RESUMO

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.

RESUMO

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 R2CV values ranging from 0.81 to 0.87 and RPDP 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.

RESUMO

Currently, there are no free databases of 3D point clouds and images for seedling phenotyping. Therefore, this paper describes a platform for seedling scanning using 3D Lidar with which a database was acquired for use in plant phenotyping research. In total, 362 maize seedlings were recorded using an RGB camera and a SICK LMS4121R-13000 laser scanner with angular resolutions of 45° and 0.5° respectively. The scanned plants are diverse, with seedling captures ranging from less than 10 cm to 40 cm, and ranging from 7 to 24 days after planting in different light conditions in an indoor setting. The point clouds were processed to remove noise and imperfections with a mean absolute precision error of 0.03 cm, synchronized with the images, and time-stamped. The database includes the raw and processed data and manually assigned stem and leaf labels. As an example of a database application, a Random Forest classifier was employed to identify seedling parts based on morphological descriptors, with an accuracy of 89.41%.

RESUMO

In this study, daily changes over a short period and diurnal progression of spectral reflectance at the leaf level were used to identify spring wheat genotypes (Triticum aestivum L.) susceptible to adverse conditions. Four genotypes were grown in pots experiments under semi-controlled conditions in Chile and Spain. Three treatments were applied: i) control (C), ii) water stress (WS), and iii) combined water and heat shock (WS+T). Spectral reflectance, gas exchange and chlorophyll fluorescence measurements were performed on flag leaves for three consecutive days at anthesis. High canopy temperature ( H CT ) genotypes showed less variability in their mean spectral reflectance signature and chlorophyll fluorescence, which was related to weaker responses to environmental fluctuations. While low canopy temperature ( L CT ) genotypes showed greater variability. The genotypes spectral signature changes, in accordance with environmental fluctuation, were associated with variations in their stomatal conductance under both stress conditions (WS and WS+T); L CT genotypes showed an anisohydric response compared that of H CT , which was isohydric. This approach could be used in breeding programs for screening a large number of genotypes through proximal or remote sensing tools and be a novel but simple way to identify groups of genotypes with contrasting performances.

RESUMO

Plant perception and responses to environmental stresses are known to encompass a complex set of mechanisms in which the microbiome is involved. Knowledge about plant physiological responses is therefore critical for understanding the contribution of the microbiome to plant resilience. However, as plant growth is a dynamic process, a major hurdle is to find appropriate tools to effectively measure temporal variations of different plant physiological parameters. Here, we used a non-invasive real-time phenotyping platform in a one-to-one (plant-sensors) set up to investigate the impact of a synthetic community (SynCom) harboring plant-beneficial bacteria on the physiology and response of three commercial maize hybrids to drought stress (DS). SynCom inoculation significantly reduced yield loss and modulated vital physiological traits. SynCom-inoculated plants displayed lower leaf temperature, reduced turgor loss under severe DS and a faster recovery upon rehydration, likely as a result of sap flow modulation and better water usage. Microbiome profiling revealed that SynCom bacterial members were able to robustly colonize mature plants and recruit soil/seed-borne beneficial microbes. The high-resolution temporal data allowed us to record instant plant responses to daily environmental fluctuations, thus revealing the impact of the microbiome in modulating maize physiology, resilience to drought, and crop productivity.

RESUMO

The selection of wheat genotypes according to their drought tolerance is essential to off-season cultivation. The objective of this work was to characterize wheat genotypes through yield and components, morphological characteristics under water stress, and irrigated water use efficiency in the Cerrado region in Brazil. Genotypes were planted during the winters of 2016 and 2017 since there is no precipitation during this season and water levels can be measured. They were then submitted to four water regimes: WR1, WR2, WR3, and WR4, representing 100 %, 83 %, 50 %, and 30 % of evapotranspiration replacement. The following variables were evaluated: peduncle length (PL), number of ears m-² (NE m-²), hectoliter weight (HW), thousand grain weight (TGW), drought resistance index (DRI), irrigated water use efficiency (IWUE) and yield. Most variables showed correlation with yield and can be a useful tool for breeding programs. PL and HW were best correlated with yield. BRS 264 (irrigated biotype) was productive in treatments receiving the greatest number of irrigation treatments. Given that WR1 registered the highest water level, it was not expected that the rainfed biotype (BR18) would show a higher yield than an irrigated biotype (BRS254). BRS404 (rainfed biotype) was the most productive under moderate stress treatment (WR3). Aliança (rainfed biotype) showed a higher yield under severe stress. Rainfed biotypes presented a higher DRI than the irrigated ones. These genotypes can be used as a reference in breeding programs under each water regime in which their performance was outstanding. None of the variables studied contributed to the selection of the most efficient wheat genotypes in the IWUE.

