Leaf Moisture Content Research Articles

Influence of Plant Leaf Moisture Content on Retention of Electrostatic-Induced Droplets

Agricultural electrostatic spraying can help to reduce the threat of pesticides to human health and the environment. However, the influence of the law of leaf water content on electrostatic spraying has not been studied. In this study, we used leaf water content as an evaluation index of electrostatic spraying technology and verified the correlation between leaf water content and leaf capacitance value by statistical methods in order to achieve in vivo measurements of leaf water content in relation to tomato, pepper, and wheat crop leaves. Using these in vivo measurements of leaf water content and retention, we demonstrate that the retention of electrostatic droplets on the leaves of all three crops increases with increasing water content; the retention per unit area of leaves increased by 6.1 mg/cm2, an increase of 7.29%. Increasing the electrostatic spray voltage (10~30 kV) enhances the retention of droplets on the leaves of the crops, with a maximum increase of 6.1. The retention of non-electrostatic droplets decreases with increasing water content; retention at the lowest water content was 1.103~1.131 times greater than at the highest water content. This study has implications for research related to improving the retention of electrostatic droplets in leaves.

Thermo-gas dynamics affect the leaf canopy shape and moisture content of aquaponic lettuce in a modified partially diffused microclimatic chamber

Precise and timely monitoring of leaf water content and growth is necessary for precision agriculture. In this study, the impact of temperature and gas concentration variations was synthesized with leaf canopy shape and moisture content of aquaponic lettuce in a customized partially diffused microclimatic chamber equipped with an RGB camera. Using the acquired thermo-gas sensor data during the light-dependent photosynthetic reaction and dark-period aerobic respiration, it was analyzed with the extracted spectro-morphological leaf canopy signatures. Applying spectro-morphological leaf canopy data as input to the recurrent neural network it was possible to sensitively predict full moisture content (FMT) and equivalent water thickness (EWT) with 92.34% and 85.96% accuracy, respectively. A decrease in red green blue vegetation index (RGBVI) and an increase in excess of green (ExG) optical vegetation stress index corresponds to an increase in FMT. According to our data, it was possible to observe that the water content balance during the third week after sowing, follows the same trend observed for the oxygen production increase during the light period. Additionally, it was also possible to observe that uneven leaf broadening (canopy shape index > 1), low EWT, high photosynthetic quotient (PQ), and photothermal unit (PTU), and minimal temperature and humidity stretch resulted in higher oxygen production rates. A growing degree day of 23.46 ± 0.30 °C day is ideal for aquaponic lettuce. Plants with six (6) weeks exhibited the highest thermal sensitivity (Q1) of 0.205. Overall, the developed model and observations allow concluding that the combined temperature-O2CO2 dynamics monitoring must be considered to achieve maximum production rates. Moreover, in the framework of the new approaches developed within precision agriculture schemes, the collected optical data allied to the recurrent neural network are suitable tools for this continuous monitoring in an automated way.

Appraisal of Three Proximal Sensing Systems to Estimate Macronutrient Contents of Detached Soybean Leaves

ABSTRACT Overapplication of fertilizers is common and may lead to plant toxicity and pollution of water resources in agriculture. A practical method is needed to estimate nutrient stress quickly, easily, and economically for a sustainable management. This study dealt with the prediction of leaf moisture content (MC) and macronutrient contents including nitrogen (N), phosphorus (P), and potassium (K) of soybean leaves using three different optical instruments of chromameter, chlorophyll meter and fluorometer. Forty-five leaf samples from a commercial soybean field were obtained. Color parameters (L*, a*, b*), SPAD (soil plant analysis development) values and quantum yield (QY) values were quantified for each leaf sample. Leaf and soil samples were analyzed using standard leaf and soil chemical analysis procedures. Correlation analysis, principal component analysis (PCA) and partial least square regression (PLSR) were used for data analysis. High correlation was found between two color parameters (L* and b*) with MC and macronutrient contents (r ≥ 0.65) and also, among SPAD, QY, MC, and macronutrient contents (r ≥ 0.70). The prediction models were evaluated based on the RMSEP (root mean square error of prediction) and R 2 (coefficient of determination) values. The results showed that soybean leaf N content can be estimated using all three optical instruments with chromameter giving slightly better results (RMSEP = 0.23%, R 2 = 0.80). Concerning the MC, chlorophyll meter gave slightly better prediction performance (RMSEP = 2.69%, R 2 = 0.73) than the other two instruments. Hence, results suggest that these three optical instruments can be used to assess the macronutrient contents of soybean leaves quickly, easily, and economically.

