Asian soybean rust (Phakopsora pachyrhizi) is the most severe disease in soybean crops production. The early detection of the disease by traditional methods involves visual inspection of the symptoms present in the leaves and is expensive and time-consuming. The limitations of visual detection have led to an interest in the development of spectroscopically based detection techniques for the rapid diagnosis of this disease. Thus, this work aimed to develop a procedure for early and accurate detection and differentiation of soybean under different levels of Asian rust disease, based on spectral analysis and linear discriminant analysis (LDA), with optimum wavelengths selection by a stepwise procedure. Reflectance spectroscopy ranging from the visible (Vis) to the near-infrared (NIR) region (350–2,500 nm) was obtained by a Fieldspec 3 Jr. hyperspectral sensor through the spectral measurement of soybean leaves with different levels of disease that had the following treatments: uninfected (T1), severity 0.6% (T2), severity 2.0% (T3), severity 7.0% (T4), severity 18.0% (T5), and severity 42.0% (T6). There were 15 spectral curves measured in each treatment, totalling 90 spectral samples. Principal component analysis (PCA) was applied as an indicator of the explained variance of the reflectance spectra among the different disease progressions. The spectral signature of the leaves showed the existence of a strong increase in reflectance in the Vis region when the levels of disease increased, associated with a lower concentration of pigments. The PCA explained over 97.00% of the spectral variance in the first and second principal components and the stepwise procedure selected from 87 spectral bands. The LDA achieved global accuracies of 100.00% and 82.51%, in the calibration and validation procedures, respectively. These results suggest the spectral reflectance technique as a promising tool for cost-effective, fast analysis and a non-destructive method for diagnosis Asian soybean rust.

Soybean (Glycine max (L.) Merrill) is highly sensitive to water deficit, particularly during the vegetative phase, when morphological and metabolic plasticity support continued growth and photosynthetic efficiency. We applied eleven water regimes, from full irrigation (W100) to total water withholding (W0), to plants grown under controlled conditions. After 14 days, we quantified morphophysiological, biochemical, leaf optical, gas exchange, and chlorophyll a fluorescence traits. Drought induces significant reductions in leaf area, biomass, pigment pools, and photosynthetic rates (A, g_s, ΦPSII) while increasing the levels of oxidative stress markers (electrolyte leakage, ROS) and proline accumulation. OJIP transients and JIP test metrics revealed reduced electron-transport efficiency and increased energy dissipation for many parameters under severe stress. Principal component analysis (PCA) clearly separated those treatments. PC1 captured growth and water status variation, whereas PC2 reflected photoprotective adjustments. These data show that progressive drought limits carbon assimilation via coordinated diffusive and biochemical constraints and that the accumulation of proline, phenolics, and lignin is associated with osmotic adjustment, antioxidant buffering, and cell wall reinforcement under stress. The combined use of hyperspectral sensors, gas exchange, chlorophyll fluorescence, and multivariate analyses for phenotyping offers a rapid, nondestructive diagnostic tool for assessing drought severity and the possibility of selecting drought-resistant genotypes and phenotypes in a changing stress environment.

Reflectance hyperspectroscopy is recognised for its potential to elucidate biochemical changes, thereby enhancing the understanding of plant biochemistry. This study used the UV-VIS-NIR-SWIR spectral range to identify the different biochemical constituents in Hibiscus and Geranium plants. Hyperspectral vegetation indices (HVIs), principal component analysis (PCA), and correlation matrices provided in-depth insights into spectral differences. Through the application of advanced algorithms—such as PLS, VIP, iPLS-VIP, GA, RF, and CARS—the most responsive wavelengths were discerned. PLSR models consistently achieved R² values above 0.75, presenting noteworthy predictions of 0.86 for DPPH and 0.89 for lignin. The red-edge and SWIR bands displayed strong associations with pivotal plant pigments and structural molecules, thus expanding the perspectives on leaf spectral dynamics. These findings highlight the efficacy of spectroscopy coupled with multivariate analysis in evaluating the management of biochemical compounds. A technique was introduced to measure the photosynthetic pigments and structural compounds via hyperspectroscopy across UV-VIS-NIR-SWIR, underpinned by rapid multivariate PLSR. Collectively, our results underscore the burgeoning potential of hyperspectroscopy in precision agriculture. This indicates a promising paradigm shift in plant phenotyping and biochemical evaluation.

Brazil is one of the largest producers of maize worldwide. However, this production is threatened due to low soil fertility, especially low levels of potassium (K⁺). K⁺ is one of the most important nutrients in plant metabolism, acting on enzymatic activation and also on photosynthetic processes. The identification of its deficiency by using traditional methods is difficult with regard to timely restoration of the nutrient to adequate levels. Therefore, the use of low-cost modified cameras attached to Unmanned Aerial Vehicles (UAVs) are important tools for agricultural monitoring. Nevertheless, there are no reports of studies with the purpose of evaluating the monitoring of K⁺ deficiency in maize crops using multispectral images captured from UAVs. Therefore, this study aimed at exploring the possibility of identifying K⁺ deficiency and quantifying the nutrient leaf content by using a Vegetation Index (VI). The experiment was carried out at the National Soybean Research Centre (Embrapa Soja, a branch of the Brazilian Agricultural Research Corporation). The experimental plots were constantly managed in order to obtain different conditions of K⁺ availability to plants, achieving levels that ranged from severe deficiency to an adequate nutrient level. The following treatments were established: severe potassium deficiency (SPD), moderate potassium deficiency (MPD) and adequate supply of potassium (ASP). The evaluations were performed in the Brazilian maize crop referred to as ‘safrinha, at the V7, V12 and R3 developmental stages, with image capture covering the visible and near-infrared region, using two Fujifilm IS PRO digital cameras attached to an UAV. In these development stages, leaves were collected to determine tissue K⁺ concentration. The images were radiometrically corrected with the support of calibration targets and reference values, using an Fieldspec 3 Jr. spectroradiometer. The VIs comprised the ratio among the red, green and infrared spectral bands, that is, green normalized difference vegetation index (GNDVI), normalized difference vegetation index (NDVI), ratio between infrared and green (GRVI), ratio between green and infrared (GNIR), ratio between red and infrared (RNIR) and ratio between infrared and red (RVI). Regarding all the treatments assessed, the results showed that foliar K⁺ was statistically different. The VIs were efficient only in differentiating SPD and ASP treatments at all development stages evaluated. However, none were statistically significant for MPD. The linear regressions showed a high coefficient of determination (R²) and low root mean square error (RMSE) value; the best prediction of K⁺ concentration obtained was at V12 for regressions with these VIs: GRVI (R² = 0.79, RMSE 4.50 g kg⁻¹) and RVI (R² = 0.71, RMSE 4.39 g kg⁻¹). The grain yield values showed that SPD caused an average reduction of 5,645.90 kg ha⁻¹ in relation to the ASP. Considering MPD, the grain yield was 1,242.00 kg ha⁻¹ lower in comparison with ASP. In conclusion, estimating foliar K⁺ content and identifying its deficiency in maize crops based on the VIs of multispectral images from cameras attached to UAVs is possible, which ensures agility to these evaluations in a non-destructive manner, improving efficiency of K⁺ fertilization and providing farmers with a new tool.