Hyperspectral reflectance (HR) technology as proxy approach to diagnose fusarium head blight (FHB) in wheat crop could be a real-time and non-invasive approach for its in-field management to reduce grain damage. In-field canopy’s non-imaging HR (400–2400 nm using ground-based spectrometer system), photosynthesis rate (Pn) and disease severity (DS) data were simultaneously acquired from artificially inoculated wheat plots over a period of two years (2020 and 2021) in the field. Subsequently, continuous wavelet transform (CWT) was employed to select the consistent spectral bands (CSBs) and to develop the canopy-based difference indices with criterion of variable importance score using random forest—recursive feature elimination. Thereby, different machine learning algorithms were employed for FHB classification and multivariate estimation, and linear regression models to evaluate the newly developed indices against conventional vegetation indices. The results showed that inoculation reduced the Pn rate of spikes, elevated reflectance in visible and short-wave infrared regions and decreased in near infrared region at different days after inoculation (DAI). CWT analysis selected five CSBs (401, 460, 570, 786 and 840 nm) employing datasets from 2020 and 2021. These spectral bands were employed to develop wheat fusarium canopy indices (WFCI₁ and WFCI₂). Considering the average classification accuracy (ACA) in both years of experiments, WFCI₁ manifested a maximum ACA of 75% at 5 DAI with DS of 9.73% which raised to 100% at 10 DAI with a DS of 18%. ACA mentions the averaged results of all machine learning classifiers (MLC). While in the perspective of MLC, random forest (RF) outperformed the rest of the MLC, individually, it revealed 100% classification accuracy through WFCI₁ at DS 10.78% on the eight DAI. The univariate estimation of disease based on WFCI₁ and WFCI₂ with independent data produced R² and root mean square error (RMSE) values of 0.80 and 14.7, and 0.81 and13.50, respectively. However, Knn regression analysis with both canopy indices (WFCI₁ and WFCI₂) manifested the maximum accuracy for disease estimation with RMSE of 11.61 and R² = 0.83. Conclusively, the newly proposed HR indices show great potential as proxy approach for detecting FHB at early stage and understanding the physical state of crops in field conditions for the better management and control of plant diseases.

Salinity is a long-standing global environmental stressor of terrestrial agroecosystems, with important implications for viticulture sustainability, especially in arid and semi-arid environments. Salt-induced physiological and biochemical disruptions to grapevines undermine yield and long-term vineyard sustainability. This review aims to integrate physiological, molecular, and omics-based insights to elucidate how grapevine rootstocks confer salinity tolerance and to identify future breeding directions for sustainable viticulture. This review critically assesses the ecological and molecular processes underlying salt stress adaptation in grapevine (Vitis spp.) rootstocks, with an emphasis on their contribution to modulating scion performance under saline conditions. Core adaptive mechanisms include morphological plasticity, ion compartmentalization, hormonal regulation, antioxidant defense, and activation of responsive genes to stress. Particular emphasis is given to recent integrative biotechnological developments—including transcriptomics, proteomics, metabolomics, and genomics—that reveal the intricate signaling and regulatory networks enabling rootstock-mediated tolerance. By integrating advances across eco-physiological, agronomic, and molecular realms, this review identifies rootstock selection as a promising strategy for bolstering resilience in grapevine production systems confronted by salinization, a phenomenon increasingly exacerbated by anthropogenic land use and climate change. The research highlights the value of stress ecology and adaptive root system strategies for alleviating the environmental consequences of soil salinity for perennial crop systems.