Hyperspectral imaging has become one of the most
popular technologies in plant phenotyping because it can efficiently and
accurately predict numerous plant physiological features such as plant biomass,
leaf moisture content, and chlorophyll content. Various hyperspectral imaging systems
have been deployed in both greenhouse and field phenotyping activities. However,
the hyperspectral imaging quality is severely affected by the continuously
changing environmental conditions such as cloud cover, temperature and wind
speed that induce noise in plant spectral data. Eliminating these environmental
effects to improve imaging quality is critically important. In this thesis, two
approaches were taken to address the imaging noise issue in greenhouse and field
separately. First,
a computational simulation model was built to simulate the greenhouse
microclimate changes (such as the temperature and radiation distributions)
through a 24-hour cycle in a research greenhouse. The simulated results were
used to optimize the movement of an automated conveyor in the greenhouse: the
plants were shuffled with the conveyor system with optimized frequency and
distance to provide uniform growing conditions such as
temperature and lighting intensity for each individual plant. The results
showed the variance of the plants’ phenotyping feature measurements decreased significantly
(i.e., by up to 83% in plant canopy size) in this conveyor greenhouse. Secondly,
the environmental effects (i.e., sun radiation) on <a>aerial
</a>hyperspectral images in field plant phenotyping were investigated and
modeled. <a>An artificial neural network (ANN) method was
proposed to model the relationship between the image variation and
environmental changes. Before the 2019 field test, a gantry system was designed
and constructed to repeatedly collect time-series hyperspectral images with 2.5
minutes intervals of the corn plants under varying environmental conditions, which
included sun radiation, solar zenith angle, diurnal time, humidity, temperature
and wind speed. Over 8,000 hyperspectral images of </a>corn (<i>Zea mays </i>L.) were collected with
synchronized environmental data throughout the 2019 growing season. The models trained with
the proposed ANN method were able to accurately predict the variations in
imaging results (i.e., 82.3% for NDVI) caused by the changing environments. Thus,
the ANN method can be used by remote sensing professionals to adjust or correct
raw imaging data for changing environments to improve plant characterization.
| Identifer | oai:union.ndltd.org:purdue.edu/oai:figshare.com:article/12800675 |
| Date | 14 August 2020 |
| Creators | Dongdong Ma (9224231) |
| Source Sets | Purdue University |
| Detected Language | English |
| Type | Text, Thesis |
| Relation | https://figshare.com/articles/thesis/Ameliorating_Environmental_Effects_on_Hyperspectral_Images_for_Improved_Phenotyping_in_Greenhouse_and_Field_Conditions/12800675 |
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