Stimulation of soy seeds using environmentally friendly magnetic and
electric fields
- URL: http://arxiv.org/abs/2211.09240v1
- Date: Wed, 16 Nov 2022 22:05:58 GMT
- Title: Stimulation of soy seeds using environmentally friendly magnetic and
electric fields
- Authors: Agata Dziwulska-Hunek, Agnieszka Niemczynowicz, Rados{\l}aw A. Kycia,
Arkadiusz Matwijczuk, Krzysztof Kornarzy\'nski, Joanna Stadnik, Mariusz
Szymanek
- Abstract summary: The study analyzes the impact of constant and alternating magnetic fields and alternating electric fields on various growth parameters of soy plants.
The use of ELM (Electromagnetic) fields had a positive impact on the germination rate in Merlin plants.
An increase in terms of emergence and number of plants after seed stimulation was observed for the Mavka cultivar.
- Score: 0.0
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: The study analyzes the impact of constant and alternating magnetic fields and
alternating electric fields on various growth parameters of soy plants: the
germination energy and capacity, plants emergence and number, the Yield(II) of
the fresh mass of seedlings, protein content, and photosynthetic parameters.
Four cultivars were used: MAVKA, MERLIN, VIOLETTA, and ANUSZKA. Moreover, the
advanced Machine Learning processing pipeline was proposed to distinguish the
impact of physical factors on photosynthetic parameters. It is possible to
distinguish exposition on different physical factors for the first three
cultivars; therefore, it indicates that the EM factors have some observable
effect on soy plants. Moreover, some influence of physical factors on growth
parameters was observed. The use of ELM (Electromagnetic) fields had a positive
impact on the germination rate in Merlin plants. The highest values were
recorded for the constant magnetic field (CMF) - Merlin, and the lowest for the
alternating electric field (AEF) - Violetta. An increase in terms of emergence
and number of plants after seed stimulation was observed for the Mavka
cultivar, except for the AEF treatment (number of plants after 30 days) (...)
Related papers
- Predicting potato plant vigor from the seed tuber properties [0.0]
The vigor of potato plants depends on the origin and physiological state of the seed tuber.
Experiments carried out with six potato varieties in three test fields over three years show a 73%-90% correlation in the vigor of the plants from the same seedlot grown in different test fields.
arXiv Detail & Related papers (2024-10-24T10:05:38Z) - Topological transitions in quantum jump dynamics: Hidden exceptional points [45.58759752275849]
Phenomena associated with exceptional points (EPs) have been extensively studied in relation to superconducting circuits.
We consider a monitored three level system and find multiple EPs in the Lindbladian eigenvalues considered as functions of a counting field.
We identify dynamical observables affected by these transitions and demonstrate how the underlying topology can be recovered from experimentally measured quantum jump distributions.
arXiv Detail & Related papers (2024-08-09T18:00:02Z) - VegeDiff: Latent Diffusion Model for Geospatial Vegetation Forecasting [58.12667617617306]
We propose VegeDiff for the geospatial vegetation forecasting task.
VegeDiff is the first to employ a diffusion model to probabilistically capture the uncertainties in vegetation change processes.
By capturing the uncertainties in vegetation changes and modeling the complex influence of relevant variables, VegeDiff outperforms existing deterministic methods.
arXiv Detail & Related papers (2024-07-17T14:15:52Z) - Inferring the relationship between soil temperature and the normalized
difference vegetation index with machine learning [0.3613661942047476]
Changes in climate can greatly affect the phenology of plants, which can have important feedback effects.
In this study, we investigated the effect of soil temperature on the timing of the start of the season.
We also explored the impact of other meteorological variables, including air temperature, precipitation, and irradiance, on the inter-annual variation in vegetation phenology.
arXiv Detail & Related papers (2023-12-19T15:43:50Z) - A Causal Framework for Decomposing Spurious Variations [68.12191782657437]
We develop tools for decomposing spurious variations in Markovian and Semi-Markovian models.
We prove the first results that allow a non-parametric decomposition of spurious effects.
The described approach has several applications, ranging from explainable and fair AI to questions in epidemiology and medicine.
arXiv Detail & Related papers (2023-06-08T09:40:28Z) - Sensing of magnetic field effects in radical-pair reactions using a
quantum sensor [50.591267188664666]
Magnetic field effects (MFE) in certain chemical reactions have been well established in the last five decades.
We employ elaborate and realistic models of radical-pairs, considering its coupling to the local spin environment and the sensor.
For two model systems, we derive signals of MFE detectable even in the weak coupling regime between radical-pair and NV quantum sensor.
arXiv Detail & Related papers (2022-09-28T12:56:15Z) - Total Nitrogen Estimation in Agricultural Soils via Aerial Multispectral
Imaging and LIBS [0.6875312133832077]
Most existing methods to measure soil health indicators (SHIs) are in-lab wet chemistry or spectroscopy-based methods.
We develop an artificial intelligence (AI)-driven near real-time unmanned aerial vehicle (UAV)-based multispectral sensing (UMS) solution to estimate total nitrogen (TN) of the soil.
arXiv Detail & Related papers (2021-07-06T02:37:30Z) - Temporal Prediction and Evaluation of Brassica Growth in the Field using
Conditional Generative Adversarial Networks [1.2926587870771542]
The prediction of plant growth is a major challenge, as it is affected by numerous and highly variable environmental factors.
This paper proposes a novel monitoring approach that comprises high- throughput imaging sensor measurements and their automatic analysis.
Our approach's core is a novel machine learning-based growth model based on conditional generative adversarial networks.
arXiv Detail & Related papers (2021-05-17T13:00:01Z) - A CNN Approach to Simultaneously Count Plants and Detect Plantation-Rows
from UAV Imagery [56.10033255997329]
We propose a novel deep learning method based on a Convolutional Neural Network (CNN)
It simultaneously detects and geolocates plantation-rows while counting its plants considering highly-dense plantation configurations.
The proposed method achieved state-of-the-art performance for counting and geolocating plants and plant-rows in UAV images from different types of crops.
arXiv Detail & Related papers (2020-12-31T18:51:17Z) - Eurythmic Dancing with Plants -- Measuring Plant Response to Human Body
Movement in an Anthroposophic Environment [0.0]
In particular, body movement of a human conducting eurythmic dances is correlated with the action potential measured by a plant SpikerBox.
The first experiment shows that our measurement system captures external stimuli identically for different plants.
The second experiment illustrates that the plants' response is correlated to the movements of the dancer.
The third experiment indicates that plants that have been exposed for multiple weeks to eurythmic dancing might respond differently to plants which are exposed for the first time to eurythmic dancing.
arXiv Detail & Related papers (2020-12-23T21:14:54Z) - Estimating Crop Primary Productivity with Sentinel-2 and Landsat 8 using
Machine Learning Methods Trained with Radiative Transfer Simulations [58.17039841385472]
We take advantage of all parallel developments in mechanistic modeling and satellite data availability for advanced monitoring of crop productivity.
Our model successfully estimates gross primary productivity across a variety of C3 crop types and environmental conditions even though it does not use any local information from the corresponding sites.
This highlights its potential to map crop productivity from new satellite sensors at a global scale with the help of current Earth observation cloud computing platforms.
arXiv Detail & Related papers (2020-12-07T16:23:13Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.