Non-Destructive Testing of Moisture and Nitrogen Content in Pinus Massoniana Seedling Leaves with NIRS Based on MS-SC-CNN

Pinus massoniana is a pioneer reforestation tree species in China. It is crucial to evaluate the seedling vigor of Pinus massoniana for reforestation work, and leaf moisture and nitrogen content are key factors used to achieve it. In this paper, we proposed a non-destructive testing method based on the multi-scale short cut convolutional neural network (MS-SC-CNN) to measure moisture and nitrogen content in leaves of Pinus massoniana seedlings. By designing a reasonable short cut structure, the method realized the transmission of loss function gradient across the multi-layer structure in the network and reduced the information loss caused by the multi-layer transmission in the forward propagation. Meanwhile, in the back propagation stage, the loss caused by the multi-layer transmission of gradient was reduced. Thus, the gradient vanishing problem in training was avoided. Since the method realized cross-layer transmission error, the convolutional layer could be increased appropriately to obtain higher measurement accuracy. To verify the performance of the proposed MS-SC-CNN non-destructive measurement method, the near-infrared hyperspectral data of sample leaves of 219 Pinus massoniana seedlings were collected from the Huangping Forest Farm in Guizhou Province. The correlation coefficient between the measured and real values of the prediction was as high as 0.977 and the root mean square error was 0.242 for the moisture content of Pinus massoniana seedling leaves. For the nitrogen content of Pinus massoniana seedling leaves, the correlation coefficient between the measured and real values of the prediction was 0.906 and the root-mean-square error was 0.061. The results showed that the non-destructive testing method based on MS-SC-CNN that we proposed can accurately measure the moisture and nitrogen content in leaves of Pinus massoniana seedlings.

When do grasses resprout after fire?

This article is a Commentary on Simpson et al. (2021), 230: 832–844.

A Model for RF Loss through Vegetation with Varying Water Content

Assessing plant water status is important for monitoring plant physiology. Radio signals are attenuated when passing through vegetation. Both analytical and empirical models developed for radio frequency (RF) loss through vegetation have been dependent on experimental measurements and those measurements have been completed in specific situations. However, for models to be more broadly applicable across a broad range of vegetation types and constructs, basic electrical properties of the vegetation need to be characterised. Radio waves are affected especially by water and the relationship between water content in vegetation expressed as effective water path (EWP) in mm and measured RF loss (dB) at 2.4 GHz was investigated in this work. The EWP of eucalyptus leaves of varying amounts of leaf moisture (0% - 41.5%) ranged from 0 - 14 mm, respectively. When the model was compared with the actual RF loss there was a systematic offset equivalent to a residual leaf moisture content of 6.5% that was unaccounted for in the leaf moisture content determination (oven drying). This was attributed to bound water. When the model was adjusted for this amount of additional leaf water, the average RMSE in predicted RF loss was ±2.2 dB and was found to explain 89% of the variance in measured RF loss.

Electrical Properties Predict Wheat Leaf Moisture

HighlightsA non-destructive prediction model for moisture content of wheat leaves was established based on electrical properties.The model based on a single property (capacitance or resistance) was improved by using both properties.The model accurately detected the moisture content of wheat leaves in real-time to avoid irrigation lag.The results provide a basis for real-time and targeted water-saving irrigation of winter wheat in an arid region.Abstract. In this study, we aimed to establish a non-destructive and rapid approach to monitor the moisture content of wheat leaves in Southern Xinjiang, China, and promptly acquire information on the physiological water demand of crops to guide precision irrigation. Wheat leaves were used as the research object. Using a custom-made clamped parallel-plate capacitor and LCR digital bridge meter, we determined the electrical properties (capacitance and resistance) of wheat leaves with various moisture contents within a frequency range from 0.12 to 100 kHz. Moreover, we explored the correlation between leaf moisture content and the electrical properties. Our data showed that leaf moisture exhibited the best correlation with the electrical properties at 50 kHz. Under these optimized conditions, a model for moisture measurement was established using a single-parameter method (capacitance or resistance). However, the estimated standard errors (RMSE) of this method were 3.29% (for resistance) and 3.49% (for capacitance), which were greater than the standard error of the measured moisture content (2%). Therefore, we developed an improved model using a two-parameter method (capacitance and resistance), and the estimated standard error was 2.68%, which was more feasible for predicting moisture content compared with the single-parameter method. The model was validated using eight groups of wheat leaf samples at the turning-green stage and the jointing stage, and the RMSE values were less than 2%. Our findings provide a scientific basis for real-time and targeted water-saving irrigation of wheat in arid areas of Southern Xinjiang. Keywords: Electrical property, Model, Moisture content, Precision irrigation, Wheat leaves.

