prikaz prve stranice dokumenta Estimating nitrogen concentration in winter wheat leaves using multispectral imaging
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master's thesis
Estimating nitrogen concentration in winter wheat leaves using multispectral imaging
Zagreb: University of Zagreb, Faculty of Agriculture, 2024. urn:nbn:hr:204:785846
Wolsing, Clemens Kaspar Jakob
University of Zagreb
Faculty of Agriculture

Institutional repository: Repository Faculty of Agriculture University of Zagreb

Cite this document

Wolsing, C. K. J. (2024). Estimating nitrogen concentration in winter wheat leaves using multispectral imaging (Master's thesis). Zagreb: University of Zagreb, Faculty of Agriculture. Retrieved from https://urn.nsk.hr/urn:nbn:hr:204:785846

Wolsing, Clemens Kaspar Jakob. "Estimating nitrogen concentration in winter wheat leaves using multispectral imaging." Master's thesis, University of Zagreb, Faculty of Agriculture, 2024. https://urn.nsk.hr/urn:nbn:hr:204:785846

Wolsing, Clemens Kaspar Jakob. "Estimating nitrogen concentration in winter wheat leaves using multispectral imaging." Master's thesis, University of Zagreb, Faculty of Agriculture, 2024. https://urn.nsk.hr/urn:nbn:hr:204:785846

Wolsing, C. K. J. (2024). 'Estimating nitrogen concentration in winter wheat leaves using multispectral imaging', Master's thesis, University of Zagreb, Faculty of Agriculture, accessed 03 April 2025, https://urn.nsk.hr/urn:nbn:hr:204:785846

Wolsing CKJ. Estimating nitrogen concentration in winter wheat leaves using multispectral imaging [Master's thesis]. Zagreb: University of Zagreb, Faculty of Agriculture; 2024 [cited 2025 April 03] Available at: https://urn.nsk.hr/urn:nbn:hr:204:785846

C. K. J. Wolsing, "Estimating nitrogen concentration in winter wheat leaves using multispectral imaging", Master's thesis, University of Zagreb, Faculty of Agriculture, Zagreb, 2024. Available at: https://urn.nsk.hr/urn:nbn:hr:204:785846

Metadata
TitleEstimating nitrogen concentration in winter wheat leaves using multispectral imaging
AuthorClemens Kaspar Jakob Wolsing
MentorBoris Lazarević (mentor)
Committee memberBoris Lazarević (predsjednik povjerenstva)
Committee memberToni Safner (član povjerenstva)
Committee memberMarko Petek (član povjerenstva)
GranterUniversity of Zagreb
Faculty of Agriculture
Zagreb
Defense date and country2024-05-02, Croatia
Scientific / art field,
discipline and subdiscipline		BIOTECHNICAL SCIENCES
Agronomy
Abstract
Nitrogen (N) fertilization is key for maintaining high and stable yields. However, the nitrogen use efficiency (NUE) of winter wheat (Triticum aestivum L.) is only 30-50%. To ensure sufficient yield, some farmers overfertilize their fields. The excess N causes pollution and an increase in costs. For optimal fertilization, the plant N status should be monitored. Most of the existing methods are destructive and time-consuming. Multispectral imaging is a promising technology to estimate the N... More content of crops by using the leaf spectral reflectance. This study aims to assess the efficiency of multispectral imaging in the determination of N content in winter wheat leaves. A field trial was set up with a split-plot design in four replications. N-treatments, (80 kg N/ha), 300kg NPK applied at seeding + 130 kg of Urea (46% N) and N+ included additional 120kg calcium ammonium nitrate (27%N) split into three applications, representing a main plot. The subplot was represented as ten different wheat varieties. During a heading stage (GS50) ten flag leaves have been collected per N-treatment x wheat variety x replication. Multispectral images in six different wavelengths were acquired, and different vegetation indexes were calculated for each leaf. Total leaf N was determined using the Kjeldahl method. Data were analysed using a general linear regression model with multispectral data as a predictor and N leaf content as a response. In addition, machine learning techniques were used: Multiple Linear Regression, Random Forest Algorithm, Partial Least Squared Regression and Support Vector Regression as well Lasso-Regression. The Highest relationship for leaf N content was found with an Anthocyanin Index (R2=0,779), Excess Green Index, (R2= 0,726 ), and Chlorophyll Index (R2=0,639) for Linear Regression. The best model to predict leaf nitrogen concentration was the Multiple Linear Regression with R2= 0,867 but it did not show any significant independent variables. The rest of the machine learning algorithms showed similar results: the Random Forest Algorithm (R2= 0,762), Partial Least Squared Regression (R2= 0,763), and Support Vector Regression (R2= 0,747). This study shows that N content in leaves can be estimated using multispectral imaging and that ML can help to deal with high-dimensional datasets. Nevertheless, further data must be provided to develop a more accurate model. Less
Keywords
Languageenglish
URN:NBNurn:nbn:hr:204:785846
Study programmeTitle: Environment, Agriculture and Resource Management
Study programme type: university
Study level: graduate
Academic / professional title: Master of Engineering in Agriculture (Master of Engineering in Agriculture)
Type of resourceText
File originBorn digital
Access conditionsAccess restricted to students and staff of home institution
Terms of useLicence In copyright icon
Created on2024-05-20 07:07:53
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