Determination of Relative Water Content

2006 ◽  
pp. 207-212 ◽  
Author(s):  
Luís González ◽  
Marco González-Vilar
Keyword(s):  
Water Content ◽  
Relative Water Content ◽  
Relative Water

scholarly journals Instantaneous and non-destructive relative water content estimation from deep learning applied to resonant ultrasonic spectra of plant leaves

Plant Methods ◽  
2019 ◽  
Vol 15 (1) ◽  
Author(s):  
María Dolores Fariñas ◽  
Daniel Jimenez-Carretero ◽  
Domingo Sancho-Knapik ◽  
José Javier Peguero-Pina ◽  
Eustaquio Gil-Pelegrín ◽  
...  
Keyword(s):  
Deep Learning ◽  
Water Content ◽  
Transmission Coefficient ◽  
Relative Water Content ◽  
Plant Leaves ◽  
Resonant Ultrasound Spectroscopy ◽  
Relative Water ◽  
Resonant Ultrasound ◽  
Non Destructive

Abstract Background Non-contact resonant ultrasound spectroscopy (NC-RUS) has been proven as a reliable technique for the dynamic determination of leaf water status. It has been already tested in more than 50 plant species. In parallel, relative water content (RWC) is highly used in the ecophysiological field to describe the degree of water saturation in plant leaves. Obtaining RWC implies a cumbersome and destructive process that can introduce artefacts and cannot be determined instantaneously. Results Here, we present a method for the estimation of RWC in plant leaves from non-contact resonant ultrasound spectroscopy (NC-RUS) data. This technique enables to collect transmission coefficient in a [0.15–1.6] MHz frequency range from plant leaves in a non-invasive, non-destructive and rapid way. Two different approaches for the proposed method are evaluated: convolutional neural networks (CNN) and random forest (RF). While CNN takes the entire ultrasonic spectra acquired from the leaves, RF only uses four relevant parameters resulted from the transmission coefficient data. Both methods were tested successfully in Viburnum tinus leaf samples with Pearson’s correlations between 0.92 and 0.84. Conclusions This study showed that the combination of NC-RUS technique with deep learning algorithms is a robust tool for the instantaneous, accurate and non-destructive determination of RWC in plant leaves.

The Method of Plant Water Status Measurement in Sweet Potato (Ipomoea Batatas (L) Lam.)

2010 ◽  
Vol 7 (1) ◽  
Author(s):  
Saraswati Prabawardani
Keyword(s):  
Water Content ◽  
Sweet Potato ◽  
Relative Water Content ◽  
Water Status ◽  
Plant Water Status ◽  
Equilibration Time ◽  
Plant Water ◽  
Font Size ◽  
Potato Leaf ◽  
Relative Water

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Palmer Amaranth (Amaranthus palmeri) Competition for Water in Cotton

Weed Science ◽  
2015 ◽  
Vol 63 (4) ◽  
pp. 928-935 ◽  
Author(s):  
Sarah T. Berger ◽  
Jason A. Ferrell ◽  
Diane L. Rowland ◽  
Theodore M. Webster
Keyword(s):  
Water Content ◽  
Relative Water Content ◽  
Yield Loss ◽  
Linear Trend ◽  
Palmer Amaranth ◽  
Cotton Production ◽  
Excess Water ◽  
Relative Water ◽  
Competition For Light ◽  
Daily Water

Palmer amaranth is a troublesome weed in cotton production. Yield losses of 65% have been reported from season-long Palmer amaranth competition with cotton. To determine whether water is a factor in this system, experiments were conduced in 2011, 2012, and 2013 in Citra, FL, and in Tifton, GA. In 2011, infrequent rainfall lead to drought stress. The presence of Palmer amaranth resulted in decreased soil relative water content up to 1 m in depth. Cotton stomatal conductance (gs) was reduced up to 1.8 m from a Palmer amaranth plant. In 2012 and 2013 higher than average rainfall resulted in excess water throughout the growing season. In this situation, no differences were found in soil relative water content or cottongsas a function of proximity to Palmer amaranth. A positive linear trend was found in cotton photosynthesis and yield; each parameter increased as distance from Palmer amaranth increased. Even in these well-watered conditions, daily water use of Palmer amaranth was considerably higher than that of cotton, at 1.2 and 0.49 g H20 cm−2d−1, respectively. Although Palmer amaranth removed more water from the soil profile, rainfall was adequate to replenish the profile in 2 of the 3 yr of this study. However, yield loss due to Palmer amaranth was still observed despite no change ings, indicating other factors, such as competition for light or response to neighboring plants during development, are driving yield loss.

Influence of humates to mitigate NaCl-induced adverse effects on Ocimum basilicum L.: relative water content and photosynthetic pigments

2021 ◽  
Vol 53 (4) ◽  
Author(s):  
Juan José Reyes-Pérez ◽  
Bernardo Murillo-Amador ◽  
Alejandra Nieto-Garibay ◽  
Luis G. Hernández-Montiel ◽  
Francisco H. Ruiz-Espinoza ◽  
...  
Keyword(s):  
Adverse Effects ◽  
Water Content ◽  
Photosynthetic Pigments ◽  
Relative Water Content ◽  
Ocimum Basilicum ◽  
Relative Water

scholarly journals Assessment of lettuce quality during storage at low relative humidity using Global Stability Index methodology

2012 ◽  
Vol 32 (2) ◽  
pp. 366-373 ◽  
Author(s):  
María Roberta Ansorena ◽  
María Victoria Agüero ◽  
María Grabriela Goñi ◽  
Sara Roura ◽  
Alejandra Ponce ◽  
...  
Keyword(s):  
Ascorbic Acid ◽  
Relative Humidity ◽  
Global Stability ◽  
Water Content ◽  
Relative Water Content ◽  
Storage Time ◽  
Stability Index ◽  
Total Chlorophyll ◽  
External Zone ◽  
Relative Water

During postharvest, lettuce is usually exposed to adverse conditions (e.g. low relative humidity) that reduce the vegetable quality. In order to evaluate its shelf life, a great number of quality attributes must be analyzed, which requires careful experimental design, and it is time consuming. In this study, the modified Global Stability Index method was applied to estimate the quality of butter lettuce at low relative humidity during storage discriminating three lettuce zones (internal, middle, and external). The results indicated that the most relevant attributes were: the external zone - relative water content, water content , ascorbic acid, and total mesophilic counts; middle zone - relative water content, water content, total chlorophyll, and ascorbic acid; internal zone - relative water content, bound water, water content, and total mesophilic counts. A mathematical model that takes into account the Global Stability Index and overall visual quality for each lettuce zone was proposed. Moreover, the Weibull distribution was applied to estimate the maximum vegetable storage time which was 5, 4, and 3 days for the internal, middle, and external zone, respectively. When analyzing the effect of storage time for each lettuce zone, all the indices evaluated in the external zone of lettuce presented significant differences (p < 0.05). For both, internal and middle zones, the attributes presented significant differences (p < 0.05), except for water content and total chlorophyll.

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