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Agronomy
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Site-Specific Weed Management (SSWM): Integrating Weed Population Dynamics with Information-Communications...
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Site-Specific Weed Management (SSWM): Integrating Weed Population Dynamics with Information-Communications Technologies (ICTs)

A special issue of Agronomy (ISSN 2073-4395). This special issue belongs to the section "Weed Science and Weed Management".

Deadline for manuscript submissions: closed (28 February 2025) | Viewed by 3017

Special Issue Editors

Dr. Guillermo R. Chantre

E-Mail Website
Guest Editor
Departamento de Agronomía, Universidad Nacional del Sur y CERZOS (CONICET), Bahía Blanca 8000, Argentina
Interests: weed bioecology; integrated weed management; population dynamics; community dynamics; modelling; decision support systems
Special Issues, Collections and Topics in MDPI journals
Dr. Roberto Benech-Arnold

E-Mail Website
Guest Editor
IFEVA - Cátedra de Cultivos Industriales, Facultad de Agronomía, Universidad de Buenos Aires/CONICET, Av. San Martín 4453, Buenos Aires 1417, Argentina
Interests: weed population dynamics; seed ecology and physiology; crop physiology; weed management; ecological modeling
Dr. Marcos Yanniccari

E-Mail Website
Guest Editor
Facultad de Agronomía, National University of La Pampa/YPF Tecnología (YPF-CONICET), Santa Rosa 6300, Argentina
Interests: weed control; herbicide resistance; modes of action of herbicides; integrated weed management
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The developing techniques in precision agriculture suggest that high-tech strategies, such as artificial intelligence (AI), machine learning, and deep learning techniques, are of the uppermost importance in the novel management of weeds, which aims to produce high crop yields while reducing the negative environmental impact of agronomic practices. In fact, ICTs are currently being used to empower site-specific weed management (SSWM). Site-specific weed management is based on the fact that weed populations are commonly irregularly distributed within crop fields, and it implies applying chemical and/or physical control measures only where and when they are needed. Currently, the use of AI models allows for the integration of photogrammetry or image analysis to establish databases for developing algorithms that enable weed management using automated or robotic techniques to distinguish weeds from crops. Both remote sensing (satellite) and unmanned aerial vehicles (UAVs) are being used to collect data at different spatial scales. The integration of both remote and proximal datasets to train and test AI algorithms is a novel route towards more precise and optimized management decisions. In this Special Issue, we invite researchers to contribute original researches, reviews, and opinion pieces covering all topics related to site-specific weed management, weed population dynamics, and ICTs.

Dr. Guillermo R. Chantre
Dr. Roberto Benech-Arnold
Dr. Marcos Yanniccari
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. Agronomy is an international peer-reviewed open access monthly 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 2600 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

  • weed bioecology
  • population dynamics
  • precision agriculture
  • remote sensing
  • proximal sensing
  • artificial intelligence
  • decision support systems

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Published Papers (3 papers)

