scholarly journals Modelling the functional dependency between root and shoot compartments to predict the impact of the environment on the architecture of the whole plant. Methodology for model fitting on simulated data using Deep Learning techniques

2021 ◽  
Author(s):  
Abel Louis Masson ◽  
Yves Caraglio ◽  
Eric Nicolini ◽  
Philippe Borianne ◽  
Jean-Francois Barczi
Keyword(s):  
Neural Network ◽  
Climate Change ◽  
Model Fitting ◽  
Simulated Data ◽  
Structural Plasticity ◽  
Tree Roots ◽  
Mean Error ◽  
Whole Plant ◽  
Growing Conditions ◽  
The Impact

Abstract Tree structural and biomass growth studies mainly focus on the shoot compartment. Tree roots usually have to be taken apart due to the difficulties involved in measuring and observing this compartment, particularly root growth. In the context of climate change, the study of tree structural plasticity has become crucial and both shoot and root systems need to be considered simultaneously as they play a joint role in adapting traits to climate change (water availability for roots and light or carbon availability for shoots). We developed a botanically accurate whole-plant model and its simulator (RoCoCau) with a linkable external module (TOY) to represent shoot and root compartment dependencies and hence tree structural plasticity in different air and soil environments. This paper describes a new deep neural network calibration trained on simulated datasets computed from a set of more than 360 000 random TOY parameter values and random climate values. These datasets were used for training and for validation. For this purpose, we chose Voxnet, a convolutional neural network designed to classify 3D objects represented as a voxelized scene. We recommend further improvements for Voxnet inputs, outputs, and training. We were able to teach the network to predict the value of environment data well (mean error < 2%), and to predict the value of TOY parameters for plants under water stress conditions (mean error < 5% for all parameters), and for any environmental growing conditions (mean error < 20%).

scholarly journals Effect of salinity on the zinc(II) binding efficiency of siderophore functional groups and implications for salinity tolerance mechanisms in barley

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
George H. R. Northover ◽  
Yiru Mao ◽  
Haris Ahmed ◽  
Salvador Blasco ◽  
Ramon Vilar ◽  
...  
Keyword(s):  
Climate Change ◽  
Functional Groups ◽  
Salinity Tolerance ◽  
Metal Binding ◽  
Micronutrient Deficiencies ◽  
Ecosystem Productivity ◽  
Tolerance Mechanisms ◽  
Whole Plant ◽  
Chelating Ability ◽  
The Impact

AbstractBacteria, fungi and grasses use siderophores to access micronutrients. Hence, the metal binding efficiency of siderophores is directly related to ecosystem productivity. Salinization of natural solutions, linked to climate change induced sea level rise and changing precipitation patterns, is a serious ecological threat. In this study, we investigate the impact of salinization on the zinc(II) binding efficiency of the major siderophore functional groups, namely the catecholate (for bacterial siderophores), α-hydroxycarboxylate (for plant siderophores; phytosiderophores) and hydroxamate (for fungal siderophores) bidentate motifs. Our analysis suggests that the order of increasing susceptibility of siderophore classes to salinity in terms of their zinc(II) chelating ability is: hydroxamate < catecholate < α-hydroxycarboxylate. Based on this ordering, we predict that plant productivity is more sensitive to salinization than either bacterial or fungal productivity. Finally, we show that previously observed increases in phytosiderophore release by barley plants grown under salt stress in a medium without initial micronutrient deficiencies, are in line with the reduced zinc(II) binding efficiency of the α-hydroxycarboxylate ligand and hence important for the salinity tolerance of whole-plant zinc(II) status.

scholarly journals DeepLocate: Smartphone Based Indoor Localization with a Deep Neural Network Ensemble Classifier

Sensors ◽  
2019 ◽  
Vol 20 (1) ◽  
pp. 133 ◽  
Author(s):  
Imran Ashraf ◽  
Soojung Hur ◽  
Sangjoon Park ◽  
Yongwan Park
Keyword(s):  
Neural Network ◽  
Deep Neural Network ◽  
Indoor Localization ◽  
Ensemble Classifier ◽  
Location Based Services ◽  
Magnetic Data ◽  
Neural Network Ensemble ◽  
Localization Accuracy ◽  
Mean Error ◽  
The Impact

