Rapid Determination of Bud and Leaf Water Content Using Hyperspectral Sensors to Monitor Cold Hardiness in Grapevine

Water content in plants is one of the most common biochemical parameters limiting efficiency of photosynthesis and crop productivity. Therefore, it has very important meaning to predict the water content rapidly and nondestructively. The objective of this study was to investigate the feasibility of detecting the water content in the leaf using the diffuse reflectance spectra limited in the VIS/NIR region (400–1100 nm), which could be used to determine other biochemical parameters such as chlorophyll and nitrogen content. The experiment with leaves in different water stress was conducted. The statistical test result indicated that the determination of water content in leaf could be successfully performed by VIS/NIR spectroscopy combined with chemometrics method. The performances of different pretreatment methods were compared. The model with best performance was obtained from the first derivative spectra. In order to make the calibration model more parsimonious and stable, a hybrid wavelength selection method was proposed to extract the efficient feature wavelength. Under the optimal condition, an RMSEP of 0.73% with 25 variables was obtained for water content prediction using extern validation. The conclusions presented could lead to the development of portable instrument for synchronous detecting water content and other biochemical parameters rapidly and nondestructively.

Rapid determination of leaf water content for monitoring waterlogging in winter wheat based on hyperspectral parameters

Plant leaf water content significantly influences photosynthetic efficiency and crop yield. Leaf water content (LWC) and equivalent water thickness (EWT) are indicators that reflect the water state within plant tissues, and they play a crucial role in assessing plant water supply and usage. In recent years, there has been a growing focus on the rapid and precise determination of plant water content. In this study, Cinnamomum camphora (C. camphora) was chosen as the subject of investigation. After acquiring spectral data, three types of vegetation indices were computed: the empirical vegetation index, the random combination dual-band vegetation index, and the ‘trilateral’ parameter. Four groups of optimal spectral index screening strategies were established, namely an empirical vegetation index group (G1), a random combination dual-band vegetation index group (G2), a ‘trilateral’ parameter group (G3), and a mixed group (G4). Three algorithms, specifically random forest (RF), radial basis function neural network (RBFNN), and support vector machine (SVM), were employed for the estimation of leaf water content (LWC) and equivalent water thickness (EWT) in mature C. camphora. The results demonstrated that the G4 group displayed superior performance, yielding five optimal spectral indices for LWC: water index (WI), optimized soil-adjusted vegetation index (OSAVI), difference vegetation index (DVI) at wavelengths 734 and 956 nm, first-order difference vegetation index (DVI-FD) at wavelengths 1009 and 774 nm, and red-edge amplitude (Dr). With regard to EWT estimation, the five optimal spectral indices encompassed the red-edge normalized difference vegetation index (RE-NDVI), simple ratio water index (SRWI), difference vegetation index (DVI) at wavelengths 700 and 1167 nm, first-order difference vegetation index (DVI-FD) at wavelengths 1182 and 1514 nm, and red-edge area (SDr). Utilizing these indices as inputs significantly enhanced the accuracy of the models, with the RF model emerging as the most effective for estimating LWC and EWT in C. camphora. Based on the LWC estimation model of the G4 group and the RF algorithm, the determination coefficient (R2) for both the training and test sets reached 0.848 and 0.871, respectively. The root mean square error (RMSE) was 0.568% for the training set and 0.582% for the test set, while the average relative error (MRE) stood at 0.806% and 0.642%, respectively. Regarding the EWT estimation model, R2 values of 0.887 and 0.919 were achieved for the training and test sets, accompanied by RMSE values of 0.6 × 10−3 g·cm−2 and 0.7 × 10−3 g·cm−2, and MRE values of 3.198% and 2.901%, respectively. These findings lay a solid foundation for hyperspectral moisture monitoring in C. camphora and offer valuable reference for the rapid assessment of crop growth status.

Leaf water content is one of the most common physiological parameters limiting efficiency of photosynthesis and biomass productivity in plants including Miscanthus. Therefore, it is of great significance to determine or predict the water content quickly and non-destructively. In this study, we explored the relationship between leaf water content and diffuse reflectance spectra in Miscanthus. Three multivariate calibrations including partial least squares (PLS), least squares support vector machine regression (LSSVR), and radial basis function (RBF) neural network (NN) were developed for the models of leaf water content determination. The non-linear models including RBF_LSSVR and RBF_NN showed higher accuracy than the PLS and Lin_LSSVR models. Moreover, 75 sensitive wavelengths were identified to be closely associated with the leaf water content in Miscanthus. The RBF_LSSVR and RBF_NN models for predicting leaf water content, based on 75 characteristic wavelengths, obtained the high determination coefficients of 0.9838 and 0.9899, respectively. The results indicated the non-linear models were more accurate than the linear models using both wavelength intervals. These results demonstrated that visible and near-infrared (VIS/NIR) spectroscopy combined with RBF_LSSVR or RBF_NN is a useful, non-destructive tool for determinations of the leaf water content in Miscanthus, and thus very helpful for development of drought-resistant varieties in Miscanthus.

