Spatial Root Zone Soil Water Content Estimation in Agricultural Lands Using Bayesian-Based Artificial Neural Networks and High- Resolution Visual, NIR, and Thermal Imagery

2017 ◽  
Vol 66 (2) ◽  
pp. 273-288 ◽  
Author(s):  
Leila Hassan-Esfahani ◽  
Alfonso Torres-Rua ◽  
Austin Jensen ◽  
Mac Mckee
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
High Resolution ◽  
Water Content ◽  
Soil Water ◽  
Soil Water Content ◽  
Root Zone ◽  
Agricultural Lands ◽  
Thermal Imagery ◽  
Artificial Neural

Estimation of soil water content in watershed using artificial neural networks

2017 ◽  
Vol 62 (13) ◽  
pp. 2120-2138 ◽  
Author(s):  
Marquis Henrique Campos de Oliveira ◽  
Vanessa Sari ◽  
Nilza Maria dos Reis Castro ◽  
Olavo Correa Pedrollo
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Water Content ◽  
Soil Water ◽  
Soil Water Content ◽  
Artificial Neural

scholarly journals Modelling the Impact on Root Water Uptake and Solute Return Flow of Different Drip Irrigation Regimes with Brackish Water

Water ◽  
2019 ◽  
Vol 11 (3) ◽  
pp. 425 ◽  
Author(s):  
Fairouz Slama ◽  
Nessrine Zemni ◽  
Fethi Bouksila ◽  
Roberto De Mascellis ◽  
Rachida Bouhlila
Keyword(s):  
Water Content ◽  
Soil Water ◽  
Soil Water Content ◽  
Water Uptake ◽  
Brackish Water ◽  
Deficit Irrigation ◽  
Root Zone ◽  
Root Water Uptake ◽  
Return Flows ◽  
Semi Arid

Water scarcity and quality degradation represent real threats to economic, social, and environmental development of arid and semi-arid regions. Drip irrigation associated to Deficit Irrigation (DI) has been investigated as a water saving technique. Yet its environmental impacts on soil and groundwater need to be gone into in depth especially when using brackish irrigation water. Soil water content and salinity were monitored in a fully drip irrigated potato plot with brackish water (4.45 dSm−1) in semi-arid Tunisia. The HYDRUS-1D model was used to investigate the effects of different irrigation regimes (deficit irrigation (T1R, 70% ETc), full irrigation (T2R, 100% ETc), and farmer’s schedule (T3R, 237% ETc) on root water uptake, root zone salinity, and solute return flows to groundwater. The simulated values of soil water content (θ) and electrical conductivity of soil solution (ECsw) were in good agreement with the observation values, as indicated by mean RMSE values (≤0.008 m3·m−3, and ≤0.28 dSm−1 for soil water content and ECsw respectively). The results of the different simulation treatments showed that relative yield accounted for 54%, 70%, and 85.5% of the potential maximal value when both water and solute stress were considered for deficit, full. and farmer’s irrigation, respectively. Root zone salinity was the lowest and root water uptake was the same with and without solute stress for the treatment corresponding to the farmer’s irrigation schedule (273% ETc). Solute return flows reaching the groundwater were the highest for T3R after two subsequent rainfall seasons. Beyond the water efficiency of DI with brackish water, long term studies need to focus on its impact on soil and groundwater salinization risks under changing climate conditions.

scholarly journals A MODEL FOR ESTIMATING ACTUAL EVAPOTRANSPIRATION FROM POTENTIAL EVAPOTRANSPIRATION

1975 ◽  
Vol 6 (3) ◽  
pp. 170-188 ◽  
Author(s):  
K. J. KRISTENSEN ◽  
S. E. JENSEN
Keyword(s):  
Water Content ◽  
Soil Water ◽  
Soil Water Content ◽  
Root Zone ◽  
Potential Evapotranspiration ◽  
Actual Evapotranspiration ◽  
Vegetation Density ◽  
Rainfall Distribution ◽  
Sugar Beets

A model for calculating the daily actual evapotranspiration based on the potential one is presented. The potential evapotranspiration is reduced according to vegetation density, water content in the root zone, and the rainfall distribution. The model is tested by comparing measured (EAm) and calculated (EAc) evapotranspirations from barley, fodder sugar beets, and grass over a four year period. The measured and calculated values agree within 10 %. The model also yields information on soil water content and runoff from the root zone.

