”
✓ located verbatim in the source · title, characters 93–124

t1
“winter wheat leaf water content”
✓ located verbatim in the source · title, characters 93–124

Title

Feature replacement and fusion enhance the accuracy of canopy spectral monitoring models for winter wheat leaf water content

Zhigang Wang; Sha Yang; Qing Liang; et al. International Journal of Applied Earth Observation and Geoinformation 2026. doi:10.1016/j.jag.2026.105110

Other claims from this paper (6)

Method / resource First derivative preprocessing, IVSO selection and BPNN modeling yielded the best leaf water monitoring results.
Method / resource IVSO fusion features gave the highest leaf water prediction accuracy.
Method / resource Band replacement improved leaf water model robustness.
Method / resource Band fusion improved leaf water model robustness.
Method / resource Band replacement addressed spectral sensitivity issues.
Method / resource Band fusion addressed spectral sensitivity issues.

Method / resourceobservedoriginal resultpositivemeasured

Claim · model-written summary

The random forest model outperformed SVR in estimating leaf water content (LWC), with R2 reaching 0.94.

Taxon: MaizeZea mayst0
Outcome: estimation accuracy R2 valuese0
Direction: outperformede0
Value: 0.94e0
Qualifier: LWCe0
Comparator: SVRe0

Verbatim

e0
“RF outperformed SVR in predicting agricultural traits (SPAD, LWC, and LNC), with estimation accuracy R2 values reaching 0.92, 0.94, and 0.95, respectively”
✓ located verbatim in the source · abstract, characters 879–1033

t0
“Water and Fertilizer Diagnosis of Maize”
✓ located verbatim in the source · title, characters 12–51

Title

Research on Water and Fertilizer Diagnosis of Maize Using Visible–Near-Infrared Hyperspectral Technology

Na Ta; Yanliang Li; Xiaofang Yu; et al. Agriculture 2025. doi:10.3390/agriculture16010084

Other claims from this paper (5)

Intervention effect Reducing nitrogen by 10% (N3) had no significant effect on the physiological indicators of maize.
Intervention effect Reducing irrigation by 10% (W3) led to significant differences in the physiological indicators of maize, with no direction stated.
Intervention effect First- and second-derivative transformations of the spectral data enhanced the correlation with agricultural traits in maize.
Method / resource The random forest model outperformed SVR in estimating SPAD, with R2 reaching 0.92.
Method / resource The random forest model outperformed SVR in estimating leaf nitrogen content (LNC), with R2 reaching 0.95.

Method / resourceauthor interpretationoriginal resultpositivemeasured

Claim · model-written summary

Accounting for leaf water content may enhance SPAD estimation accuracy.

Taxon: riceOryza sativat0
Outcome: accuracy of SPAD estimatione0
Direction: enhancede0
Qualifier: may be enhancede0

Not reportedComparatorValue

Verbatim

e0
“By taking into account the influence of leaf water content (LWC), the accuracy of SPAD estimation may be enhanced”
✓ located verbatim in the source · abstract, characters 1219–1332

t0
“rice leaves”
✓ located verbatim in the source · title, characters 47–58

Title

Improving SPAD spectral estimation accuracy of rice leaves by considering the effect of leaf water content

Ziyang Yu; Xinle Zhang; Huanjun Liu; et al. Crop Science 2022. doi:10.1002/csc2.20809

Other claims from this paper (3)

Method / resourceobservedoriginal resultpositivemeasured

Claim · model-written summary

ERT-x-Lw estimated leaf water content more accurately than DVI.

Taxon: wheatTriticum aestivumt0
Outcome: accuracy of the LWC estimation modele0
Direction: highere0
Comparator: DVI (R1185, R1307)e0

Verbatim

e0
“the accuracy of the LWC estimation model constructed by ERT-x-Lw was higher than that of DVI (R1185, R1307).”
✓ located verbatim in the source · abstract, characters 1755–1863

t0
“winter wheat”
✓ located verbatim in the source · title, characters 84–96

Title

Comparison of new hyperspectral index and machine learning models for prediction of winter wheat leaf water content

Juanjuan Zhang; Wen Zhang; Shuping Xiong; et al. Plant Methods 2021. doi:10.1186/s13007-021-00737-2

Other claims from this paper (4)

Method / resourceobservedoriginal resultpositivemeasured

Claim · model-written summary

Combining multiple data sources improved rice leaf water content model accuracy by 6–44% against a single ND index.

Taxon: riceOryza sativae0
Outcome: model accuracy of rice LWC estimatione0
Direction: improvede0
Value: 6–44%e0
Comparator: single spectral index normalized difference index (ND)e0

Verbatim

e0
“model accuracy of rice LWC estimation after combining data from multiple sources improved by 6–44% compared to the accuracy of a single spectral index normalized difference index (ND)”
✓ located verbatim in the source · abstract, characters 1015–1198

Title

Improving the prediction performance of leaf water content by coupling multi-source data with machine learning in rice (Oryza sativa L.)

Xuenan Zhang; Haocong Xu; Yehong She; et al. Plant Methods 2024. doi:10.1186/s13007-024-01168-5

Other claims from this paper (1)

Method / resource A GBDT model using ND and CWSI produced optimal rice leaf water prediction accuracy with R2 of 0.86 and RMSE of 0.01.

Method / resourceobservedoriginal resultpositivemeasured

Claim · model-written summary

The proposed SWIR vegetation index estimated leaf water content with an accuracy of 0.0046 g cm−2, better than traditional NDWI.

Taxon: plante0
Outcome: accuracy of LWC estimatione2
Direction: bettere2
Value: 0.0046 g cm−2e2
Qualifier: root mean square error equalled to 0.0104 g cm−2e2
Comparator: traditional NDWIe2

Verbatim

e0
“the spectral characteristics of the plant in shortwave infrared (SWIR) domain”
✓ located verbatim in the source · abstract, characters 375–452

e2
“The results showed that the accuracy of LWC estimation from this index can reach 0.0046 g cm−2, which is better than that from traditional NDWI with root mean square error equalled to 0.0104 g cm−2”
✓ located verbatim in the source · abstract, characters 899–1096

Title

Retrieval of leaf water content from remotely sensed data using a vegetation index model constructed with shortwave infrared reflectances

Ziyang Zhang; Bo-Hui Tang; Zhao-Liang Li International Journal of Remote Sensing 2018. doi:10.1080/01431161.2018.1471553

Other claims from this paper (1)

Method / resource Leaf water content retrieved from both indices was insensitive to band width and random noise.

“LWC estimation accuracy”

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