This study examined the effects of intraspecific variation in leaf nitrogen and water content on the growth, consumption, conversion efficiency and nitrogen use of Colias butterfly larvae. Pest and non-pest Colias philodice eriphyle larvae and Colias eurytheme larvae were fed field-collected alfalfa (Medicago sativa) and vetch (Vicia americana) leaves in laboratory experiments. In all treatments, at least one indicator of larval growth performance was positively correlated with leaf nitrogen content, which supports the view that nitrogen is a limiting nutrient for larval growth. The benefits associated with eating leaves with high nitrogen content included higher growth rates, conversion efficiencies, nitrogen accumulation rates and larval nitrogen contents. Over the ranges examined in this study, variation in leaf nitrogen content (2.8-7.0% dry wt) affected larval growth more than variation in leaf water content (66-79% fresh wt). Pest and non-pest C. p. eriphyle responded alike to variation in the leaf nitrogen content of vetch, but there were differences between populations on alfalfa. Pest larvae were more sensitive to variation in leaf water content than non-pest larve. The differences between these populations may be due to specific adaptations resulting from the shift to alfala by pest Colias. It is suggested that herbivores' responses to intraspecific variation in leaf nitrogen content may have important consequences for the evolution of plant defenses and nutrient allocation patterns, and for agricultural pest management.

Crop water stress is a deficiency in plants in water supply when the transpiration rate becomes higher than the water absorption capacity. The stress may be detected by a reduction in soil water content, or by the change in physiological properties of the crop. The leaf water content (LWC) is commonly used to assess the water status of plants, which is one of the indicators of crop water stress. In this work, the leaf relative water contents of four different crops: canola, wheat, soybeans, and corn—all in vegetative growth stage—were determined by a noninvasive tool called, electrical impedance spectroscopy (EIS). Using a frequency range of 5–15 kHz, a strong correlation between leaf water contents and leaf impedances was obtained using multiple linear regression. The trained dataset was validated by analysis of variance tests. Regression results were obtained using the least square method. The optimized regression model coefficients for different crops were proposed by selecting features using the wrapper backward elimination method. Multi-collinearity among the features was considered and individual T-tests were made in the feature selection. A maximum correlation coefficient (R) of 0.99 was obtained for canola compared to the other crops; the corresponding coefficient of determination (R2) of 0.98, an adjusted R2 of 0.93, and root mean square error (rmse) of 0.30% were obtained for 36 features. Therefore, the results show that the proposed technique using EIS can be used to develop a low-cost and effective tool for determining the leaf water contents rapidly and efficiently in multiple crops.

Understanding the relationship between plant water status and productivity and between plant water status and plant mortality is required to effectively quantify and predict the effects of drought on plants. Plant water status is closely linked to leaf water content that may be estimated using remote sensing technologies. Here, we used an inexpensive miniature hyperspectral spectrometer in the 1550–1950 nm wavelength domain to measure changes in silver birch (Betula pendula Roth) leaf water content combined with leaf gas exchange measurements at a sub-minute time resolution, under increasing vapor pressure deficit, CO2 concentrations, and light intensity within the measurement cuvette; we also developed a novel methodology for calibrating reflectance measurements to predict leaf water content for individual leaves. Based on reflectance at 1550 nm, linear regression modeling explained 98–99% of the variation in leaf water content, with a root mean square error of 0.31–0.43 g cm−2. The prediction accuracy of the model represents a c. ten-fold improvement compared to previous studies that have used destructive sampling measurements of several leaves. This novel methodology allows the study of interlinkages between leaf water content, transpiration, and assimilation at a high time resolution that will increase understanding of the movement of water within plants and between plants and the atmosphere.