Assuntos

RESUMO

The selection of wheat genotypes according to their drought tolerance is essential to off-season cultivation. The objective of this work was to characterize wheat genotypes through yield and components, morphological characteristics under water stress, and irrigated water use efficiency in the Cerrado region in Brazil. Genotypes were planted during the winters of 2016 and 2017 since there is no precipitation during this season and water levels can be measured. They were then submitted to four water regimes: WR1, WR2, WR3, and WR4, representing 100 %, 83 %, 50 %, and 30 % of evapotranspiration replacement. The following variables were evaluated: peduncle length (PL), number of ears m-² (NE m-²), hectoliter weight (HW), thousand grain weight (TGW), drought resistance index (DRI), irrigated water use efficiency (IWUE) and yield. Most variables showed correlation with yield and can be a useful tool for breeding programs. PL and HW were best correlated with yield. BRS 264 (irrigated biotype) was productive in treatments receiving the greatest number of irrigation treatments. Given that WR1 registered the highest water level, it was not expected that the rainfed biotype (BR18) would show a higher yield than an irrigated biotype (BRS254). BRS404 (rainfed biotype) was the most productive under moderate stress treatment (WR3). Aliança (rainfed biotype) showed a higher yield under severe stress. Rainfed biotypes presented a higher DRI than the irrigated ones. These genotypes can be used as a reference in breeding programs under each water regime in which their performance was outstanding. None of the variables studied contributed to the selection of the most efficient wheat genotypes in the IWUE.(AU)

Assuntos

RESUMO

Investigating the growth behavior of plant root systems as a function of soil water is considered an important information for the study of root physiology. A non-invasive tool based on electromagnetic wave transmittance in the microwave frequency range, operating close to 4.8 GHz, was developed using microstrip patch antennas to determine the volumetric moisture of soil in rhizoboxes. Antennas were placed on both sides of the rhizobox and, using a vector network analyzer, measured the S parameters. The dispersion parameter S21 (dB) was also used to show the effect of different soil types and temperature on the measurement. In addition, system sensitivity, reproducibility and repeatability were evaluated. The quantitative results of the soil moisture, measured in rhizoboxes, presented in this paper, demonstrate that the microwave technique using microstrip patch antennas is a reliable, non-invasive and accurate system, and has shown potentially promising applications for measurement of rhizobox-based root phenotyping.

Assuntos

RESUMO

The feasibility of thermography as a technique for plant screening aiming at drought-tolerance has been proven by its relationship with gas exchange, biomass, and yield. In this study, unlike most of the previous, thermography was applied for phenotyping contrasting maize genotypes whose classification for drought tolerance had already been established in the field. Our objective was to determine whether thermography-based classification would discriminate the maize genotypes in a similar way as the field selection in which just grain yield was taken into account as a criterion. We evaluated gas exchange, daily water consumption, leaf relative water content, aboveground biomass, and grain yield. Indeed, the screening of maize genotypes based on canopy temperature showed similar results to traditional methods. Nevertheless, canopy temperature only partially reflected gas exchange rates and daily water consumption in plants under drought. Part of the explanation may lie in the changes that drought had caused in plant leaves and canopy structure, altering absorption and dissipation of energy, photosynthesis, transpiration, and partitioning rates. Accordingly, although there was a negative relationship between grain yield and plant canopy temperature, it does not necessarily mean that plants whose canopies were maintained cooler under drought achieved the highest yield.

Assuntos

RESUMO

Gene function discovery in plants, as other plant science quests, is aided by tools that image, document, and measure plant phenotypes. Tools that acquire images of plant organs and tissues at the microscopic level have evolved from qualitative documentation tools, to advanced tools where software-assisted analysis of images extracts quantitative information that allows statistical analyses. They are useful to perform morphometric studies that describe plant physical characteristics and quantify phenotypes, aiding gene function discovery. In parallel, non-destructive, versatile, robust, and user friendly technologies have also been developed for surface topography analysis and quality control in the industrial manufacture sector, such as optoelectronic three-dimensional (3D) color microscopes. These microscopes combine optical lenses, electronic image sensors, motorized stages, graphics engines, and user friendly software to allow the visualization and inspection of objects of diverse sizes and shapes from different angles. This allow the integration of different automatically obtained images along the Z axis of an object, into a single image with a large depth-of-field, or a 3D model in color. In this work, we explored the performance of an optoelectronic microscope to study plant morphological phenotypes and plant surfaces in different model species. Furthermore, as a "proof-of-concept," we included the phenotypic characterization (morphometric analyses at the organ level, color, and cell size measurements) of Arabidopsis mutant leaves. We found that the microscope tested is a suitable, practical, and fast tool to routinely and precisely analyze different plant organs and tissues, producing both high-quality, sharp color images and morphometric and color data in real time. It is fully compatible with live plant tissues (no sample preparation is required) and does not require special conditions, high maintenance, nor complex training. Therefore, though barely reported in plant scientific studies, optoelectronic microscopes should emerge as convenient and useful tools for phenotypic characterization in plant sciences.