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Plants
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Sensors and Information Technologies for Plant Development Monitoring
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Sensors and Information Technologies for Plant Development Monitoring

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

Deadline for manuscript submissions: closed (20 April 2022) | Viewed by 14757

Special Issue Editors

Prof. Dr. Jesus R. Millan-Almaraz

E-Mail Website
Guest Editor
Faculty of Physics and Mathematics, Autonomous University of Sinaloa, Universitarios Blvd.De las Americas Ave.Cd. Universitaria, Culiacan, Sinaloa 80000, Mexico
Interests: agricultural electronics; photosynthesis measurement; vegetation indexes; instrumentation; signal processing; sensor networks
Dr. Luis M. Contreras-Medina

E-Mail Website
Guest Editor
Faculty of Engineering, Autonomous University of Querétaro, Querétaro 76010, Mexico
Interests: signal processing; biosystems; plants; instrumentation
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Plant development is a very important discipline of plant sciences that is constantly evolving in the way to measure, monitor, and analyze its phenomena. Currently, many productive sectors are entering in the Industry 4.0 society; it refers to a new era where information technologies are changing all sciences by adding the value of data concept. Sensors, computer vision, and artificial intelligence are some of these information technologies that are proposing new ways and possibilities for plant sciences. Furthermore, plant sensing technologies have evolved rapidly in the last years by incorporating new remote sensing technologies, biosensors, instrumentation, computer hardware, software, and data analytics in a new way where new data can be obtained in novel ways. In addition, it is important to mention that there is still much to be discovered about plant development by combining electronics, informatics, chemistry, and physics among other disciplines in a multidisciplinary way.

Prof. Dr. Jesus R. Millan-almaraz
Dr. Luis M. Contreras-Medina
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Plants is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2700 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • plant development monitoring
  • agricultural sensors
  • remote sensing
  • biosensors
  • phytopathometry
  • plant modeling

Published Papers (4 papers)

