scholarly journals Soil Moisture Levels Affect the Anatomy and Mechanical Properties of Basil Stems (Ocimum basilicum L.)

Plants ◽  
2021 ◽  
Vol 10 (7) ◽  
pp. 1320
Author(s):  
Elisa Driesen ◽  
Maurice De Proft ◽  
Wouter Saeys
Keyword(s):  
Soil Moisture ◽  
Vessel Diameter ◽  
Ocimum Basilicum ◽  
Healthy Plant ◽  
Vascular Tissues ◽  
Fast Growing ◽  
Structural Support ◽  
Stem Strength ◽  
Stem Segments

As plants would benefit from adjusting and optimizing their architecture to changing environmental stimuli, ensuring a strong and healthy plant, it was hypothesized that different soil moisture levels would affect xylem and collenchyma development in basil (Ocimum basilicum L. cv. Marian) stems. Four different irrigation set-points (20, 30, 40 and 50% VWC), corresponding respectively to pF values of 1.95, 1.65, 1.30 and 1.15, were applied. Basil plants grown near the theoretical wilting point (pF 2) had a higher xylem vessel frequency and lower mean vessel diameter, promoting water transport under drought conditions. Cultivation at low soil moisture also impacted the formation of collenchyma in the apical stem segments, providing mechanical and structural support to these fast-growing stems and vascular tissues. The proportion of collenchyma area was significantly lower for the pF1.15 treatment (9.25 ± 3.24 %) compared to the pF1.95 and pF1.30 treatments (16.04 ± 1.83% and 13.28 ± 1.38 %, respectively). Higher fractions of collenchyma resulted in a higher mechanical stem strength against bending. Additionally, tracheids acted as the major support tissues in the basal stem segments. These results confirm that the available soil moisture impacts mechanical stem strength and overall plant quality of basil plants by impacting xylem and collenchyma development during cultivation, ensuring sufficient mechanical support to the fast-growing stem and to the protection of the vascular tissues. To our knowledge, this study is the first to compare the mechanical and anatomical characteristics of plant stems cultivated at different soil moisture levels.

SISTEM OTOMASI PENYIRAMAN TANAMAN BERBASIS TELEGRAM

SIGMA TEKNIKA ◽  
2019 ◽  
Vol 2 (1) ◽  
pp. 81
Author(s):  
Muhammad Irsyam
Keyword(s):  
Soil Moisture ◽  
Plant Growth ◽  
Water Intake ◽  
Smart Phone ◽  
Growth Failure ◽  
Automation System ◽  
Notification System ◽  
Efficiency And Effectiveness ◽  
Plant Automation

ABSTRAK           Faktor yang menentukan kegagalan pertumbuhan suatu tanaman hampir dipengaruhi oleh teknik atau cara penyiraman tanaman yang salah. Hal ini disebabkan oleh teknik penyiraman yang dilakukan secara manual sehingga tidak semua tanaman mendapatkan asupan air yang merata untuk menghidari tanaman menjadi layu. Faktor lain yang menyebabkan kegagalan pertumbuhan tanaman adalah kelembaban tanah.          Oleh karena itu, untuk mengurangi permasalahan tersebut dirancanglah “Sistem Otomasi Penyiraman Tanaman Berbasis Telegram”. Adapun sistem ini meliputi penyiraman tanaman secara otomatis berdasarkan kadar kelembaban tanah dengan sistem pemberitahuan atau notifikasi yang akan dikirimkan kepada petani dengan menggunakan aplikasi smart phone Telegram.          Sistem ini telah mampu mengontrol penyiraman sesuai dengan kondisi yang diinginkan. Dengan adanya sistem otomasi penyiraman tanaman berbasis telegram maka dapat meningkatkan efesiensi dan efektivitas petani sehingga kualitas tanaman dapat terjaga dengan baik.Kata kunci -- Penyiraman Tanaman, Penyiraman Secara Otomatis, Telegram.ABSTRACT                Factors that determine the failure of a plant's growth of almost are influenced by incorrect cropping techniques or methods. This is caused by the technique of watering is done manually so that not all plants get a uniform water intake to avoid crops withered. Another factor that causes plant growth failure is soil moisture.          Therefore, to reduce the problem was designed "Telegram Based Water Planting Automation System". The system includes automatic watering of plants based on moisture level of the soil with a notification or notification system that will be sent to farmers using Telegram smart phone applications.          This system has been able to control the watering according to the desired conditions. With the telegraph-based plant watering plant automation system can improve the efficiency and effectiveness of farmers so that the quality of the plant can be maintained properly. Keywords -- Watering Plants, Watering Automatically, Telegram.  

