Name
Title | ||
|---|---|---|
Research Vertical Distribution Of Chlorophyll Content Of Wheat Leaves Using Imaging Hyperspectra |
Abstract | ||
|---|---|---|
Chlorophyll content is an important indicator for judging crop photosynthesis ability and monitoring growth status. Hyperspectral imaging is one of the hot spots in quantitative remote sensing research. As an image-spectrum merging technology, it could be used to explore and develop new methods for diagnosing of crop nutrition, diseases and insect pests. In this study, an auto-development pushbroom imaging spectrometer (PIS) was applied to measure the chlorophyll content of wheat leaves. The tested sites of spectrum and the chlorophyll content measured positions were on the same area of single leaf. Partial least square (PLS) regression was used to establish prediction models of chlorophyll content. The model accuracy of single leaf with values from different positions was evaluated; and the model accuracy of leaves from different layers was also studied. The results showed that the model of the leaf with 2, 4, 6 sites was better than that of 1, 3, 5 sites; those models of leaves from vertical levels were medium layer > upper layer > lower layer; the predicting accuracy of the whole layers was the highest. To sum up, as a new technology, hyperspectral imaging can be used to accurately monitor the crop growth situation on single leaf; and the theories would be founded to survey optimal position for measuring chlorophyll content and explore vertical distribution of crop nutrition, especially when leaves are suffered from shortage of element, diseases and insect pests. |
Year | DOI | Venue |
|---|---|---|
2012 | 10.1080/10798587.2008.10643315 | INTELLIGENT AUTOMATION AND SOFT COMPUTING |
Keywords | Field | DocType |
pushbroom imaging spectrometer, image-spectrum merging technology, PLS, leaf, chlorophyll content | Photosynthesis,Imaging spectrometer,Crop,Computer science,Hyperspectral imaging,Chlorophyll content,Artificial intelligence,Horticulture,Merge (version control),Machine learning | Journal |
Volume | Issue | ISSN |
18 | 8 | 1079-8587 |
Citations | PageRank | References |
2 | 0.91 | 0 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Dongyan Zhang | 1 | 6 | 5.81 |
| Xiu Wang | 2 | 3 | 4.10 |
| Wei Ma | 3 | 2 | 0.91 |
| J.-C. Zhao | 4 | 135 | 52.42 |