Introduction: Hyperspectral remote sensing provides a powerful platform for identifying plant species with pharmacological relevance by capturing subtle variations in pigment content, physiological traits, and leaf structure.Methods: This study employed spectral indices to evaluate and differentiate 10 Ficus and two Moringa spp. based on their reflectance characteristics and morphological features. Spectral data were collected using an ASD FieldSpec spectroradiometer, and vegetation indices such as NDVI, SR, PRI, ARI2, NDRE, and MCARI were calculated to infer photosynthetic performance and secondary metabolite potential. Descriptive morphological traits were recorded to aid in species-level discrimination.Results and Discussion: Statistical analyses, including one-way ANOVA and Linear Discriminant Analysis (LDA), identified specific bands in the NIR and SWIR II regions as particularly effective for distinguishing among the studied taxa. Notably, Ficus benghalensis, Ficus racemosa, and Moringa oleifera exhibited superior spectral profiles, reflecting high pigment density and physiological vigor, which correspond with their well-documented pharmacological roles. Conversely, species like F. religiosa and M. peregrina showed relatively subdued spectral signatures. Statistical analyses (ANOVA and LDA) confirmed the discriminatory power of NIR and SWIR II regions across species. Morphological traits provided taxonomic support but were less distinctive than spectral indices. These findings demonstrate the value of hyperspectral indices as rapid, non-destructive tools to identify and prioritize medicinally potent species within Ficus and Moringa spp., offering insights for pharmacognosy, conservation, and phytochemical prospecting. Moreover, by enabling efficient identification of underutilized species with confirmed medical value, this approach may support efforts to sustainably manage native plant resources, particularly in regions where such species contribute to traditional healthcare systems, nutritional supplementation, and the resilience of local livelihoods.

EL-FAYOUM region is the unique Egyptian western desert depression provided by surface Nile water. It's one of the global oldest agricultural provinces. Hyperspectral remote sensing plays an important role in assessing the biophysical characters of plant. The current investigation is one of the early attempts to study the hyperspectral characteristics of cultivated crops and wild plants in El-Fayoum Governorate. The work assists in understanding the status of the vegetation structure responding to water scarcity in the study area. Spectral reflectance of cultivated & wild plants was recorded ASD field spec spectroradiometer device and integrated with lab analyses using statistical analyses of SPSS and JMP Software. Optimal waveband and optimal wavelength were calculated by ANOVA and Tukey's analysis for discriminating of plants in the study area. Remotely sensed hyperspectral data were processed to produce spectral indices of plants to estimate the plant's vitality. Results indicated that Near Infrared (NIR) and Short Wave Infrared II (SWIR II) spectral regions were optimal to discriminate investigated taxa. In the Blue spectral zone no significant reflection was noticed. Spectral characteristics for the Mangifera indica (infected plant) indicated water stress. Spectral Reflectance analysis for Ablmoschus esculentus indicated plant suffered chlorophyll decrease. Plant Senescence Reflectance Index (PSRI) and Moisture Stress Index (MSI) for Mangifera indica (infected plant) and Abelmoschus esculentus (infected plant) were high and indicated that two plants suffer canopy stress. Also, results showed that Citrus sinensis has the highest value of NDVI (0.82) and CRII (7.99) between other plants. It could be concluded that the study of spectral signature is rather valuable in characterizing vegetation cover. Also, growth conditions and the environment can be predicted via spectral characterization curves.

Introduction: Zinc (Zn) deficiency is a major limiting factor for crop productivity, particularly in sandy soils where nutrient retention and bioavailability are poor. The field experiment was conducted at the Agricultural Experimental Station, Cairo University (Giza, Egypt) during the 2022–2024 winter seasons. This study aimed to analyze the spectral responses of faba bean (Vicia faba L.) under different foliar Zn concentrations (0, 0.5, 1.0, 1.5, and 2.0 g L ⁻¹ ZnSO ₄ ·7H ₂ O) using a Randomized Complete Block Design (RCBD) with three replications, and to identify vegetation indices sensitive to Zn nutrition. Methods: Growth traits, biochemical attributes, seed yield, hyperspectral reflectance, and soil spectral properties were integrated to establish robust vegetation–soil indicators. Results and discussion: Foliar Zn application increased chlorophyll content by 24.5%, protein by 18.3%, and seed yield by 27.6% compared with the control (p ≤ 0.05). Results revealed that foliar Zn application significantly enhanced chlorophyll content, protein accumulation, soluble sugars, and seed yield, with the optimum response observed at 2.0 g L ⁻¹ . Spectral signatures demonstrated that Zn enrichment decreased red reflectance (∼670 nm), sharpened the red-edge slope (700–750 nm), and increased near-infrared reflectance (∼800 nm), indicating improved canopy structure and photosynthetic activity. Vegetation indices such as Normalized Difference Vegetation Index (NDVI), Green NDVI (GNDVI), Photochemical Reflectance Index (PRI), Normalized Difference Red-Edge Index (NDRE), Red-Edge Position (REP), and Modified Chlorophyll Absorption Ratio Index (MCARI) were highly responsive to Zn levels, showing strong correlations with physiological traits and closely aligned with growth and yield performance. Soil-based spectral indices, including the Clay Ratio Index (CRI) and the Normalized Difference Soil Index (NDSI), also differentiated among soil samples, linking soil spectral features with Zn-associated crop responses. These findings confirm that hyperspectral reflectance and derived indices provide reliable, non-destructive tools for monitoring Zn nutrition in faba bean. The integration of spectral traits with agronomic and biochemical data emphasizes the potential of hyperspectral sensing for optimizing micronutrient management in sandy soils. Moreover, the outcomes highlight the role of hyperspectral tools in precision agriculture, enabling site-specific nutrient strategies that enhance productivity, resource-use efficiency, and sustainable food security.

Pink bollworm (Pectinophora gossypiella (Saunders), PBW) is a major pest of cotton worldwide. The system of cotton pest management adopted in Egypt is described in relation to the economic thresholds of infestation (infested green boll3%). The best way to minimize impacts of a pest outbreak is to regularly monitor the crop to detect the onset of pest and to take timely action if the pest is present. Monitoring should be done on the basis of local knowledge and up-to-date and reliable global information. Reduction in losses caused by pests by timely and effective control measures will considerably add to food production in the country. Monitoring of this pest is generally undertaken through regular field surveys, which is labour intensive, time consuming and error prone. Alternately, radiometry is a reliable technique for rapid and non-destructive assessment of plant health. The purpose of this research was to develop a new method to detect infested cotton plant with PBW without any losses to boll. Thus, a study was conducted to characterize reflectance spectra of cotton plants with known PBW infestation, and seek to identify specific narrow wavelengths sensitive to PBW damage. Reflectance measurements were made in the spectral range of 350–2500 nm using a hyperspectral radiometer. Reflectance sensitivity analysis of the hyperspectral data to PBW damage also determined. Results of this study could suggest potential usage of remote sensing in monitoring spatial distribution of the PBW, and thereby enable effective planning and implementation of site-specific pest management practices. The study shows that it is feasible to detect PBW infestation using the hyperspectral data and recognize its level, which could be utilized to monitor trade and predictions.