Leaf spectral reflectance of Hevea brasiliensis in response to Pseudocercospora ulei

Hyperspectral response of cape gooseberry (Physalis peruviana L.) plants inoculated with Fusarium oxysporum f. sp. physali for vascular wilt detection

“…Recent research has indicated that the use of optical sensors generates information for disease identification (Zhao et al, 2014;Sterling and Melgarejo, 2020), nutritional status of plants (Martínez and Ramos, 2015;Martínez, 2017) and specific site management of crops (Peng and Gitelson, 2011). Hyperspectral data can be used to identify stress symptoms at an early stage and can be correlated with plant characteristics to support timely decision-making processes in crop management (Lowe et al, 2017).…”

Prediction of South American Leaf Blight and Disease-Induced Photosynthetic Changes in Rubber Tree, Using Machine Learning Techniques on Leaf Hyperspectral Reflectance

“…The two tested cultivars were made by Ford Plantations in Brazil from 1935 to 1943 [8] and were commercially introduced to Colombia in 1964 and 2000, respectively [39]. In this study, these two clones were used to obtain an infection gradient and achieve the lowest and greatest SALB severity classes [20].…”

“…(1) four-leaf gas exchange traits, net CO2 assimilation rate (A) (μmol CO2 m −2 s −1 ), transpiration rate (E) (mmol H2O m −2 s −1 ), stomatal conductance (gs) (mol H2O m −2 s −1 ), and extrinsic water use efficiency (WUEe) (μmol CO2 mmol −1 H2O), using an IRGA portable infrared gas analyzer (TPS-2 Portable Photosynthesis System, USA); and (2) five chlorophyll a fluorescence traits in leaflets adapted to darkness using dark-adaptation leaf-clips for 30 min, maximum photochemical efficiency of PSII (Fv/Fm), efficiency in capturing excitation en- After the photosynthetic traits' measurements, the spectral reflectance was measured using an Apogee PS-100 spectroradiometer (spectral range of 350 to 1000 nm) and Spec-traWIZ operation software (StellarNet Inc., Tampa, FL, USA) according to the methodology by Sterling and Melgarejo [20]. Thus, each spectral reading was automatically averaged with 30 spectral reflectance, using a spectral interval of 1 nm.…”

“…The spectral characteristics of light reflected by the leaves can be an indicator of biotic stress in plants with different types of pathogens [9]. Thus, several studies have demonstrated the potential of spectral reflectance and VIS-NIR spectroscopy systems (wavelength: 400-1000 nm) in remote detection and discrimination of plant pathogens [10,[18][19][20][21][22], as well as the use of hyperspectral data as a proxy to analyze physiological changes in plants from different types of stress [15][16][17]23,24]. In the VIS-NIR sensors, the most important regions of electromagnetic spectrum correspond to the visible portion of the spectrum (VIS, wavelength: 400-750 nm), mainly related to changes in chlorophyll and carotenoid contents, and the near-infrared (NIR), more precisely the short-wavelength region (SW-NIR, wavelength: 750-1300 nm), which is associated with structural discontinuities found in the leaf mesophyll [9,11].…”

“…In the H. brasiliensis-SALB pathosystem, Sterling and Melgarejo [20] used spectral vegetation indices (SVIs) and multiple linear regression to analyze foliar spectral changes caused by the fungus P. ulei, and Lucas Domingos da Silva et al [33] used near-infrared spectroscopy (NIRS) technique and PLS-DA (partial least squares discriminate analysis) modeling for the tree leaves classification of mature leaves with and without SALB.…”