The importance of understanding spatial variability of soils is connected to crop management planning. This understanding makes it possible to treat soil not as a uniform, but a variable entity, and it enables site-specific management to increase production efficiency, which is the target of precision agriculture. Questions remain as the optimum soil sampling interval needed to make site-specific fertilizer recommendations in Brazil. The objectives of this study were: i) to evaluate the spatial variability of the main attributes that influence fertilization recommendations, using georeferenced soil samples arranged in grid patterns of different resolutions; ii) to compare the spatial maps generated with those obtained with the standard sampling of 1 sample ha-1, in order to verify the appropriateness of the spatial resolution. The attributes evaluated were phosphorus (P), potassium (K), organic matter (OM), base saturation (V%) and clay. Soil samples were collected in a 100 × 100 m georeferenced grid. Thinning was performed in order to create a grid with one sample every 2.07, 2.88, 3.75 and 7.20 ha. Geostatistical techniques, such as semivariogram and interpolation using kriging, were used to analyze the attributes at the different grid resolutions. This analysis was performed with the Vesper software package. The maps created by this method were compared using the kappa statistics. Additionally, correlation graphs were drawn by plotting the observed values against the estimated values using cross-validation. P, K and V%, a finer sampling resolution than the one using 1 sample ha-1 is required, while for OM and clay coarser resolutions of one sample every two and three hectares, respectively, may be acceptable.

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.

Hyperspectral technology offers significant potential for non-invasive monitoring and prediction of morphological parameters in plants. In this study, UV−VIS−NIR−SWIR reflectance hyperspectral data were collected from Nicotiana tabacum L. plants using a spectroradiometer. These plants were grown under different light and gibberellic acid (GA₃) concentrations. Through spectroscopy and multivariate analyses, key growth parameters, such as height, leaf area, energy yield, and biomass, were effectively evaluated based on the interaction of light with leaf structures. The shortwave infrared (SWIR) bands, specifically SWIR1 and SWIR2, showed the strongest correlations with these growth parameters. When classifying tobacco plants grown under different GA₃ concentrations in greenhouses, artificial intelligence (AI) and machine learning (ML) algorithms were employed, achieving an average accuracy of over 99.1% using neural network (NN) and gradient boosting (GB) algorithms. Among the 34 tested vegetation indices, the photochemical reflectance index (PRI) demonstrated the strongest correlations with all evaluated plant phenotypes. Partial least squares regression (PLSR) models effectively predicted morphological attributes, with R²_CV values ranging from 0.81 to 0.87 and RPD_P values exceeding 2.09 for all parameters. Based on Pearson’s coefficient XYZ interpolations and HVI algorithms, the NIR−SWIR band combination proved the most effective for predicting height and leaf area, while VIS−NIR was optimal for optimal energy yield, and VIS−VIS was best for predicting biomass. To further corroborate these findings, the SWIR bands for certain morphological characteristic wavelengths selected with s−PLS were most significant for SWIR1 and SWIR2, while i−PLS showed a more uniform distribution in VIS−NIR−SWIR bands. Therefore, SWIR hyperspectral bands provide valuable insights into developing alternative bands for remote sensing measurements to estimate plant morphological parameters. These findings underscore the potential of remote sensing technology for rapid, accurate, and non-invasive monitoring within stationary high-throughput phenotyping systems in greenhouses. These insights align with advancements in digital and precision technology, indicating a promising future for research and innovation in this field.

Heat stress is an abiotic factor that affects the photosynthetic parameters of plants. In this study, we examined the photosynthetic mechanisms underlying the rapid response of tobacco plants to heat stress in a controlled environment. To evaluate transient heat stress conditions, changes in photochemical, carboxylative, and fluorescence efficiencies were measured using an infrared gas analyser (IRGA Licor 6800) coupled with chlorophyll a fluorescence measurements. Our findings indicated that significant disruptions in the photosynthetic machinery occurred at 45 °C for 6 h following transient heat treatment, as explained by 76.2% in the principal component analysis. The photosynthetic mechanism analysis revealed that the dark respiration rate (Rd and Rd^*_CO2) increased, indicating a reduced potential for carbon fixation during plant growth and development. When the light compensation point (LCP) increased as the light saturation point (LSP) decreased, this indicated potential damage to the photosystem membrane of the thylakoids. Other photosynthetic parameters, such as A_MAX, VC_MAX, J_MAX, and ΦCO₂, also decreased, compromising both photochemical and carboxylative efficiencies in the Calvin–Benson cycle. The energy dissipation mechanism, as indicated by the NPQ, qN, and thermal values, suggested that a photoprotective strategy may have been employed. However, the observed transitory damage was a result of disruption of the electron transport rate (ETR) between the PSII and PSI photosystems, which was initially caused by high temperatures. Our study highlights the impact of rapid temperature changes on plant physiology and the potential acclimatisation mechanisms under rapid heat stress. Future research should focus on exploring the adaptive mechanisms involved in distinguishing mutants to improve crop resilience against environmental stressors.