engleski

Proximal sensing and vegetation indices of wheat for predictive agriculture

Climate change will have the impact on wheat production, in the form of lower harvests, higher costs, and necessary deviation from traditional farming. Monitoring the wheat canopy using remote and proximal sensors during the growing season plays an important role in site-specific management decisions, but could also improve understanding of the abiotic and physiological processes that control plant N uptake, yield, and protein content. As automated sensors provide information on wheat development, we are exploring the hypothesis that proximal canopy reflectance data could define plant phenology in new ways throughout the season, allowing better prediction of crop yields and detection of plant stress caused by climate change parameters. Spectroradiometers and multispectral imaging are capable of measuring the spectral reflectance of plants, providing vegetation indices that are valuable for assessing the amount and condition of vegetation. This paper presents ongoing work on the use of a system to collect multispectral data on wheat development under different climatic conditions, artificially generated and modified in prototype climate chambers

climate change ; wheat ; multispectral imaging ; spectroradiometer ; prediction

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