Abstract
Non-destructive nutrition diagnosis provides effective technological support for agricultural sustainability. According to the plant nutrition mechanism, leaf characteristics displays different changing trends under nitrogen (N), phosphorus (P), and potassium (K) nutrition stress. In this study, the dynamic capture of rice leaf by scanning was used to research the changing regulation of leaf characteristics under nutrition stress. The leaf characteristics were extracted by mean value and regionprops functions in MATLAB, and the leaf dynamics were quantified by calculating the relative growth rate. Stepwise discriminant analysis and leave one out cross validation were applied to identify NPK deficiencies. The results indicated that leaves with N deficiency presented the lowest extension rate and the fastest wilt rate, followed by P and K deficiencies. During the identification, both morphological and color indices of the first incomplete leaf were effective indices for identification, but for the third fully expanded leaf, they were mainly color indices. Moreover, the first incomplete leaf had comparative advantage in early diagnosis (training accuracy 73.7%, validation accuracy 71.4% at the 26th day after transplantation), and the third fully expanded leaf generated higher accuracy at later stage. Overall, dynamic analysis expanded the application of leaf characteristics in identification, which contributes to improving the diagnostic effect.
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Claims 8
Claim · model-written summary
Leaves with N deficiency show a lower extension rate than leaves with P or K deficiency in rice.
- Taxon
- rice
Oryza sativae0 - Treatment
- N deficiencye1
- Outcome
- extension ratee1
- Direction
- loweste1
- Comparator
- P and K deficienciese1
Organisms pictured · Representative pictures of the matched species, not figures from the paper.
Verbatim
e0“the dynamic capture of rice leaf by scanning was used”
e1“leaves with N deficiency presented the lowest extension rate and the fastest wilt rate, followed by P and K deficiencies”
Claim · model-written summary
Leaves with N deficiency show the fastest wilt rate of the three nutrient deficiencies in rice.
- Taxon
- riceOryza sativae0
- Treatment
- N deficiencye1
- Outcome
- wilt ratee1
- Direction
- fasteste1
- Comparator
- P and K deficienciese1
Organisms pictured · Representative pictures of the matched species, not figures from the paper.
Verbatim
e0“the dynamic capture of rice leaf by scanning was used”
e1“leaves with N deficiency presented the lowest extension rate and the fastest wilt rate, followed by P and K deficiencies”
Claim · model-written summary
Morphological and colour indices of the first incomplete leaf are effective indices for identifying deficiencies.
- Taxon
- riceOryza sativae0
- Organ / stage
- first incomplete leafe1
- Outcome
- morphological and color indicese1
- Qualifier
- effective indices for identificatione1
Organisms pictured · Representative pictures of the matched species, not figures from the paper.
Verbatim
e0“the dynamic capture of rice leaf by scanning was used”
e1“both morphological and color indices of the first incomplete leaf were effective indices for identification”
Claim · model-written summary
For the third fully expanded leaf, the effective identification indices are mainly colour indices.
- Taxon
- riceOryza sativae0
- Organ / stage
- third fully expanded leafe1
- Outcome
- color indicese1
- Qualifier
- mainlye1
Organisms pictured · Representative pictures of the matched species, not figures from the paper.
Verbatim
e0“the dynamic capture of rice leaf by scanning was used”
e1“for the third fully expanded leaf, they were mainly color indices”
Claim · model-written summary
The first incomplete leaf reaches 73.7% training accuracy for NPK deficiency identification at the 26th day after transplantation.
- Taxon
- riceOryza sativae0
- Organ / stage
- first incomplete leafe1
- Outcome
- training accuracye1
- Value
- 73.7%e1
- Qualifier
- at the 26th day after transplantatione1
Organisms pictured · Representative pictures of the matched species, not figures from the paper.
Verbatim
e0“the dynamic capture of rice leaf by scanning was used”
e1“the first incomplete leaf had comparative advantage in early diagnosis (training accuracy 73.7%, validation accuracy 71.4% at the 26th day after transplantation)”
Claim · model-written summary
The first incomplete leaf reaches 71.4% validation accuracy for NPK deficiency identification at the 26th day after transplantation.
- Taxon
- riceOryza sativae0
- Organ / stage
- first incomplete leafe1
- Outcome
- validation accuracye1
- Value
- 71.4%e1
- Qualifier
- at the 26th day after transplantatione1
Organisms pictured · Representative pictures of the matched species, not figures from the paper.
Verbatim
e0“the dynamic capture of rice leaf by scanning was used”
e1“the first incomplete leaf had comparative advantage in early diagnosis (training accuracy 73.7%, validation accuracy 71.4% at the 26th day after transplantation)”
Claim · model-written summary
The third fully expanded leaf gives higher identification accuracy at a later stage than the first incomplete leaf.
- Taxon
- riceOryza sativae0
- Organ / stage
- third fully expanded leafe1
- Outcome
- accuracye1
- Direction
- highere1
- Qualifier
- at later stagee1
- Comparator
- first incomplete leafe2
Organisms pictured · Representative pictures of the matched species, not figures from the paper.
Verbatim
e0“the dynamic capture of rice leaf by scanning was used”
e1“the third fully expanded leaf generated higher accuracy at later stage”
e2“the first incomplete leaf had comparative advantage in early diagnosis”
Claim · model-written summary
Dynamic analysis of leaf characteristics improves the diagnostic effect for nutrient deficiency in rice.
- Taxon
- riceOryza sativae0
- Outcome
- diagnostic effecte1
- Direction
- improvinge1
Organisms pictured · Representative pictures of the matched species, not figures from the paper.
Verbatim
e0“the dynamic capture of rice leaf by scanning was used”
e1“Overall, dynamic analysis expanded the application of leaf characteristics in identification, which contributes to improving the diagnostic effect”
The same questions in other papers
nitrogen · disease · Oryza sativa (rice)Positive and negative results
38 papers in the same direction as this paper · 42 in the opposite direction · 1 reporting no difference from an alternative
This paper is about lacking the nutrient, so it counts the other way round here.
- negative
- negative
- negative
78 papers more in this group
See all 82 papers →Claims are grouped by organism, treatment and what was measured or targeted. A paper counts as positive when any of its claims in the group is positive.