Weed Infestation, Soil Moisture, Temperature under Mulching Cultivation with Different Films and the Effects on Yield and Quality of Potato

2017 ◽  
Vol 43 (4) ◽  
pp. 571
Author(s):  
Shu-Min ZHANG ◽  
Tang-Yuan NING ◽  
Zhen LIU ◽  
Bin WANG ◽  
Tao SUN ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Yield And Quality ◽  
Weed Infestation

EFFECTS OF ATMOSPHERE COMPOSITION ON POSTHARVEST QUALITY OF FRESH BASIL LEAVES (OCIMUM BASILICUM L.)

2005 ◽  
pp. 731-736 ◽  
Author(s):  
M.L. Amodio ◽  
G. Peri ◽  
G. Colelli ◽  
D. Centonze ◽  
M. Quinto
Keyword(s):  
Ocimum Basilicum ◽  
Postharvest Quality ◽  
Atmosphere Composition

scholarly journals Synergetic Use of Sentinel-1 and Sentinel-2 Data for Soil Moisture Mapping at Plot Scale

2018 ◽  
Vol 10 (8) ◽  
pp. 1285 ◽  
Author(s):  
Reza Attarzadeh ◽  
Jalal Amini ◽  
Claudia Notarnicola ◽  
Felix Greifeneder
Keyword(s):  
Soil Moisture ◽  
Optical Data ◽  
Estimation Accuracy ◽  
Support Vector ◽  
Practical Applications ◽  
Initial Stage ◽  
Object Based ◽  
Single Polarization ◽  
Selection Of

This paper presents an approach for retrieval of soil moisture content (SMC) by coupling single polarization C-band synthetic aperture radar (SAR) and optical data at the plot scale in vegetated areas. The study was carried out at five different sites with dominant vegetation cover located in Kenya. In the initial stage of the process, different features are extracted from single polarization mode (VV polarization) SAR and optical data. Subsequently, proper selection of the relevant features is conducted on the extracted features. An advanced state-of-the-art machine learning regression approach, the support vector regression (SVR) technique, is used to retrieve soil moisture. This paper takes a new look at soil moisture retrieval in vegetated areas considering the needs of practical applications. In this context, we tried to work at the object level instead of the pixel level. Accordingly, a group of pixels (an image object) represents the reality of the land cover at the plot scale. Three approaches, a pixel-based approach, an object-based approach, and a combination of pixel- and object-based approaches, were used to estimate soil moisture. The results show that the combined approach outperforms the other approaches in terms of estimation accuracy (4.94% and 0.89 compared to 6.41% and 0.62 in terms of root mean square error (RMSE) and R2), flexibility on retrieving the level of soil moisture, and better quality of visual representation of the SMC map.

scholarly journals Deriving surface soil moisture from reflected GNSS signal observations from a grassland site in southwestern France

2017 ◽  
Author(s):  
Sibo Zhang ◽  
Jean-Christophe Calvet ◽  
José Darrozes ◽  
Nicolas Roussel ◽  
Frédéric Frappart ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Land Surface ◽  
Land Surface Model ◽  
Ground Surface ◽  
Satellite System ◽  
Surface Model ◽  
Global Navigation Satellite ◽  
Grassland Site

Abstract. This work aims to assess the estimation of surface volumetric soil moisture (VSM) using the Global Navigation Satellite System Interferometric Reflectometry (GNSS-IR) technique. Year-round observations were acquired from a grassland site in southwestern France using an antenna consecutively placed at two contrasting heights above the ground surface (3.3 or 29.4 m). The VSM retrievals are compared with two independent reference datasets: in situ observations of soil moisture, and numerical simulations of soil moisture and vegetation biomass from the ISBA (Interactions between Soil, Biosphere and Atmosphere) land surface model. Scaled VSM estimates can be retrieved throughout the year removing vegetation effects by the separation of growth and senescence periods and by the filtering of the GNSS-IR observations that are most affected by vegetation. Antenna height has no significant impact on the quality of VSM estimates. Comparisons between the VSM GNSS-IR retrievals and the in situ VSM observations at a depth of 5 cm show a good agreement (R2 = 0.86 and RMSE = 0.04 m3 m−3). It is shown that the signal is sensitive to the grass litter water content and that this effect triggers differences between VSM retrievals and in situ VSM observations at depths of 1 cm and 5 cm, especially during light rainfall events.

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