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

2021 ◽  
Vol 13 (21) ◽  
pp. 11685
Author(s):  
Jing Ma ◽  
Kuan Liu ◽  
Chenggong Chen ◽  
Fiaz Ahmad ◽  
Baijing Qiu

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.

1998 ◽  
Vol 46 (1) ◽  
pp. 135 ◽  
Author(s):  
Masako Mishio ◽  
Naoki Kachi

Stomatal conductance and leaf water potential at around noon, pre-dawn leaf water potential, pressure–volume parameters, and leaf structural characteristics including leaf thickness, leaf dry mass per unit area and turgid leaf water content per unit area were compared between a coastal shrub species, Eurya emarginata (Thunb.) Makino and an inland shrub species, E. japonica Thunb. The pre-dawn leaf water potential was only slightly lower in E. emarginata than in E. japonica, and the environmental conditions such as the photosynthetic photon flux density and the vapour pressure deficit did not differ obviously between the two habitats. No apparent differences were observed in the pressure–volume parameters between the two species. On the other hand, E. emarginata had much higher stomatal conductance and significantly thicker leaves with higher turgid leaf water content per unit area than E. japonica. The thicker leaf with higher water content on an area basis in E. emarginata maintains adequate leaf turgor pressure against a higher rate of transpiration.


Author(s):  
Rahul Raj ◽  
Jeffrey P. Walker ◽  
Vishal Vinod ◽  
Rohit Pingale ◽  
Balaji Naik ◽  
...  

2021 ◽  
Vol 13 (13) ◽  
pp. 2634
Author(s):  
Qiyuan Wang ◽  
Yanling Zhao ◽  
Feifei Yang ◽  
Tao Liu ◽  
Wu Xiao ◽  
...  

Vegetation heat-stress assessment in the reclamation areas of coal gangue dumps is of great significance in controlling spontaneous combustion; through a temperature gradient experiment, we collected leaf spectra and water content data on alfalfa. We then obtained the optimal spectral features of appropriate leaf water content indicators through time series analysis, correlation analysis, and Lasso regression analysis. A spectral feature-based long short-term memory (SF-LSTM) model is proposed to estimate alfalfa’s heat stress level; the live fuel moisture content (LFMC) varies significantly with time and has high regularity. Correlation analysis of the raw spectrum, first-derivative spectrum, spectral reflectance indices, and leaf water content data shows that LFMC and spectral data were the most strongly correlated. Combined with Lasso regression analysis, the optimal spectral features were the first-derivative spectral value at 1661 nm (abbreviated as FDS (1661)), RVI (1525,1771), DVI (1412,740), and NDVI (1447,1803). When the classification strategies were divided into three categories and the time sequence length of the spectral features was set to five consecutive monitoring dates, the SF-LSTM model had the highest accuracy in estimating the heat stress level in alfalfa; the results provide an important theoretical basis and technical support for vegetation heat-stress assessment in coal gangue dump reclamation areas.


2013 ◽  
Vol 40 (4) ◽  
pp. 409 ◽  
Author(s):  
Harald Hackl ◽  
Bodo Mistele ◽  
Yuncai Hu ◽  
Urs Schmidhalter

Spectral measurements allow fast nondestructive assessment of plant traits under controlled greenhouse and close-to-field conditions. Field crop stands differ from pot-grown plants, which may affect the ability to assess stress-related traits by nondestructive high-throughput measurements. This study analysed the potential to detect salt stress-related traits of spring wheat (Triticum aestivum L.) cultivars grown in pots or in a close-to-field container platform. In two experiments, selected spectral indices assessed by active and passive spectral sensing were related to the fresh weight of the aboveground biomass, the water content of the aboveground biomass, the leaf water potential and the relative leaf water content of two cultivars with different salt tolerance. The traits were better ascertained by spectral sensing of container-grown plants compared with pot-grown plants. This may be due to a decreased match between the sensors’ footprint and the plant area of the pot-grown plants, which was further characterised by enhanced senescence of lower leaves. The reflectance ratio R760 : R670, the normalised difference vegetation index and the reflectance ratio R780 : R550 spectral indices were the best indices and were significantly related to the fresh weight, the water content of the aboveground biomass and the water potential of the youngest fully developed leaf. Passive sensors delivered similar relationships to active sensors. Across all treatments, both cultivars were successfully differentiated using either destructively or nondestructively assessed parameters. Although spectral sensors provide fast and qualitatively good assessments of the traits of salt-stressed plants, further research is required to describe the potential and limitations of spectral sensing.


2019 ◽  
Vol 104 ◽  
pp. 41-47 ◽  
Author(s):  
Wenpeng Lin ◽  
Yuan Li ◽  
Shiqiang Du ◽  
Yuanfan Zheng ◽  
Jun Gao ◽  
...  

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Ruomeng Wang ◽  
Nianpeng He ◽  
Shenggong Li ◽  
Li Xu ◽  
Mingxu Li

AbstractLeaf water content (LWC) has important physiological and ecological significance for plant growth. However, it is still unclear how LWC varies over large spatial scale and with plant adaptation strategies. Here, we measured the LWC of 1365 grassland plants, along three comparative precipitation transects from meadow to desert on the Mongolia Plateau (MP), Loess Plateau, and Tibetan Plateau, respectively, to explore its spatial variation and the underlying mechanisms that determine this variation. The LWC data were normally distributed with an average value of 0.66 g g−1. LWC was not significantly different among the three plateaus, but it differed significantly among different plant life forms. Spatially, LWC in the three plateaus all decreased and then increased from meadow to desert grassland along a precipitation gradient. Unexpectedly, climate and genetic evolution only explained a small proportion of the spatial variation of LWC in all plateaus, and LWC was only weakly correlated with precipitation in the water-limited MP. Overall, the lasso variation in LWC with precipitation in all plateaus represented an underlying trade-off between structural investment and water income in plants, for better survival in various environments. In brief, plants should invest less to thrive in a humid environment (meadow), increase more investment to keep a relatively stable LWC in a drying environment, and have high investment to hold higher LWC in a dry environment (desert). Combined, these results indicate that LWC should be an important variable in future studies of large-scale trait variations.


2018 ◽  
Vol 9 ◽  
Author(s):  
Samuli Junttila ◽  
Junko Sugano ◽  
Mikko Vastaranta ◽  
Riikka Linnakoski ◽  
Harri Kaartinen ◽  
...  

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