Ensuring a food-secure/safe future dictates changes in agriculture to cope with several plant diseases and stresses. Understanding how to achieve greater crop yield and quality, with minimized environmental footprint, requires advancements in high-throughput techniques to early and accurately detect and monitor the effects of biotic and abiotic stresses, as well as the effectiveness of plant protection strategies. Vegetation spectroscopy has emerged as a promising tool, being a non-destructive, rapid, and relatively low-cost technique to monitor vegetation status: it relies on the interaction of light with plant chemical/structural composition and water content. Reflection of light in the visible (400-700 nm), near-infrared (700-1100 nm), and short-wave infrared (1100-2400 nm) can provide a comprehensive assessment of shifts in macroscopic symptoms and the underlying morpho-anatomical and physio-chemical responses of plants to diseases and stresses. Actually, another benefit of this spectral approach is represented by its capability to monitor plant traits and functions over large geographic areas if scaled from leaf to remote sensing level with measurements acquired from a variety of airborne and space platforms, including UAVs, aircrafts and satellite. In this context, hyperspectral imaging integrates imaging and point spectroscopy to bring the hyperspectral approach to the pixel level, so allowing to map outcomes. The present work aims to highlight the potential of vegetation spectroscopy for detecting and monitoring plant responses to environmental constraints, in order to increase crop yield and quality, as well as optimize management and input efforts. First, it briefly reports basic concepts of vegetation optical properties. It then reports some case studies from the Plant Pathology group of the Department of Agriculture, Food and Environment of the University of Pisa concerning the spectroscopic detection and monitoring of plant diseases (e.g., Verticillium wilt of eggplant) and stress conditions (e.g., aucuba under drought). These studies highlight the capability of spectral data to accurately 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. The presented results could be used in many frameworks such as precision agriculture, high-throughput plant phenotyping, and smart nursery management. Knowledge gaps and perspectives of the proposed approach are finally reported such as the need to (i) explore other major crop diseases/stress, (ii) develop spectral sensors, and (iii) advance algorithms for exploitation of spectral data.
Vegetation spectroscopy: a tool for the early detection and monitoring of plant diseases and stress
Ivan FiaccadoriPrimo
;Lorenzo Cotrozzi
Ultimo
2022-01-01
Abstract
Ensuring a food-secure/safe future dictates changes in agriculture to cope with several plant diseases and stresses. Understanding how to achieve greater crop yield and quality, with minimized environmental footprint, requires advancements in high-throughput techniques to early and accurately detect and monitor the effects of biotic and abiotic stresses, as well as the effectiveness of plant protection strategies. Vegetation spectroscopy has emerged as a promising tool, being a non-destructive, rapid, and relatively low-cost technique to monitor vegetation status: it relies on the interaction of light with plant chemical/structural composition and water content. Reflection of light in the visible (400-700 nm), near-infrared (700-1100 nm), and short-wave infrared (1100-2400 nm) can provide a comprehensive assessment of shifts in macroscopic symptoms and the underlying morpho-anatomical and physio-chemical responses of plants to diseases and stresses. Actually, another benefit of this spectral approach is represented by its capability to monitor plant traits and functions over large geographic areas if scaled from leaf to remote sensing level with measurements acquired from a variety of airborne and space platforms, including UAVs, aircrafts and satellite. In this context, hyperspectral imaging integrates imaging and point spectroscopy to bring the hyperspectral approach to the pixel level, so allowing to map outcomes. The present work aims to highlight the potential of vegetation spectroscopy for detecting and monitoring plant responses to environmental constraints, in order to increase crop yield and quality, as well as optimize management and input efforts. First, it briefly reports basic concepts of vegetation optical properties. It then reports some case studies from the Plant Pathology group of the Department of Agriculture, Food and Environment of the University of Pisa concerning the spectroscopic detection and monitoring of plant diseases (e.g., Verticillium wilt of eggplant) and stress conditions (e.g., aucuba under drought). These studies highlight the capability of spectral data to accurately 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. The presented results could be used in many frameworks such as precision agriculture, high-throughput plant phenotyping, and smart nursery management. Knowledge gaps and perspectives of the proposed approach are finally reported such as the need to (i) explore other major crop diseases/stress, (ii) develop spectral sensors, and (iii) advance algorithms for exploitation of spectral data.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
