Sensors, IoT Technologies, Modeling, and Signal Processing for Monitoring Biophysical and Physiological Signals in Plants

A special issue of Plants (ISSN 2223-7747). This special issue belongs to the section "Plant Modeling".

Deadline for manuscript submissions: closed (30 November 2023) | Viewed by 2105

Special Issue Editors


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Guest Editor
Faculty of Engineering, Autonomous University of Querétaro, Querétaro 76010, Mexico
Interests: signal processing; biosystems; plants; instrumentation
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Guest Editor
Department of Electrical and Electronic Engineering, National Technological Institute of Mexico in Celaya, Celaya, Guanajuato 38010, Mexico
Interests: thermography; plant characterization; greenhouse

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Guest Editor
Faculty of Engineering, Autonomous University of Querétaro, Querétaro 76010, Mexico
Interests: greenhouse climate; greenhouse production; Computational-Fluid-Dynamics (CFD)

Special Issue Information

Dear Colleagues,

Plants constantly face biotic and abiotic stresses which cause a reduction in yield and food availability worldwide. In order to face these threats, plants generate physiological and biophysical signals, which can measure if they are being affected and by how much in order to adopt an early and adequate management strategy or to measure the effect of stress treatments to increase defensive secondary metabolites; these signals, which are related to biochemical, enzymatic, and molecular activity, can be detected using suitable sensors that, in many cases, incorporate sophisticated technology that improves the precision of the measurements as well as the suitability, transmission, and analysis of data. Some physiological and biophysical signals present in plants include photosynthesis; transpiration; root, stem, and leaf temperature; chlorophyll fluorescence; visible symptomatology; vibration; sound; electricity; etc. These signals are often measured conjointly with data-processing techniques, using sensors of images, relative humidity, temperature, CO2, etc.

This Special Issue covers, but is not limited to, sensors and IoT technologies used to monitor such signals in plants, data-processing techniques, and modeling to relate biochemical, enzymatic, and molecular activities to biophysical and physiological signals.

Dr. Luis M. Contreras-Medina
Dr. Jose Alfredo Padilla-Medina
Dr. Enrique Rico-Garcia
Guest Editors

Manuscript Submission Information

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Keywords

  • sensors
  • IoT technology
  • biophysical and physiological signals in plants
  • signal processing
  • modeling
  • biochemical and molecular signals

Published Papers (1 paper)

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Research

9 pages, 1048 KiB  
Communication
A Model for the Determination of Potato Tuber Mass by the Measurement of Carbon Dioxide Concentration
by Boris Rumiantsev, Sofya Dzhatdoeva, Elchin Sadykhov and Azret Kochkarov
Plants 2023, 12(16), 2962; https://doi.org/10.3390/plants12162962 - 16 Aug 2023
Cited by 2 | Viewed by 1221
Abstract
The implementation of advanced precision farming systems, which are becoming relevant due to rapid technological development, requires the invention of new approaches to the diagnostics and control of the growing process of cultivated crops. This is especially relevant for potato, as it is [...] Read more.
The implementation of advanced precision farming systems, which are becoming relevant due to rapid technological development, requires the invention of new approaches to the diagnostics and control of the growing process of cultivated crops. This is especially relevant for potato, as it is one of the most demanded crops in the world. In the present work, an analytic model of the dependence of potato tubers mass on carbon dioxide concentration under cultivation in a closed vegetation system is presented. The model is based on the quantitative description of starch molecule synthesis from carbon dioxide under photosynthesis. In the frame of this work, a comprehensive description of the proposed model is presented, and the verification of this model was conducted on the basis of experimental data from a closed urban vertical farm with automated climate control. The described model can serve as a basis for the non-contact non-invasive real-time measurement of potato tuber mass under growth in closed vegetation systems, such as vertical farms and greenhouses, as well as orbital and space crop production systems. Full article
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