A quickly growing location-based services area has led to increased demand for indoor positioning and localization. Undoubtedly, Wi-Fi fingerprint-based localization is one of the promising indoor localization techniques, yet the variation of received signal strength is a major problem for accurate localization. Magnetic field-based localization has emerged as a new player and proved a potential indoor localization technology. However, one of its major limitations is degradation in localization accuracy when various smartphones are used. The localization performance is different from various smartphones even with the same localization technique. This research leverages the use of a deep neural network-based ensemble classifier to perform indoor localization with heterogeneous devices. The chief aim is to devise an approach that can achieve a similar localization accuracy using various smartphones. Features extracted from magnetic data of Galaxy S8 are fed into neural networks (NNs) for training. The experiments are performed with Galaxy S8, LG G6, LG G7, and Galaxy A8 smartphones to investigate the impact of device dependence on localization accuracy. Results demonstrate that NNs can play a significant role in mitigating the impact of device heterogeneity and increasing indoor localization accuracy. The proposed approach is able to achieve a localization accuracy of 2.64 m at 50% on four different devices. The mean error is 2.23 m, 2.52 m, 2.59 m, and 2.78 m for Galaxy S8, LG G6, LG G7, and Galaxy A8, respectively. Experiments on a publicly available magnetic dataset of Sony Xperia M2 using the proposed approach show a mean error of 2.84 m with a standard deviation of 2.24 m, while the error at 50% is 2.33 m. Furthermore, the impact of devices on various attitudes on the localization accuracy is investigated.

scholarly journals Evaluation of the production process of potato in a changing climate in the Eastern and Western forest-steppe

2017 ◽  
pp. 120-127
Author(s):  
S.M. Svyderska
Keyword(s):  
Climate Change ◽  
Climatic Conditions ◽  
Future Climate Change ◽  
Forest Steppe ◽  
Herbaceous Plant ◽  
Climate Change Scenarios ◽  
Perennial Herbaceous Plant ◽  
Photosynthetic Productivity ◽  
Growing Conditions ◽  
The Impact

An important element of climate change is to assess changes in agro-climatic growing conditions of crops and the impact of these changes on their performance. Agriculture is the most vulnerable sector of  Ukraine's economy to fluctuations and climate change. Given the inertial nature of agriculture and the dependence of the efficiency on the weather, now need to make timely and adequate solutions to complex problems caused by climate change. Due to the expected increase in air temperature of the Northern Hemisphere food security Ukraine will largely depend on how effectively adapting agriculture to future climate change. This includes advance assessment of the impact of the expected climate change on agro-climatic conditions for growing crops. Potatoes - perennial, herbaceous, plant, but in nature is treated as an annual plant, so that the life cycle, beginning with germination and ending with the formation of bubbles and the formation of mature tubers, is one growing season. Potato is one of the most important crops grown and diversified use in almost all parts of our country. But the main focus areas of potatoes in Polesie and Forest-steppe. We consider the relative performance of the photosynthetic productivity of potato and agro-climatic conditions for growing potatoes for the period 1986 to 2005, and expected their changes calculated by the climate change scenarios A1B and A2 for the period 2011 to 2050 in Eastern and Western Forest-Steppe. We consider the agrometeorological and agro-climatic conditions in which there may be a maximum performance of potato.

scholarly journals Modern Transformations of Geosystems of the Western Macroslope of the Barguzin Ridge

2021 ◽  
Vol 38 ◽  
pp. 88-99
Author(s):  
Z. O. Litvintseva ◽  
Keyword(s):  
Climate Change ◽  
Environmental Factors ◽  
Forest Fires ◽  
Factors Affecting ◽  
The Past ◽  
Natural Restoration ◽  
The Republic ◽  
Growing Conditions ◽  
The Impact ◽  
Natural Territory