HighlightsA portable NIRS system with local computing hardware was developed for leaf water content determination.The proposed convolutional neural network for regression showed a satisfactory performance.Decision fusion of multiple regression models achieved a higher precision than single models.All of the devices and machine intelligence algorithms were integrated into the system.Software was developed for system control and user interface.Abstract. Spectroscopy has been widely used as a valid non-destructive technique for the determination of crop physiological parameters. In this study, a portable near-infrared spectroscopy (NIRS) system was developed for rapid measurement of rape (Brassica campestris) leaf water content. An integrated spectrometer (900 to 1700 nm) was used to collect the spectra. A Wi-Fi module was adopted for driving the spectrometer and realizing data communication. The NVIDIA Jetson Nano developer kit was employed to handle the received spectra and perform computing tasks. Three embedded spectral analysis models, including support vector regression (SVR), partial least square regression (PLSR), and deep convolutional neural network for regression (CNN-R), and decision fusions of these methods were built and compared. The results demonstrated that the separate models produced satisfactory predictions. The proposed system achieved the highest precision based on the fusion of PLSR and CNN-R. The hardware devices and analytical algorithms were all integrated into the proposed portable system, and the tested samples were collected from an actual field environment, which shows great potential of the system for outdoor applications. Keywords: Decision fusion, Deep learning, Leaf water content, Local computing, Portable NIRS system.

Sufficient nitrogen and shade may benefit photosynthetic down-regulation at elevated CO2 concentrations. Two levels of atmospheric CO2 400 and 760 μmol mol−1 were simulated using controlled environment open-top chambers, wheat (Triticum aestivum L) was grown at two N application rates (0 and 200 mg N kg−1 soil), and two photosynthetic photon fluxes (PPF, 100 and 60 % of solar irradiance). The increasing leaf N concentration, shade and N application extended the wheat developmental period and increased flag leaf fresh mass, water content, plant height and spike length by 11 d, 125, 126, 29 and 43 %, respectively. However, with sufficient N and elevated CO2, shade decreased dry mass, kilo-grain weight, instantaneous water use efficiency, grain water use efficiency and photosynthetic N use efficiency was increased by 30, 12, 2, 36 and 44 %, as compared with the unshaded treatment. Higher N application and shade increased N and chlorophyll concentration in flag leaves by 135 and 35 %, resulting in extended growth stage and increased plant and leaf water content, caused significant increments of plant height, grain number per spike and grain weight per spike under elevated CO2 as compared to N-deprived (0 mg N kg−1 soil) and unshaded treatment. Shade significantly increased leaf and plant water content, but did not affect wheat water and nitrogen use efficiency, which indicated that the decline of water content in flag leaves was a response of wheat to elevated CO2. The increment of leaf water status may extend the growth period to relieve photosynthesis acclimation at elevated CO2.

Hedera helix L. is a widespread liana that significantly influences forest ecosystems in temperate zones, exhibiting high adaptability to varying soil moisture and light levels. In this study, it was confirmed that H. helix dominates the herbaceous layer of the Kórnik Arboretum (Poland), with clear links between its above-ground biomass and key environmental factors. The study revealed that, under intense soil shading, the leaf to stem biomass ratio was disproportional, favoring leaves. Leaf and stem water content reflected the plant's adaptation to soil moisture, aligning with its field capacity. Strong relationships were found between leaf water content and soil moisture, while the correlations between leaf water content and light availability were weaker. The study also confirmed positive relationships between daily light integral and leaf water content, with a less pronounced effect on stem water content. These results enhance understanding of H. helix's role in temperate forests and its impact on ecosystem regeneration.

Leaf water content determination of oilseed rape using near-infrared hyperspectral imaging with deep learning regression methods

This paper presents the assessment of lettuce plant health using unmanned aerial vehicle (UAV)-based hyperspectral sensor, proximal sensors, and measurement of agronomic & physiological parameters. Hyperspectral data of lettuce plants at Cal Poly Pomona’s Spadra Farm was collected from a DJI Matric 600 multicopter UAV. An experimental lettuce plot was designed for the study. The plot was divided into several subplots that were subject to different water and nitrogen applications with three replications. Proximal sensors included Handheld spectroradiometer, water potential meter, and chlorophyll meter. The hypespectral data from the UAV and spectroradiometer were used in the determination of several vegetation indices including normalized difference vegetation index (NDVI), water band index (WBI), and modified chlorophyll absorption ratio index (MCARI). These indices were compared with chlorophyll meter data, water potential, plant height, leaf numbers, leaf water content, and leaf nitrogen content. With the hyperspectral data collected so far, MCARI has shown good correlation with chlorophyll meter data and WBI has shown good correlation with leaf water content. The paper will show and discuss all the vegetation indices and their relationship with proximal sensor data, agronomic measurement, and leaf water & nitrogen contents.