The Effect of Dry Pegging Zone Soil on Pod Formation of Florunner Peanut1

1997 ◽  
Vol 24 (1) ◽  
pp. 19-24 ◽  
Author(s):  
P. J. Sexton ◽  
J. M. Bennett ◽  
K. J. Boote
Keyword(s):  
Drought Stress ◽  
Water Content ◽  
Soil Water ◽  
Soil Water Content ◽  
Water Deficit ◽  
Growth Rates ◽  
Root Zone ◽  
Soil Water Deficit ◽  
Seed Growth ◽  
Pod Development

Abstract Peanut (Arachis hypogaea L.) fruit growth is sensitive to surface soil (0-5 cm) conditions due to its subterranean fruiting habit. This study was conducted to determine the effect of soil water content in the pegging zone (0-5 cm) on peanut pod growth rate and development. A pegging-pan-root-tube apparatus was used to separately control soil water content in the pegging and root zone for greenhouse trials. A field study also was conducted using portable rainout shelters to create a soil water deficit. Pod phenology, pod and seed growth rates, and final pod and seed dry weights were determined. In greenhouse studies, dry pegging zone soil delayed pod and seed development. In the field, soil water deficits in the pegging and root zone decreased pod and seed growth rates by approximately 30% and decreased weight per seed from 563 to 428 mg. Pegs initiating growth during drought stress demonstrated an ability to suspend development during the period of soil water deficit and to re-initiate pod development after the drought stress was relieved.

scholarly journals Large-scale, high-resolution comparison of the core visual object recognition behavior of humans, monkeys, and state-of-the-art deep artificial neural networks

2018 ◽  
Author(s):  
Rishi Rajalingham ◽  
Elias B. Issa ◽  
Pouya Bashivan ◽  
Kohitij Kar ◽  
Kailyn Schmidt ◽  
...  
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
High Resolution ◽  
Large Scale ◽  
State Of The Art ◽  
Neural Mechanisms ◽  
Mechanistic Models ◽  
Object Discrimination ◽  
Ann Models ◽  
Artificial Neural

ABSTRACTPrimates—including humans—can typically recognize objects in visual images at a glance even in the face of naturally occurring identity-preserving image transformations (e.g. changes in viewpoint). A primary neuroscience goal is to uncover neuron-level mechanistic models that quantitatively explain this behavior by predicting primate performance for each and every image. Here, we applied this stringent behavioral prediction test to the leading mechanistic models of primate vision (specifically, deep, convolutional, artificial neural networks; ANNs) by directly comparing their behavioral signatures against those of humans and rhesus macaque monkeys. Using high-throughput data collection systems for human and monkey psychophysics, we collected over one million behavioral trials for 2400 images over 276 binary object discrimination tasks. Consistent with previous work, we observed that state-of-the-art deep, feed-forward convolutional ANNs trained for visual categorization (termed DCNNIC models) accurately predicted primate patterns of object-level confusion. However, when we examined behavioral performance for individual images within each object discrimination task, we found that all tested DCNNIC models were significantly non-predictive of primate performance, and that this prediction failure was not accounted for by simple image attributes, nor rescued by simple model modifications. These results show that current DCNNIC models cannot account for the image-level behavioral patterns of primates, and that new ANN models are needed to more precisely capture the neural mechanisms underlying primate object vision. To this end, large-scale, high-resolution primate behavioral benchmarks—such as those obtained here—could serve as direct guides for discovering such models.SIGNIFICANCE STATEMENTRecently, specific feed-forward deep convolutional artificial neural networks (ANNs) models have dramatically advanced our quantitative understanding of the neural mechanisms underlying primate core object recognition. In this work, we tested the limits of those ANNs by systematically comparing the behavioral responses of these models with the behavioral responses of humans and monkeys, at the resolution of individual images. Using these high-resolution metrics, we found that all tested ANN models significantly diverged from primate behavior. Going forward, these high-resolution, large-scale primate behavioral benchmarks could serve as direct guides for discovering better ANN models of the primate visual system.

