Abstract
Evaluating crop health and forecasting yields in the early stages are crucial for effective crop and market management during periods of biotic stress for both farmers and policymakers. Field experiments were conducted during 2017-18 and 2018-19 with objective to evaluate the effect of yellow rust on various biophysical parameters of 24 wheat cultivars, with varying levels of resistance to yellow rust and to develop machine learning (ML) models with improved accuracy for predicting yield by integrating thermal and RGB indices with crucial plant biophysical parameters. Results revealed that as the level of rust increased, so did the canopy temperature and there was a significant decrease in crop photosynthesis, transpiration, stomatal conductance, leaf area index, membrane stability index, relative leaf water content, and normalized difference vegetation index due to rust, and the reductions were directly correlated with levels of rust severity. The yield reduction in moderate resistant, low resistant and susceptible cultivars as compared to resistant cultivars, varied from 15.9-16.9%, 28.6-34.4% and 59-61.1%, respectively. The ML models were able to provide relatively accurate early yield estimates, with the accuracy increasing as the harvest approached. The yield prediction performance of the different ML models varied with the stage of the crop growth. Based on the validation output of different ML models, Cubist, PLS, and SpikeSlab models were found to be effective in predicting the wheat yield at an early stage (55-60 days after sowing) of crop growth. The KNN, Cubist, SLR, RF, SpikeSlab, XGB, GPR and PLS models were proved to be more useful in predicting the crop yield at the middle stage (70 days after sowing) of the crop, while RF, SpikeSlab, KNN, Cubist, ELNET, GPR, SLR, XGB and MARS models were found good to predict the crop yield at late stage (80 days after sowing). The study quantified the impact of different levels of rust severity on crop biophysical parameters and demonstrated the usefulness of remote sensing and biophysical parameters data integration using machine-learning models for early yield prediction under biotically stressed conditions.
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Claims 12
Claim · model-written summary
Canopy temperature increased as wheat rust increased.
- Taxon
- wheat
Triticum aestivume1 - Interactor
- ruste0pathogen
- Outcome
- canopy temperaturee0
- Direction
- increasede0
Organisms pictured · Representative pictures of the matched species, not figures from the paper.
Verbatim
e0“as the level of rust increased, so did the canopy temperature”
e1“24 wheat cultivars”
Claim · model-written summary
Yellow rust significantly decreased wheat photosynthesis, transpiration, and stomatal conductance.
- Taxon
- wheatTriticum aestivume1
- Interactor
- ruste0pathogen
- Outcome
- crop photosynthesis, transpiration, stomatal conductancee0
- Direction
- decreasee0
Organisms pictured · Representative pictures of the matched species, not figures from the paper.
Verbatim
e0“there was a significant decrease in crop photosynthesis, transpiration, stomatal conductance, leaf area index, membrane stability index, relative leaf water content, and normalized difference vegetation index due to rust”
e1“24 wheat cultivars”
Claim · model-written summary
Yellow rust significantly decreased wheat leaf area index, membrane stability index, and relative leaf water content.
- Taxon
- wheatTriticum aestivume1
- Interactor
- ruste0pathogen
- Outcome
- leaf area index, membrane stability index, relative leaf water contente0
- Direction
- decreasee0
Organisms pictured · Representative pictures of the matched species, not figures from the paper.
Verbatim
e0“there was a significant decrease in crop photosynthesis, transpiration, stomatal conductance, leaf area index, membrane stability index, relative leaf water content, and normalized difference vegetation index due to rust”
e1“24 wheat cultivars”
Claim · model-written summary
Yellow rust significantly decreased wheat normalized difference vegetation index.
- Taxon
- wheatTriticum aestivume1
- Interactor
- ruste0pathogen
- Outcome
- normalized difference vegetation indexe0
- Direction
- decreasee0
Organisms pictured · Representative pictures of the matched species, not figures from the paper.
Verbatim
e0“there was a significant decrease in crop photosynthesis, transpiration, stomatal conductance, leaf area index, membrane stability index, relative leaf water content, and normalized difference vegetation index due to rust”
e1“24 wheat cultivars”
Claim · model-written summary
Reductions in wheat physiological traits were directly correlated with rust severity.
- Taxon
- wheatTriticum aestivume1
- Interactor
- ruste0pathogen
- Outcome
- the reductionse0
- Direction
- directly correlatede0
Organisms pictured · Representative pictures of the matched species, not figures from the paper.