Determination of leaf water content was made on the "Saline"crops, using rice, wheat, barley and naked barley, grown in sand culture. The saline cultures were done by the solution to which NaCl had been added so as to become the critical concentration for their growth. Considering data from all saline cultures with the multifarious ecotypes induced by various varieties, various nutrient deficiencies or different growing stages, the differences in leaf water content were apparent, but the average decreases of water content were as small as 0.72 to 1.50%. And also the changes of leaf water content in green part (the basal part obviously uninjured) in leaves where the burn symptom began to develop, the changes in every part on an uninjured leaf and the changes in leaves classified according to leaf-order were just the same as before. Moreover, the differences in water content at various hours in a day were also small as stated above and the hourly variations in leaf water content in the saline cultures were a little more than in the non-saline cultures. It may be said in summary that although the incipient drying resulted from salinization in rhizosphere seems to be concerned with the osmotic inhibition effect, the specific features and the details of the progressive mechanism of saline injury still remain obscure

Chlorophyll meter SPAD-502 is widely used in determining leaf chlorophyll content in field plants as it is non-destructive and less time consuming. Currently, there are about 25 Hevea genotypes recommended by the Rubber Research Institute of Sri Lanka. Characteristics of leaves, i.e., leaf thickness, wax and water content varies among the genotypes and also could affect the readings of the SPAD-502 chlorophyll meter. Therefore, this study was carried out to investigate effect of leaf thickness, wax and water content on determination of leaf chlorophyll content of H. brasiliensis with SPAD-502 chlorophyll meter. Most commonly grown Hevea genotypes i.e., RRIC 121, RRIC 100, RRISL 203 and RRISL 2001 were selected for the study. Leaf samples of each genotype were collected from the budwood nurseries in Dartonfield Estate, Agalawatta. SPAD value of each leaf sample was measured and subjected to analyse the actual chlorophyll content by acetone extraction method. Leaf thickness, water content and epicuticular wax content of each leaf sample were also measured by using standard test methods. The highest leaf thickness, wax content and water content were observed in RRIC 100 genotype whilst RRISL 203 genotype gave the lowest values. Leaf chlorophyll content of RRIC 121 genotype was comparatively lower with an average value of 36.51 μg/cm2. However, the highest leaf chlorophyll content was observed in RRISL 203 genotype and the average value was 54.28 μg/cm2. Despite the genotype average, leaf wax content and water content were 79.75 μg/cm2 and 55.79%, respectively whilst leaf thickness was 0.126 mm. Pearson correlation coefficients for leaf thickness, wax content and water content vs. reading of SPAD-502 chlorophyll meter were above 0.05% and revealed that these three parameters did not affect the reading of SPAD-502 chlorophyll meter significantly (p=0.05) in determination of leaf chlorophyll content of H. brasiliensis. Keywords: Chlorophyll, Epicuticular wax, Hevea, Leaf thickness

summaryPrevious experiments have shown that leaf specific mass (LSM: the ratio of leaf dry mass lo area) was lower and leaf water content (LWC) was higher in annuals than in perennials, differences that are more generally found between fast‐and slow‐growing species. Leaf transverse sections of seven annual‐perennial pairs of grass species grown in the laboratory were analyzed to elucidate the anatomical bases of these differences.Leaf thickness was similar in annuals and perennials, but leaf density was significantly higher in perennials. The proportion of the leaf volume occupied by mesophyll was higher in annuals, at the expense of the three other tissues (i.e. epidermis, sclerenchyma and vascular tissues). The cross‐sectional area of mesophyll cells was higher in annuals than in perennials, but epidermal cell size was similar for both life‐forms.The ranges of LSM (23.1–49.5 g m −2) and LWC (0.70–0.86 g g−1) displayed by the 14 species were large enough to examine the general relationships between these two parameters and various anatomical characters. LSM was significantly correlated with leaf density, but not with leaf thickness. The anatomical character that best explained interspecific differences in LSM was the volume of cell walls per unit leaf area (approximated by the sum: sclerenchyma + vascular tissues (including its living component) + cell wall components of mesophyll and epidermis). LWC was found to depend on leaf density, and interspecific differences in this parameter were best explained by the proportion of mesophyll protoplast (i.e. proportion of mesophyll minus proportion of mesophyll occupied by cell walls) in the transverse sections.The physiological and ecological implications of these findings are discussed in terms of a trade‐off between leaf productivity and persistence.