Method / resource 289,819 claims

Claim · model-written summary

Damage detection rates at the bottom of the grain collection box differed from actual damage rates by only 0.42% under full coverage.

Taxon

maizeZea mayst0

Outcome

damage detection ratese0

Value

0.42%e0

Qualifier

under full coveragee0

Comparator

actual damage ratese0

Verbatim

t0

“maize ear harvesters”

e0

“The damage detection rates for the bottom of the grain collection box differed from the actual damage rates by only 0.31% under partial coverage and 0.42% under full coverage”

Claim · model-written summary

The proposed system achieves up to 98% accuracy for crop recommendation.

Outcome

accuracy for crop recommendatione0

Value

98%e0

Verbatim

e0

“The system achieves up to 98% accuracy for crop recommendation”

Claim · model-written summary

Second-order polynomial models fitted the nutrient accumulation of hydroponic blueberry with adjusted R2 of at least 0.94, revealing dynamic uptake patterns.

Taxon

Vaccinium corymbosumVaccinium corymbosumt0

Outcome

R²ₐdⱼe0

Value

≥ 0.94e0

Verbatim

t0

“blueberry (Vaccinium corymbosum L.)”

e0

“Second-order polynomial models (R²ₐdⱼ ≥ 0.94) revealed dynamic uptake patterns”

Claim · model-written summary

A molecular marker designed from Zm00001d006212 variants showed that plants with the target band had significantly larger leaf angle than plants without it.

Taxon

maizeZea mayst0

Outcome

LAe0

Direction

largere0

Comparator

those withoute0

Verbatim

t0

“maize”

e0

“plants with the target band had significantly larger LA than those without”

Claim · model-written summary

The proposed image-based IoT irrigation system is presented as enabling more accurate and adaptive water usage than conventional irrigation systems.

Outcome

water usagee0

Comparator

conventional irrigation systemse0

Verbatim

e0

“This approach enables more accurate and adaptive water usage compared to conventional irrigation systems”

Claim · model-written summary

The vision system reached a fruit detection precision of 96.9% in greenhouse trials.

Outcome

fruit detection precisione0

Value

96.9%e0

Setting

practical greenhouse conditionse0

Verbatim

e0

“Experimental trials conducted under practical greenhouse conditions demonstrated a fruit detection precision of 96.9%, recall of 93.5%, and mean Intersection-over-Union of 79.2%”

Claim · model-written summary

The vision system reached a recall of 93.5% in greenhouse trials.

Outcome

recalle0

Value

93.5%e0

Setting

practical greenhouse conditionse0

Verbatim

e0

“Experimental trials conducted under practical greenhouse conditions demonstrated a fruit detection precision of 96.9%, recall of 93.5%, and mean Intersection-over-Union of 79.2%”

Claim · model-written summary

The vision system reached a mean Intersection-over-Union of 79.2% in greenhouse trials.

Outcome

mean Intersection-over-Unione0

Value

79.2%e0

Setting

practical greenhouse conditionse0

Verbatim

e0

“Experimental trials conducted under practical greenhouse conditions demonstrated a fruit detection precision of 96.9%, recall of 93.5%, and mean Intersection-over-Union of 79.2%”

Claim · model-written summary

The robotic platform achieved an overall harvesting success rate of 78.5%.

Outcome

overall harvesting success ratee0

Value

78.5%e0

Verbatim

e0

“The robotic platform achieved an overall harvesting success rate of 78.5%”

Claim · model-written summary

The harvesting success rate reached 85% for unobstructed fruits.

Outcome

harvesting success ratee0

Value

85%e0

Qualifier

unobstructed fruitse0

Verbatim

e0

“overall harvesting success rate of 78.5%, reaching 85% for unobstructed fruits”