Single bands leaf reflectance prediction based on fuel moisture content for forestry applications

Vegetation indices can be used to perform quantitative and qualitative assessment of vegetation cover. These indices exploit the reflectance features of leaves to predict their biophysical properties. In general, there are different vegetation indices capable of describing the same biophysical parameter. For instance, vegetation water content can be inferred from at least sixteen vegetation indices, where each one uses the reflectance of leaves in different spectral bands. Therefore, if the leaf moisture content, a vegetation index and the reflectance at the wavelengths to compute the vegetation index are known, then the reflectance in other spectral bands can be computed with a bounded error. The current work proposes a method to predict, by a machine learning regressor, the leaf reflectance (spectral signature) at specific spectral bands using the information of leaf moisture content and a single vegetation index of two tree species (Pinus radiata, and Eucalyptus globulus), which constitute 97.5% of the Valparaíso forests in Chile. Results suggest that the most suitable vegetation index to predict the spectral signature is the Leaf Water Index, which using a Kernel Ridge Regressor achieved the best prediction results, with a RMSE lower than 0.022, and a average R2 greater than 0.95 for Pinus radiata and 0.81 for Eucalyptus globulus, respectively.

Genotype x Environmental Interaction for Growth and Yield Parameters of Tree Mulberry Genotypes in Different Seasons

Aims: To identify the stable genotypes across the seasons for different yield and its contributing traits.
 Study Design: Field experimental design was used
 Place and Duration of Study: The experiment was conducted in different seasons during 2017-19 at Department of Sericulture, University of Agricultural Sciences, GKVK, Bangalore.
 Methodology: The present study comprised of six mulberry genotypes viz., MI-012, MI-79, MI-21, MI-139, MI-516, ME-05 and two popular check varieties V1 and M5.
 Results: The mean squares due to seasons was significant for total shoot length (cm), number of leaves per plant, leaf yield per plant (g), ten fresh leaf weight (g), leaf moisture content (%) at harvest, leaf moisture retention capacity (%) at 6 and 9 hrs after harvest. Analysis of variance indicated high significance of mean sum of squares due to season for number of branches per plant, number of leaves per plant, leaf yield per plant, single leaf area, moisture content and moisture retention capacity at 6 and 9 hrs after harvest of leaf. Further, it could be observed that variance due to seasons (linear) were highly significant for number of branches per plant, total shoot length, number of leaves per plant, ten fresh leaf weight, leaf yield per plant, single leaf area, moisture content and moisture retention capacity at 6 and 9 hrs after harvest of leaf. Whereas, variance due to G х S (linear) was non significant for shoot height, internodal distance, number of leaves per plant, ten fresh leaf weight, leaf yield per plant, moisture content and moisture retention capacity at 6 and 9 hrs after harvest of leaf. Variance due to pooled deviation was highly significant for shoot height, number of branches per plant, total shoot length, internodal distance, number of leaves per plant, ten fresh leaf weight, single leaf area, moisture content and moisture retention capacity at 6 and 9 hrs after harvest of leaf. Whereas, variance due to pooled deviation was non significant for leaf yield per plant.

The feasibility of hand-held thermal and UAV-based multispectral imaging for canopy water status assessment and yield prediction of irrigated African eggplant (Solanum aethopicum L)

This study was conducted to evaluate the feasibility of a mobile phone-based thermal and UAV-based multispectral imaging to assess the irrigation performance of African eggplant. The study used a randomized block design (RBD) with sub-plots being irrigated at 100% (I100), 80% (I80) and 60% (I60) of the calculated crop water requirements using drip. The leaf moisture content was monitored at different soil moisture conditions at early, vegetative and full vegetative stages. The results showed that, the crop water stress index (CWSI) derived from the mobile phone-based thermal images is sensitive to leaf moisture content (LMC) in I80 and I60 at all vegetative stages. The UAV-derived Normalized Difference Vegetation Index (NDVI) and Optimized Soil Adjusted Vegetation Index (OSAVI) correlated with LMC at the vegetative and full vegetative stages for all three irrigation treatments. In cases where eggplant is irrigated under normal conditions, the use of NDVI or OSAVI at full vegetative stages will be able to predict eggplant yields. In cases where, eggplant is grown under deficit irrigation, CWSI can be used at vegetative or full vegetative stages next to NDVI or OSAVI depending on available resources.

