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Measuring crop status using multivariate analysis of hyperspectral field reflectance with
Uppsala University, Interfaculty Units, Centre for Image Analysis. Uppsala University, Interfaculty Units, Centre for Image Analysis. Teknisk-naturvetenskapliga vetenskapsområdet, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis.
2005 (English)In: 5th European Conference on Precision Agriculture: Precision Agriculture ‘05, 2005, 1008- p.Conference paper (Refereed)
Abstract [en]

Using spectral reflectance to estimate crop status is a method suitable for developing sensors for site-specific agricultural applications. When developing spectral analysis methods the influence of different crop parameters on the spectral reflectance profile is important to know. The objective of this report was to present and evaluate a multivariate method for objective hyperspectral analysis in the examination of how different parts of the reflectance spectrum are affected by disease severity and above ground plant density. Data from two field experiments was used, fungal disease severity assessments in wheat 1998 and above ground plant density measurements 2003. The analysis method consisted of two steps: normalisation and classification. Using only 12% of the data as training data, the method resulted in the coefficients of determination (R²) of 94.3% for the disease severity data and 96.9% for the plant density data. The presented hyperspectral analysis method could also be used to extract spectral profiles of disease severity and plant density using the experimental data. In general two types of spectral profiles for both data sets were observed (1) a flattening of the green reflectance peak together with a general decrease in reflectance in the near infrared region and (2) a decrease of the shoulder of the near infrared reflectance plateau together with a general increase in the visible region between 550 and 750 nm.

Place, publisher, year, edition, pages
2005. 1008- p.
Keyword [en]
hyperspectral reflectance analysis, plant disease, plant density
National Category
Computer Vision and Robotics (Autonomous Systems)
URN: urn:nbn:se:uu:diva-71765ISBN: 9076998698OAI: oai:DiVA.org:uu-71765DiVA: diva2:99676
Available from: 2005-05-23 Created: 2005-05-23

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Hamid Muhammed, Hamed
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Centre for Image AnalysisComputerized Image Analysis
Computer Vision and Robotics (Autonomous Systems)

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