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Research

24 pages, 2116 KiB  
Article
Roadmapping 5.0 Technologies in Agriculture: A Technological Proposal for Developing the Coffee Plant Centered on Indigenous Producers’ Requirements from Mexico, via Knowledge Management
by David Israel Contreras-Medina, Sergio Ernesto Medina-Cuéllar and Juan Manuel Rodríguez-García
Plants 2022, 11(11), 1502; https://doi.org/10.3390/plants11111502 - 3 Jun 2022
Cited by 7 | Viewed by 2860
Abstract
The coffee plant, with more than 40 billion shrubs, 9 million tons of grains produced, and 80% of its production accounted for by small-scale producers, has been severely damaged since the emergence of Hemileia vastatrix and Hypothenemus hampei. Despite technological support, these [...] Read more.
The coffee plant, with more than 40 billion shrubs, 9 million tons of grains produced, and 80% of its production accounted for by small-scale producers, has been severely damaged since the emergence of Hemileia vastatrix and Hypothenemus hampei. Despite technological support, these pests have caused 20% to 40% production losses, a 50% to 60% deficit in performance, and a cost of between USD 70 million and USD 220 million to the world economies, which forces us to rethink actions centered on people as the key elements to develop appropriate solutions. For this, the present study presents a technological proposal centered on small indigenous coffee producer requirements for introducing Industry 5.0 technologies, considering roadmapping, knowledge management, statistical analysis, and the social, productive, and digital contexts of five localities in Mexico. The results show a correlation between monitoring and control, soil analysis, the creation of organic fertilizers, accompaniment, and coffee experimentation, as the actions to be implemented, proposing the introduction of a mobile application; sensors, virtual platforms, dome-shaped greenhouses, and spectrophotometric technology as relevant technologies centered on indigenous coffee producers’ requirements. This study is important for policymakers, academics, and producers who wish to develop strategies centered on people in Mexico and the world. Full article
(This article belongs to the Special Issue Sensors and Information Technologies for Plant Development Monitoring)
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11 pages, 1774 KiB  
Article
Comparative Analysis of the NDVI and NGBVI as Indicators of the Protective Effect of Beneficial Bacteria in Conditions of Biotic Stress
by Nallely Solano-Alvarez, Juan Antonio Valencia-Hernández, Santiago Vergara-Pineda, Jesús Roberto Millán-Almaraz, Irineo Torres-Pacheco and Ramón Gerardo Guevara-González
Plants 2022, 11(7), 932; https://doi.org/10.3390/plants11070932 - 30 Mar 2022
Cited by 7 | Viewed by 2005
Abstract
Precision agriculture has the objective of improving agricultural yields and minimizing costs by assisting management with the use of sensors, remote sensing, and information technologies. There are several approaches to improving crop yields where remote sensing has proven to be an important methodology [...] Read more.
Precision agriculture has the objective of improving agricultural yields and minimizing costs by assisting management with the use of sensors, remote sensing, and information technologies. There are several approaches to improving crop yields where remote sensing has proven to be an important methodology to determine agricultural maps to show surface differences which may be associated with many phenomena. Remote sensing utilizes a wide variety of image sensors that range from common RGB cameras to sophisticated, hyper-spectral image cameras which acquire images from outside the visible electromagnetic spectrum. The NDVI and NGBVI are computer vision vegetation index algorithms that perform operations from color masks such as red, green, and blue from RGB cameras and hyper-spectral masks such as near-infrared (NIR) to highlight surface differences in the image to detect crop anomalies. The aim of the present study was to determine the relationship of NDVI and NGBVI as plant health indicators in tomato plants (Solanum lycopersicum) treated with the beneficial bacteria Bacillus cereus-Amazcala (B. c-A) as a protective agent to cope with Clavibacter michiganensis subsp. michiganensis (Cmm) infections. The results showed that in the presence of B. c-A after infection with Cmm, NDVI and NGBVI can be used as markers of plant weight and the activation of the enzymatic activities related to plant defense induction. Full article
(This article belongs to the Special Issue Sensors and Information Technologies for Plant Development Monitoring)
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16 pages, 7085 KiB  
Article
A Crop Modelling Strategy to Improve Cacao Quality and Productivity
by Angela Patricia Romero Vergel, Anyela Valentina Camargo Rodriguez, Oscar Dario Ramirez, Paula Andrea Arenas Velilla and Adriana Maria Gallego
Plants 2022, 11(2), 157; https://doi.org/10.3390/plants11020157 - 7 Jan 2022
Cited by 6 | Viewed by 6101
Abstract
Cacao production systems in Colombia are of high importance due to their direct impact in the social and economic development of smallholder farmers. Although Colombian cacao has the potential to be in the high value markets for fine flavour, the lack of expert [...] Read more.
Cacao production systems in Colombia are of high importance due to their direct impact in the social and economic development of smallholder farmers. Although Colombian cacao has the potential to be in the high value markets for fine flavour, the lack of expert support as well as the use of traditional, and often times sub-optimal technologies makes cacao production negligible. Traditionally, cacao harvest takes place at exactly the same time regardless of the geographic and climatic region where it is grown, the problem with this strategy is that cacao beans are often unripe or over matured and a combination of both will negatively affect the quality of the final cacao product. Since cacao fruit development can be considered as the result of a number of physiological and morphological processes that can be described by mathematical relationships even under uncontrolled environments. Environmental parameters that have more association with pod maturation speed should be taken into account to decide the appropriate time to harvest. In this context, crop models are useful tools to simulate and predict crop development over time and under multiple environmental conditions. Since harvesting at the right time can yield high quality cacao, we parameterised a crop model to predict the best time for harvest cacao fruits in Colombia. The cacao model uses weather variables such as temperature and solar radiation to simulate the growth rate of cocoa fruits from flowering to maturity. The model uses thermal time as an indicator of optimal maturity. This model can be used as a practical tool that supports cacao farmers in the production of high quality cacao which is usually paid at a higher price. When comparing simulated and observed data, our results showed an RRMSE of 7.2% for the yield prediction, while the simulated harvest date varied between +/−2 to 20 days depending on the temperature variations of the year between regions. This crop model contributed to understanding and predicting the phenology of cacao fruits for two key cultivars ICS95 y CCN51. Full article
(This article belongs to the Special Issue Sensors and Information Technologies for Plant Development Monitoring)
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17 pages, 4720 KiB  
Article
Analysis of New RGB Vegetation Indices for PHYVV and TMV Identification in Jalapeño Pepper (Capsicum annuum) Leaves Using CNNs-Based Model
by Arturo Yee-Rendon, Irineo Torres-Pacheco, Angelica Sarahy Trujillo-Lopez, Karen Paola Romero-Bringas and Jesus Roberto Millan-Almaraz
Plants 2021, 10(10), 1977; https://doi.org/10.3390/plants10101977 - 22 Sep 2021
Cited by 5 | Viewed by 2395
Abstract
Recently, deep-learning techniques have become the foundations for many breakthroughs in the automated identification of plant diseases. In the agricultural sector, many recent visual-computer approaches use deep-learning models. In this approach, a novel predictive analytics methodology to identify Tobacco Mosaic Virus (TMV) and [...] Read more.
Recently, deep-learning techniques have become the foundations for many breakthroughs in the automated identification of plant diseases. In the agricultural sector, many recent visual-computer approaches use deep-learning models. In this approach, a novel predictive analytics methodology to identify Tobacco Mosaic Virus (TMV) and Pepper Huasteco Yellow Vein Virus (PHYVV) visual symptoms on Jalapeño pepper (Capsicum annuum L.) leaves by using image-processing and deep-learning classification models is presented. The proposed image-processing approach is based on the utilization of Normalized Red-Blue Vegetation Index (NRBVI) and Normalized Green-Blue Vegetation Index (NGBVI) as new RGB-based vegetation indices, and its subsequent Jet pallet colored version NRBVI-Jet NGBVI-Jet as pre-processing algorithms. Furthermore, four standard pre-trained deep-learning architectures, Visual Geometry Group-16 (VGG-16), Xception, Inception v3, and MobileNet v2, were implemented for classification purposes. The objective of this methodology was to find the most accurate combination of vegetation index pre-processing algorithms and pre-trained deep- learning classification models. Transfer learning was applied to fine tune the pre-trained deep- learning models and data augmentation was also applied to prevent the models from overfitting. The performance of the models was evaluated using Top-1 accuracy, precision, recall, and F1-score using test data. The results showed that the best model was an Xception-based model that uses the NGBVI dataset. This model reached an average Top-1 test accuracy of 98.3%. A complete analysis of the different vegetation index representations using models based on deep-learning architectures is presented along with the study of the learning curves of these deep-learning models during the training phase. Full article
(This article belongs to the Special Issue Sensors and Information Technologies for Plant Development Monitoring)
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