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Non-Destructive Estimation of Leaf Area Index(LAI) Using Image Data

dc.contributor.advisorNoble, Scott
dc.contributor.committeeMemberHelgason, Warren
dc.contributor.committeeMemberMeda, Venkatesh
dc.creatorSediqi, Tansu
dc.date.accessioned2024-08-27T15:17:52Z
dc.date.available2024-08-27T15:17:52Z
dc.date.copyright2024
dc.date.created2024-07
dc.date.issued2024-08-27
dc.date.submittedJuly 2024
dc.date.updated2024-08-27T15:17:53Z
dc.description.abstractThis study investigated using image data to estimate the Leaf Area Index (LAI) for wheat (Triticale aestivum L.). The calculation of LAI involved the utilization of light interception, and the estimation process involves creating a model using metrics extracted from Near-Infrared (NIR) and visible light image data. Line quantum sensors were used to collect above- and below-canopy light measurements. Standard models were used to calculate LAI based on these measurements. To address the effects of the sun azimuth angle, corrections were applied to the LAI values, effectively eliminating its impact on the determinations. The assessment of canopy coverage used in this study involved NIR and visible light images. To estimate LAI non-destructively, linear regression models were constructed, incorporating a set of metrics from the mentioned imaging modalities and the date as a proxy for the maturity of the wheat canopy. The performance of these models was evaluated using root mean squared error (RMSE) and adjusted coefficient of determination (R2). Through a comparison of these models, it was concluded that LAI was best estimated using NIR images, rather than visible-light images. Moreover, the most effective model for the estimation of LAI included only canopy coverage and maturity proxy as input parameters. Height was not an important factor in this case. The dataset examined contained a set of wheat varieties. To improve the power of LAI estimation models, the study employed a clustering technique. Rather than using a single, generic model for LAI estimation across all varieties, the proposed clustering strategy involves clustering the dataset and developing a model for each distinct cluster. This departure from the one-size-fits-all approach generated more precise LAI estimations for each cluster, ultimately enhancing the overall predictive accuracy of the models. This not only enhanced comprehension of LAI dynamics but also underscored the significance of personalized strategies in agricultural research to achieve more precise and applicable results. However, there are certain limitations to consider. The clusters showed inconsistencies, indicating possible difficulties in putting them into practice. While the suggestion of clustering exists, the specific approach to resolving these inconsistencies remains uncertain.
dc.format.mimetypeapplication/pdf
dc.identifier.urihttps://hdl.handle.net/10388/15959
dc.language.isoen
dc.subjectLeaf Area Index (LAI)
dc.subjectLight Interception
dc.subjectLine Quantum Sensors
dc.subjectSun Azimuth Angle
dc.subjectCanopy Coverage
dc.subjectLinear Regression Models
dc.titleNon-Destructive Estimation of Leaf Area Index(LAI) Using Image Data
dc.typeThesis
dc.type.materialtext
thesis.degree.departmentMechanical Engineering
thesis.degree.disciplineMechanical Engineering
thesis.degree.grantorUniversity of Saskatchewan
thesis.degree.levelMasters
thesis.degree.nameMaster of Science (M.Sc.)

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