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Method / resourceobservedoriginal resultpositivemeasured

Claim · model-written summary

FISS classified mangoes into seven ripeness and decay grades.

Taxon
mangoesMangifera indicae0
Outcome
classified mangoes into seven gradese0
Value
seven gradese0

Verbatim

e0

“Practical applications The purpose of this study is to address the time‐consuming issues associated with traditional fruit grading methods by utilizing a homemade flexible impedance sensing system (FISS). A customized spiral slide system was innovatively designed in the study to simulate the grading process on a fruit assembly line with multimodal features for low power consumption and non‐destructive evaluation. FIS…” Show full quote

✓ located verbatim in the source · abstract, characters 1369–2486

Title

Non‐destructive grading technique for mangoes using a flexible impedance sensing system and YOLOv5s_CBAM

Wentao Huang; Yangfeng Wang; Yunpeng Wang; et al. Journal of Food Process Engineering 2024. doi:10.1111/jfpe.14631

Other claims from this paper (2)
Method / resourceobservedoriginal resultpositivemeasured

Claim · model-written summary

AquaCrop calibration NRMSE for wheat biomass production was 2-5.

Taxon
winter wheatTriticum aestivume0
Outcome
biomass productione0
Value
2-5e0
Qualifier
calibratione0
Setting
Fars Agricultural Research Station (Zarghan)e0

Verbatim

e0

“This study was conducted for evaluating AquaCrop model performance in prediction of grain yield and biomass production of winter wheat, under water stress at Fars Agricultural Research Station (Zarghan), during two years (2006-2008). The experiment was a randomized block, based on a split plot design with four replications. Main plots consisted of two irrigation treatments, namely I1 (nonstressed), and I2 (Post anthe…” Show full quote

✓ located verbatim in the source · abstract, characters 0–1191

Title

Evaluating AquaCrop Model Performance to Predict Grain Yield and Wheat Biomass, Under Water Stress

Ebrahim Amırı; A. Bahrani; Afshin Khorsand; et al. 2015

Other claims from this paper (4)
Method / resourceobservedoriginal resultpositivemeasured

Claim · model-written summary

AquaCrop validation NRMSE for wheat biomass production was 3-6 percent.

Taxon
winter wheatTriticum aestivume0
Outcome
biomass productione0
Value
3-6 percente0
Qualifier
validatione0
Setting
Fars Agricultural Research Station (Zarghan)e0

Verbatim

e0

“This study was conducted for evaluating AquaCrop model performance in prediction of grain yield and biomass production of winter wheat, under water stress at Fars Agricultural Research Station (Zarghan), during two years (2006-2008). The experiment was a randomized block, based on a split plot design with four replications. Main plots consisted of two irrigation treatments, namely I1 (nonstressed), and I2 (Post anthe…” Show full quote

✓ located verbatim in the source · abstract, characters 0–1191

Title

Evaluating AquaCrop Model Performance to Predict Grain Yield and Wheat Biomass, Under Water Stress

Ebrahim Amırı; A. Bahrani; Afshin Khorsand; et al. 2015

Other claims from this paper (4)
Method / resourceobservedoriginal resultpositivemeasured

Claim · model-written summary

AquaCrop calibration NRMSE for wheat predicted grain yield was 0.5-2.

Taxon
winter wheatTriticum aestivume0
Outcome
predicted grain yielde0
Value
0.5-2e0
Qualifier
calibratione0
Setting
Fars Agricultural Research Station (Zarghan)e0

Verbatim

e0

“This study was conducted for evaluating AquaCrop model performance in prediction of grain yield and biomass production of winter wheat, under water stress at Fars Agricultural Research Station (Zarghan), during two years (2006-2008). The experiment was a randomized block, based on a split plot design with four replications. Main plots consisted of two irrigation treatments, namely I1 (nonstressed), and I2 (Post anthe…” Show full quote

✓ located verbatim in the source · abstract, characters 0–1191

Title

Evaluating AquaCrop Model Performance to Predict Grain Yield and Wheat Biomass, Under Water Stress

Ebrahim Amırı; A. Bahrani; Afshin Khorsand; et al. 2015

Other claims from this paper (4)
Method / resourceobservedoriginal resultpositivemeasured

Claim · model-written summary

AquaCrop validation NRMSE for wheat predicted grain yield was 2-9 percent.

Taxon
winter wheatTriticum aestivume0
Outcome
predicted grain yielde0
Value
2-9 percente0
Qualifier
validatione0
Setting
Fars Agricultural Research Station (Zarghan)e0

Verbatim

e0

“This study was conducted for evaluating AquaCrop model performance in prediction of grain yield and biomass production of winter wheat, under water stress at Fars Agricultural Research Station (Zarghan), during two years (2006-2008). The experiment was a randomized block, based on a split plot design with four replications. Main plots consisted of two irrigation treatments, namely I1 (nonstressed), and I2 (Post anthe…” Show full quote

✓ located verbatim in the source · abstract, characters 0–1191

Title

Evaluating AquaCrop Model Performance to Predict Grain Yield and Wheat Biomass, Under Water Stress

Ebrahim Amırı; A. Bahrani; Afshin Khorsand; et al. 2015

Other claims from this paper (4)
Method / resourceobservedoriginal resultpositivemeasured

Claim · model-written summary

The smallest FISS chlorophyll retrieval RMSE was 0.201 mg/g, over 30% lower than non-imaging ASD.

Taxon
soybeanGlycine maxe0
Outcome
RMSEe0
Direction
reductione0
Value
0.201 mg/ge0
Qualifier
more than 30%e0
Comparator
non-imaging ASD spectrometere0

Verbatim

e0

“soybean leaves. The chlorophyll content was retrieved using a multiple linear regression (MLR), partial least squares (PLS) regression and support vector machine (SVM) regression. Our objective was to verify the performance of FISS in a quantitative spectral analysis through the estimation of chlorophyll content and to determine a proper quantitative spectral analysis method for processing FISS data. The results reve…” Show full quote

✓ located verbatim in the source · abstract, characters 177–1375

Title

Plant Leaf Chlorophyll Content Retrieval Based on a Field Imaging Spectroscopy System

Bo Liu; Yue-Min Yue; Ru Li; et al. Sensors 2014. doi:10.3390/s141019910

Other claims from this paper (3)

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