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Digital archive of theses discussed at the University of Pisa

 

Thesis etd-03252024-220748


Thesis type
Tesi di dottorato di ricerca
Author
FIACCADORI, IVAN
URN
etd-03252024-220748
Thesis title
Spectroscopic detection and monitoring of plant diseases and stress. Applications to ornamental and major crop species
Academic discipline
AGR/12
Course of study
SCIENZE AGRARIE, ALIMENTARI E AGRO-AMBIENTALI
Supervisors
tutor Cotrozzi, Lorenzo
Keywords
  • leaf optical properties
  • leaf trait prediction
  • spectral signature analysis
Graduation session start date
29/03/2024
Availability
Withheld
Release date
29/03/2094
Summary
Healthy vegetation supports diverse biological communities and ecosystem processes, and provides crops, ecological services, forest products, forage, and countless other benefits. Advancements in techniques capable of detecting and monitoring plant responses to environmental constraints are mandatory to increase crop yield and quality, and optimize management and input efforts to cope with growing threats, such as climate change.
The present research aims to highlight the potential of using vegetation spectroscopy for these purposes, nested in the Digital Agriculture framework. First, it briefly reports basic concepts of vegetation spectroscopy. Then, it reports the approaches for exploiting spectral data, in particular the detection and monitoring of diseases and abiotic stress conditions. Today, many are the instruments and platforms available to acquire spectroscopic data at multiple corresponding spatial, temporal and spectral scales. Numerous studies highlight the capability of spectral data to accurately detect vegetation status and monitor specific plant responses to stress conditions, even prior to the onset of visual symptoms. Furthermore, they show that vegetation spectroscopy can be a rapid, non-destructive, and relatively inexpensive tool to accurately estimate an array of leaf physiological, biochemical and morphological parameters commonly investigated to monitor plant/stress interactions, using spectral data.
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