Hydroprinted Liquid-Alloy-Based Morphing Electronics for Fast-Growing/Tender Plants: From Physiology Monitoring to Habit Manipulation.

Monitoring physiological signals and manipulating growth habits of living plants in real time are important for botany research, biohybrid plant robots, and precision agriculture. Although emerging epidermal electronics that can conveniently acquire vital signals of living organisms exhibit a high potential for such scenarios, it is a significant challenge to adapt such devices for plants, because they are fragile and usually have complex surfaces that can change significantly during rapid growth. A gentle fabrication process is critical in order to employ compliant electronic systems to adapt to this highly dynamic situation. In this study, a hydroprinted liquid-alloy-based morphing electronics (LAME) process is employed for fast-growing plants that will sense physiological signals and even function as a biohybrid to determine plant behavior on demand. Besides various surfaces of inorganic targeting substrates, pinning liquid alloy circuits onto the complex plant epidermis is enhanced by introducing high-surface-energy liquid. Functionally, the new developed LAME can be used to monitor leaf moisture content and length, and manipulate leaf and bean sprout orientation. This study lays the foundation for a new form of morphing electronics for botany or biohybrid plant robots, potentially impacting the next generation of precision agriculture and smart hybrid robots.

Assessment of combining ability for growth and survivability traits in mulberry using line × tester analysis

Combining ability in mulberry was assessed by using Line x Tester mating design. In the present study, five lines viz., MI-0543, MI-0615, MI-0651, MI-0685, MI-0718 and three testers’ viz., V1, G4, MI-0663 were crossed to obtain fifteen F1 progenies. Growth and survivability traits were evaluated for the F1 crosses through Analysis of variance (ANOVA), General combining ability (GCA) and Specific combining ability (SCA) for the parameters such as germination per cent, survivability per cent, plant height, number of leaves per branch, internodal distance and leaf moisture content. Among the eight parental genotypes MI-0543 (female) and V1 (male) were the best combiners and MI-0685 x V1 was the best cross for growth and survivability traits. Hence, both non- additive and additive gene actions are important for the mulberry improvement.

Estimation of the surface area-to-volume ratios of litter components of the Brazilian rainforest and their impact on litter fire rate of spread and flammability

The surface-area-to-volume ratio (SAV) is a significant parameter in vegetation description, although different researchers have presented diverging estimates for forest litter. However, considering litter as a unique entity and disregarding the SAV of its components provides a misleading fire rate of spread. Undoubtedly, the leaves are more relevant than the twigs for the rate of spread of surface fires, as the former has a higher SAV. Therefore, based on the proven hyperdominance of tree species in the Brazilian rainforest, this work presents an accurate method to estimate the SAV. Furthermore, it demonstrates the critical role played by the SAV in the litter flammability and fire rate of spread assessments and shows that the leaf moisture content does not influence its SAV. Besides, comparing SAV data using the methodology presented herein with other authors’ assessments, the results differ by up to 49%. Finally, using the Brazilian rainforest litter in the methodologies of two distinct authors showed a difference of at least 36% in the results. The primary outcome of this study is the estimation of the SAV of leaves and thin, medium, and thick twigs, representative of the Brazilian rainforest, at 12,680 m−1, 1360 m−1, 620 m−1, and 310 m−1, respectively. The estimated SAV values are essential input data to numerical simulation models, and for comparison purposes with other authors’ work, the litter SAV is 8460 m−1.

Appraisal of nitric oxide priming to improve the physiology of bread wheat

AbstractSeed priming is a pre-sown treatment and it is often used to improve the performance of plants in any environment, especially germination. In the current study, various concentrations of nitric oxide (NO) were used to evaluate its role for the induction of physiological variations within seven different wheat (Triticum aestivum L.) genotypes. Two experiments were conducted during 2013 and 2014 and the data were statistically analysed for significance. All these genotypes after treatment with sodium nitroprusside (SNP) as NO donor at 10−4 and 10−5 M concentrations were sown following randomized complete block design with triplicates in the fields of District Muzaffarabad, Pakistan. The concentration of NO at 10−4 M showed promising results and most of the studied characters were found improved compared to control. Wheat varieties primed with 10−4 M SNP showed highest germination speed and germination percentage. NARC-2011 and Uqab-2002 showed much improvement in physiological attributes at both concentrations of NO priming. However, Uqab-2002 and Punjab-2011 showed a significant increase in chlorophyll contents and leaf moisture content with 10−4 and 10−5 M SNP priming compared to control. Highest relative water content was observed within unprimed Lasani, whereas the relative injury was found to be decreased at 10−4 M SNP primed Faisalabad-2008. Wheat varieties Punjab-2011 and Faisalabad-2008 showed the highest increase in grain yield and biological yield by 10−4 M SNP. Hence, it is concluded that sowing of crops after priming at 10−4 M NO concentration can improve the germination, biochemistry and physiology that ultimately lead to an increase in crop yield.