Verbatim
e0“the reductions were directly correlated with levels of rust severity”
e1“24 wheat cultivars”
Claim · model-written summary
Moderately resistant cultivars lost 15.9-16.9% yield relative to resistant cultivars.
- Taxon
- wheatTriticum aestivume1
- Material
- moderate resistante0
- Outcome
- yielde0
- Direction
- reductione0
- Value
- 15.9-16.9%e0
- Comparator
- resistant cultivarse0
Organisms pictured · Representative pictures of the matched species, not figures from the paper.
Verbatim
e0“The yield reduction in moderate resistant, low resistant and susceptible cultivars as compared to resistant cultivars, varied from 15.9-16.9%, 28.6-34.4% and 59-61.1%, respectively.”
e1“24 wheat cultivars”
Claim · model-written summary
Low resistant cultivars lost 28.6-34.4% yield relative to resistant cultivars.
- Taxon
- wheatTriticum aestivume1
- Material
- low resistante0
- Outcome
- yielde0
- Direction
- reductione0
- Value
- 28.6-34.4%e0
- Comparator
- resistant cultivarse0
Organisms pictured · Representative pictures of the matched species, not figures from the paper.
Verbatim
e0“The yield reduction in moderate resistant, low resistant and susceptible cultivars as compared to resistant cultivars, varied from 15.9-16.9%, 28.6-34.4% and 59-61.1%, respectively.”
e1“24 wheat cultivars”
Claim · model-written summary
Susceptible cultivars lost 59-61.1% yield relative to resistant cultivars.
- Taxon
- wheatTriticum aestivume1
- Material
- susceptible cultivarse0
- Outcome
- yielde0
- Direction
- reductione0
- Value
- 59-61.1%e0
- Comparator
- resistant cultivarse0
Organisms pictured · Representative pictures of the matched species, not figures from the paper.
Verbatim
e0“The yield reduction in moderate resistant, low resistant and susceptible cultivars as compared to resistant cultivars, varied from 15.9-16.9%, 28.6-34.4% and 59-61.1%, respectively.”
e1“24 wheat cultivars”
Claim · model-written summary
Machine learning yield estimates became more accurate nearer harvest.
- Taxon
- wheatTriticum aestivume1
- Outcome
- accuracye0
- Direction
- increasinge0
Not reportedComparatorValue
Organisms pictured · Representative pictures of the matched species, not figures from the paper.
Verbatim
e0“The ML models were able to provide relatively accurate early yield estimates, with the accuracy increasing as the harvest approached.”
e1“24 wheat cultivars”
Claim · model-written summary
Cubist, PLS, and SpikeSlab predicted wheat yield effectively at 55-60 days after sowing.
- Taxon
- wheatTriticum aestivume0
- Organ / stage
- early stagee0
- Outcome
- predicting the wheat yielde0
- Value
- 55-60 days after sowinge0
Organisms pictured · Representative pictures of the matched species, not figures from the paper.
Verbatim
e0“Cubist, PLS, and SpikeSlab models were found to be effective in predicting the wheat yield at an early stage (55-60 days after sowing) of crop growth.”
Claim · model-written summary
KNN, Cubist, SLR, RF, SpikeSlab, XGB, GPR, and PLS were useful for wheat yield prediction at 70 days after sowing.
- Taxon
- wheatTriticum aestivume1
- Organ / stage
- middle stagee0
- Outcome
- predicting the crop yielde0
- Value
- 70 days after sowinge0
Organisms pictured · Representative pictures of the matched species, not figures from the paper.
Verbatim
e0“The KNN, Cubist, SLR, RF, SpikeSlab, XGB, GPR and PLS models were proved to be more useful in predicting the crop yield at the middle stage (70 days after sowing) of the crop”
e1“24 wheat cultivars”
Claim · model-written summary
RF, SpikeSlab, KNN, Cubist, ELNET, GPR, SLR, XGB, and MARS predicted wheat yield well at 80 days after sowing.
- Taxon
- wheatTriticum aestivume1
- Organ / stage
- late stagee0
- Outcome
- predict the crop yielde0
- Value
- 80 days after sowinge0
Organisms pictured · Representative pictures of the matched species, not figures from the paper.
Verbatim
e0“RF, SpikeSlab, KNN, Cubist, ELNET, GPR, SLR, XGB and MARS models were found good to predict the crop yield at late stage (80 days after sowing).”
e1“24 wheat cultivars”