Measuring and modelling parameters from hyperspectral sensors for site-specific crop protection
Sammanfattning: This thesis sought to optimise systems for plant protection in precision agriculture through developing a field method for estimating crop status parameters from hyperspectral sensors, and an empirical model for estimating the required herbicide dose in different parts of the field. The hyperspectral reflectance measurements in the open field took the form of instantaneous spectra recording using an existing method called feature vector based analysis (FVBA), which was applied on disease severity. A new method called iterative normalisation based analysis (INBA) was developed and evaluated on disease severity and plant biomass. The methods revealed two different spectral signatures in both disease severity and plant density data. By concentrating the analysis on a 12% random subset of the hyperspectral field data, the unknown part of the data could be estimated with 94-97% coefficient of determination. The empirical model for site-specific weed control combined a model for weed competition and a dose response model. Comparisons of site-specific and conventional uniform spraying using model simulations showed that site-specific spraying with the uniform recommended dose resulted in 64% herbicide saving. Comparison with a uniform dose with equal weed control effect resulted in 36% herbicide saving. The methods developed in this thesis can be used to improve systems for site-specific plant protection in precision agriculture and to evaluate site-specific plant protection systems in relation to uniform spraying. Overall, this could be beneficial both for farm finances and for the environment.
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