Alterations in biomass allocation indicate the adaptation of submersed macrophytes to low-light stress

Abstract The decline in submersed macrophytes induced by low-light stress is ubiquitous in mid-lower Yangtze lakes in China. However, the trade-offs among the adaptation mechanisms used by submersed macrophytes to low-light stress remain unclear. Moreover, the experimental period used in most previous studies was relatively short, and plant traits were not monitored multiple throughout the experimental period. In the present study, we examined the growth of a representative submersed macrophyte, Potamogeton maackianus, under four light regimes (2.8%, 7.1%, 17.1% and 39.5% of ambient light) over a course of 12 months and assessed various plant traits at monthly and quarterly intervals. The results showed that P. maackianus exhibited a lower leaf moisture content and a higher stem moisture content under decreased light conditions. Under the lowest light regime, P. maackianus had low soluble carbohydrate (SC) contents in the leaves and low starch contents in the stems at all the seasons. P. maackianus showed different allometric relationships among different treatments reflected different adaption strategies resulting from different light environment. P. maackianus exhibited a relatively stable biomass allocation pattern characterized by a continual increase in the stem mass fraction of total biomass under relatively high light transmittance (17.1% and 39.5%), which is a response of P. maackianus that can ensure a high initial biomass in the next spring season. However, under low light transmittance, the biomass allocation pattern fluctuated early in the experiment, and the amplitude increased with decreases in the light transmittance (2.8% and 7.1%). P. maackianus exhibited trade-offs between the stem (plant height) and leaf mass (leaf area) fractions of the total biomass, which help improve the adaptation of the macrophyte to a low-light environment. Our results can be useful for estimating light-use conditions of P. maackianus according to regression relationships between the leaf/stem mass fractions of the total biomass and time.

Non-destructive estimation of winter wheat leaf moisture content using near-ground hyperspectral imaging technology

ABSTRACTAccurate monitoring of crop moisture content is very important for irrigation scheduling and yield increase. This study aims to construct an optimal estimation model of winter wheat leaf moisture content (LMC) through spectral data processing and feature band selection. LMC and spectral reflectance were measured in 2017-2018 to construct models using simple linear regression (SLR), principal components regression (PCR), and partial least square regression (PLSR); feature bands for modelling were selected through correlation analysis and the effects of feature band number on estimation accuracy were compared. The results showed that data transformation significantly enhanced the correlation between spectral features and LMC. However, the band position corresponding to the maximum correlation coefficient for each transformation was not fixed. The accuracy of PLSR models were significantly higher than that of PCR and SLR models. The comparison of relative percent deviation (RPD) values indicated that the RPD values increased rapidly and then tended to be stable with the increase of feature band number. The R′′ -PLSR model constructed with 28 feature bands (R2c = 0.8517; RPD > 2.0) estimated the LMC more accurately than other models. This study provides a good method for non-destructive monitoring of crop moisture content.

Prediction and monitoring of leaf water content in soybean plants using terahertz time-domain spectroscopy

Leaf water content (LWC) is one of the physiological parameters most commonly used for describing crop growth status and productivity. Thus, rapid and non-destructive methods for the prediction of LWC are important. Here, a rapid and accurate LWC monitoring method using terahertz time-domain spectroscopy (THz-TDS) was tested on soybean. A high-precision mathematical model was developed to predict LWC based on the results of THz-TDS. Next, the model was used to study the effect of various levels of water -stress, different growth media, and leaf treatment with exogenous ABA. Reliable results showed that the correlation coefficient and root mean square error of the prediction set were 0.9153 and 0.0526, respectively. LWC gradually decreased over time under different moisture treatments, and even more slowly under conditions of normal water supply than under water stress. The trend followed by LWC under water deficit was dependent on the water-holding capacity of the growth medium. Soil had the best water-holding capacity, followed by the seedling matrix and the vermiculite matrix. Furthermore, the stomata of the adaxial and abaxial leaf surfaces closed due to water deficit, as determined by laser scanning confocal microscopy (LSCM). We found that stomatal opening in leaves significantly decreased under water stress to prevent excessive water loss, consistent with the observed reduction in leaf moisture content. Results indicated a rapid increase in LWC upon ABA treatment followed by a slower decrease. Similarly, changes in stomatal opening were observed using LSCM, consistent with the changes in leaf water content detected macroscopically. This study showed that THz radiation technology combined with modeling methods provides an effective, contact-free, safe, and non-destructive technique to measure water content in soybean leaves. This technique could facilitate further study of plant-water relations.