Forest fires are one of the most important environmental factors affecting the environment. Due to climate change and the increasing frequency of forest fires, studies of the consequences of forest fires and the processes of restoration of disturbed geosystems are relevant. Over the past 20 years, there has been an increase in the frequency of fires on the territory of the Republic of Buryatia as a whole, and the western macro slope of the Barguzin ridge in particular. The situation is aggravated by the fact that a significant part of the fires occur in hardto- reach areas of the ridge, which complicates their elimination. The paper presents the results of observations (2015-2020 years) on the impact of forest fires on the taiga geosystems of the western macroslope of the Barguzin ridge. The features of post-fire restoration of geosystems are considered. The natural restoration of forests depends on the nature of the forest growing conditions and the ecological characteristics of the stands. Restoration of dark coniferous-taiga geosystems, including relict ones, after intense fires has not been revealed, since forest growing conditions are changing. The relevance of the research is also related to the fact that the western macroslope of the Barguzin Ridge is located within the Baikal Natural Territory (BPT), where protected areas are located and it is not uncommon for fires to disrupt relict geosystems that are under protection.

scholarly journals Protection against falling rocks on roads in a mountainous area - Experiences from Switzerland

2021 ◽  
Vol 67 (1) ◽  
pp. 43-46
Author(s):  
Vjekoslav Budimir ◽  
Eberhard Gröner
Keyword(s):  
Climate Change ◽  
Everyday Life ◽  
Significant Risk ◽  
Mountainous Area ◽  
Freezing And Thawing ◽  
Tree Roots ◽  
Mountain Ranges ◽  
Human Need ◽  
The Impact ◽  
Digital Or

Whether digital or real - mobility is a basic human need of everyday life in all segments. Therefore, the importance of roads and railways continues to gain in importance even in the age of digital highways. Many traffic routes have a century-old tradition. They are located in valleys and notches for overcoming mountains and mountain ranges. The design and maintenance of these traffic routes are particularly complex. An important part of this task is protection against stone impact in the mountains, but also in hilly areas. Even if there was no danger of rocks falling for decades or it was not known: due to climate change, the impact of water, the process of freezing and thawing and the growth of tree roots, there can be a significant risk.

scholarly journals Changes to Land Area Used for Grain Maize Production in Central Europe due to Predicted Climate Change

2019 ◽  
Vol 2019 ◽  
pp. 1-9 ◽  
Author(s):  
Petr Pavlik ◽  
Veronika Vlckova ◽  
Ivo Machar
Keyword(s):  
Climate Change ◽  
Central Europe ◽  
Detailed Knowledge ◽  
Agricultural Crop ◽  
Crop Species ◽  
Study Region ◽  
Impact Of Climate Change ◽  
Vegetation Zones ◽  
Growing Conditions ◽  
The Impact

Regional biogeographical models are considered to be important tools for supporting decisions relating to sustainable agricultural planning for climate change. These models are useful for a better understanding of the impact of climate change on individual crop species due to their sensitivity to regional ecological conditions. This paper deals with the application of a regional biogeographical model in order to predict the impact of climate change on growing conditions for grain maize in Central Europe. The model is based on a detailed knowledge of the relationships between the climatic characteristics of vegetation zones in landscapes with ecological growing conditions suitable for grain maize in the region under study. The results gained from using the model indicate a substantial increase in the total area suitable for growing of grain maize in the study region. By 2070, this area is expected to be triple the size it is today. Special maps are used to visualize prediction scenarios in order to support decision-making in regional planning in the study region, where grain maize is an important agricultural crop. This biogeographical model can be used in other European regions, where basic data related to vegetation zones are available.

Research on the Impact of Climate Cactors on Wood Width Based on the Improved RBF Model

2012 ◽  
Vol 461 ◽  
pp. 717-720
Author(s):  
Qi Yue ◽  
Zhuo Ran Lv ◽  
Xue Mei Guan
Keyword(s):  
Neural Network ◽  
Climate Change ◽  
Prediction Model ◽  
Physical Properties ◽  
Rbf Neural Network ◽  
Wood Formation ◽  
Ground Temperature ◽  
Climate Factors ◽  
Poplar Plantation ◽  
The Impact

Choosing cathay poplar as research object ,with the aim to reveal that how the climate change may affect the wood formation, by using the improved RBF neural network the paper studies the affects of the climate factors such as temperature, sunhine, rainfall and ground temperature on the physical properties of cathay poplar plantation and establish a prediction model finally. The simulation was carried out and the results shows that the error is less than 2.8%.

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