Effects of Typical Chemical Agents on Preventing Pollution and Controlling Drought in a Cherry Orchard

2012 ◽  
Vol 550-553 ◽  
pp. 1340-1344
Author(s):  
Ren Kuan Liao ◽  
Pei Ling Yang ◽  
Shu Mei Ren ◽  
Hang Yi ◽  
Long Wang ◽  
...  
Keyword(s):  
Water Content ◽  
Soil Water ◽  
Soil Water Content ◽  
North China Plain ◽  
North China ◽  
Root Zone ◽  
Soil Layer ◽  
The North ◽  
The North China Plain ◽  
Chemical Agents

In the North China plain, serious Non-point-source (NPS) pollution and drought are two great concerns in agricultural production. In our studies, two typical chemical agents ( SAP and FA ) were selected to control drought and pollution in a cheery orchard. Soil water content, nutrient transport in soil profile have been researched. The results showed that the soil water content of treatments with chemical agents increased maximally by 19.4% relative to treatment without chemical agents, and increased by 35.2% for Ammonium-N in 20-60 cm soil layer ( main root zone ). However, in 60-120 cm deeper soil layer, the water leakage of treatments with chemical agents decreased averagely by 15.1% relative to treatment without chemical agents, and increased by 43.8% for Nitrate-N. The chemical agents hold water and nutrient in root zone and thus reducing the risk of pollutant leaching into the underground water. It can be found that treatment ( 150kg/h㎡ SAP + 300 times FA ) is the optimal combination group in all treatments. The chemical prevention technology provided a new guide for controlling drought and reducing NPS pollution in cherry planting in the North China plain.

Water drawdown by radish (Raphanus sativus L.) multiple-root systems evaluated using computed tomography

Soil Research ◽  
2008 ◽  
Vol 46 (3) ◽  
pp. 228
Author(s):  
M. A. Hamza ◽  
S. H. Anderson ◽  
L. A. G. Aylmore
Keyword(s):  
Computed Tomography ◽  
Water Content ◽  
Soil Water ◽  
Soil Water Content ◽  
Water Uptake ◽  
Root Zone ◽  
Multiple Root ◽  
Root Systems ◽  
Bulk Soil ◽  
Water Drawdown

Although measurements of water drawdown by single radish root systems have been previously published by the authors, further research is needed to evaluate water drawdown patterns in multiple-root systems. The objective of this study was to compare water transpiration patterns estimated using X-ray computed tomography (CT) with the traditional gravimetric method and to evaluate the effects of variably spaced multiple root systems on soil water content and corresponding water content gradients. Water drawdown showed a dual pattern in which it increased rapidly when soil water content was high at the beginning of transpiration, then slowed down to an almost constant level with time as water content decreased. These results contrast with the single-root system wherein transpiration rates initially increased rapidly and then slowly increased with time. Water uptake estimated using the CT method was observed to be 27–38% lower than the gravimetrically estimated water uptake; this difference was attributed to lower water uptake for the upper 30 mm layer (CT measured) than lower layers due to differences in root density. However, good correlation (r = 0.97) was found between both measurement methods. The drawdown patterns for multiple root systems showed a convex shape from the root surface to the bulk soil, compared with a nearly linear shape for single roots. The water content drawdown areas and the drawdown distances for multiple root systems were found to be much larger than those corresponding to single radish roots. Differential water content gradients were observed for roots spaced at 15-mm distances compared with 3–4-mm distances. These differential gradients from the bulk soil towards the root-zone occurred probably creating localised water potential gradients within the root-zone, which moved water from between roots to root surfaces. The lowest water content values were located in the inter-root areas. The CT-scanned layer probably acted as one drawdown area with particularly higher water drawdown from the inter-root areas.

scholarly journals Using monitoring data of surface soil to predict whole crop-root zone soil water content with PSO-LSSVM, GRNN and WNN

2013 ◽  
Vol 7 (1) ◽  
pp. 59-68 ◽  
Author(s):  
Zhao Lixi ◽  
Shui Pengbo ◽  
Jiang Fang ◽  
Qiu Hengqing ◽  
Ren Shumei ◽  
...  
Keyword(s):  
Water Content ◽  
Soil Water ◽  
Soil Water Content ◽  
Surface Soil ◽  
Root Zone ◽  
Monitoring Data ◽  
Crop Root
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