Effect of mutual grafting on the growth and moisture content in post-grafting of two varieties cherry tomato seedlings

The effects of mutual grafting on growth and moisture content in post-grafting generations of two varieties cherry tomato seedlings (yellow cherry tomato and red cherry tomato) under stress of Cd were studied in a pot experiment involving the following: un-grafted yellow cherry tomato and red cherry tomato (Yellow CK and Red CK), yellow cherry tomato as rootstock and red cherry tomato as scion (Yellow Rootstock, Red Scion), yellow cherry tomato as scion and red cherry tomato as rootstock (Yellow Scion, Red Rootstock). Mutual grafting increased plant height, stem weight of post-grafting of yellow cherry tomato; however, stem diameter, root and leaf weight and moisture content were no significantly effects compared with Yellow CK. As for red cherry tomato seedlings, mutual grafting increased root length of post-grafting; plant height and moisture content of post-grafting of Red Rootstock were increased, while in Red Scion, the these parameters decreased or showed no significant effects. These results imply that mutual grafting may improve or no effect in growth and moisture content of post-grafting generations.

Internet of Trees (IoTr) Implemented by Highly Dispersive Electromagnetic Sensors

The water intake is intimately related to the plantation growth and the resulting production quality. The monitoring and regulation of the water content allows the city governments and municipalities to optimally design the watering schemes leading to healthy plantations and saving of irrigation water in dramatic amounts. The proposed moisture assessment system is based upon a network of highly sensitive electromagnetic sensors that work on the concept of spectral dispersion. The high sensitivity of the dispersion-based sensors comes from its narrowband magnitude or phase spectral signature at resonance. The minimal change in the moisture content of a sample placed in the sensor’s cavity is sensed by the resonance shift of this signature. When these sensors are connected in network configuration, the watering process of the whole plantation can be optimized cohesively. The concept of “Internet of Trees (IoTr)” is particularly useful in the communities where water is found in scarcity such as the Gulf countries. In this article, we experimentally demonstrate the concept using a two-node passive prototype in the 4-6 GHz range by establishing relation between leaf moisture content and resonant frequency of the dispersive sensor. On the system level, we propose implementation of a representative network system based on LoRa protocol. The proposed sensor network is low-cost, self-calibrated and can derives its power from energy harvesting, through a solar.

Impacts of Elevational Changes and Leaf Maturity Stages on Photoprotective Strategies and Biochemical Traits of Wild Fig [Ficus Carica Subsp. Rupestris (Hausskn)

ABSTRACT Three leaf maturity indices were defined as young, mature and senescence leaves. The effect of leaf phenophase resulted in a significant alteration in photosynthetic pigments, whereas site position had no significant effect on them. FPC values significantly increased along leaf phenophase development. TSP showed a sharp descending trend in the final phase of leaf elasticity. On the contrary, the highest TSS value was observed in senescence stage of leaf development. Such a trend was observed for starch content. The results revealed that FPC values significantly decreased along the elevation rise. RWC was significantly affected by both site position change and leaf phenophase factors, whereas ΨL was just significantly affected by leaf phenophase. The obtained results exhibited that leaf phenophase significantly affected six (F0, Fm, Fv/Fm, qP, NPQ, and ΦPSII) out of seven fluorescence variables. Meanwhile, the drastic effect of site position gradient was observed in F0, Fv/Fm, and qP. Severe decreases in leaf pigment variable values in SL are indicators of damage to chloroplasts due to leaf senescence and reduction of leaf moisture content, depicted through reducing leaf water potential. The FPC and TSS contents exhibited descending trends along site position gradients, which is consistent with the amount of rain and temperature. A remarkable reduction was observed in the values of Fv/Fm and Fm, ΦPSII and qP, suggesting the sufficiency of photochemistry transformation is affected by leaf cycle development, particularly in senescence stage.