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Research

14 pages, 2215 KiB  
Article
Population Parameters as Key Factors for Site-Specific Distribution of Invasive Weed Rhynchosia senna in Semiarid Temperate Agroecosystems
by Matías Quintana, Guillermo R. Chantre, Omar Reinoso and Juan P. Renzi
Agronomy 2025, 15(4), 858; https://doi.org/10.3390/agronomy15040858 - 29 Mar 2025
Viewed by 256
Abstract
The genus Rhynchosia includes more than 550 species, some exhibiting invasive behavior. Rynchosia senna var. senna (RS) is a challenging weed to control in its native range; however, its invasive potential remains unknown. The aim of this study was to evaluate RS demographic [...] Read more.
The genus Rhynchosia includes more than 550 species, some exhibiting invasive behavior. Rynchosia senna var. senna (RS) is a challenging weed to control in its native range; however, its invasive potential remains unknown. The aim of this study was to evaluate RS demographic parameters to determine its invasive potential, including (i) plant fecundity during the first year of young adult and in adult plants, (ii) seed dispersal, (iii) pre- and post-dispersal predation, (iv) soil seedbank persistence, and (v) field emergence patterns. RS fecundity declined in autumn and mainly in early established cohorts. Fecundity was influenced by pre-dispersal predation (Bruchus spp. 12 ± 2%), and post-dispersal removal by birds (66 ± 4%) and arthropods (37 ± 5%). Seed dispersal decreased with distance. Seedling emergence occurred mainly during early summer (75%), and to a lesser extent during late summer (20%) and autumn (5%). Seed physical dormancy loss (~80% in the first year) defines a short persistent seedbank. Under the evaluated conditions (native environment), RS shows a limited invasive potential. However, in non-native environments, in the absence of natural predators, its prolific fecundity and the occurrence of staggered emergence patterns could easily enhance invasiveness, enabling rapid colonization, as observed in Medicago polymorpha L. Full article
(This article belongs to the Special Issue Site-Specific Weed Management (SSWM): Integrating Weed Population Dynamics with Information-Communications Technologies (ICTs))
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30 pages, 5568 KiB  
Article
Modeling the Herbicide-Resistance Evolution in Lolium rigidum (Gaud.) Populations at the Landscape Scale
by Lucia Gonzalez-Diaz, Irene Gonzalez-Garcia and Jose L. Gonzalez-Andujar
Agronomy 2024, 14(12), 2990; https://doi.org/10.3390/agronomy14122990 - 16 Dec 2024
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Abstract
The repeated application of herbicides has led to the development of herbicide resistance. Models are useful for identifying key processes and understanding the evolution of resistance. This study developed a spatially explicit model at a landscape scale to examine the dynamics of Lolium [...] Read more.
The repeated application of herbicides has led to the development of herbicide resistance. Models are useful for identifying key processes and understanding the evolution of resistance. This study developed a spatially explicit model at a landscape scale to examine the dynamics of Lolium rigidum populations in dryland cereal crops and the evolution of herbicide resistance under various management strategies. Resistance evolved rapidly under repeated herbicide use, driven by weed fecundity and herbicide efficacy. Although fitness costs associated with resistant plants reduced the resistance evolution, they did not affect the speed of its spread. The most effective strategies for slow resistance involved diversifying cropping sequences and herbicide applications. Pollen flow was the main dispersal vector, with seed dispersal also making a significant contribution. Strategies limiting seed dispersal effectively decreased resistance spread. However, the use of a seed-catching device at harvest could unintentionally enrich resistance in the area. It would be beneficial to optimize the movement of harvesters between fields. The model presented here is a useful tool that could assist in the exploration of novel management strategies within the context of site-specific weed management at landscape scale as well as in the advancement of our understanding of resistance dynamics. Full article
(This article belongs to the Special Issue Site-Specific Weed Management (SSWM): Integrating Weed Population Dynamics with Information-Communications Technologies (ICTs))
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18 pages, 4434 KiB  
Article
Monitoring of Heracleum sosnowskyi Manden Using UAV Multisensors: Case Study in Moscow Region, Russia
by Rashid K. Kurbanov, Arkady N. Dalevich, Alexey S. Dorokhov, Natalia I. Zakharova, Nazih Y. Rebouh, Dmitry E. Kucher, Maxim A. Litvinov and Abdelraouf M. Ali
Agronomy 2024, 14(10), 2451; https://doi.org/10.3390/agronomy14102451 - 21 Oct 2024
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Abstract
Detection and mapping of Sosnowsky’s hogweed (HS) using remote sensing data have proven effective, yet challenges remain in identifying, localizing, and eliminating HS in urban districts and regions. Reliable data on HS growth areas are essential for monitoring, eradication, and control measures. Satellite [...] Read more.
Detection and mapping of Sosnowsky’s hogweed (HS) using remote sensing data have proven effective, yet challenges remain in identifying, localizing, and eliminating HS in urban districts and regions. Reliable data on HS growth areas are essential for monitoring, eradication, and control measures. Satellite data alone are insufficient for mapping the dynamics of HS distribution. Unmanned aerial vehicles (UAVs) with high-resolution spatial data offer a promising solution for HS detection and mapping. This study aimed to develop a method for detecting and mapping HS growth areas using a proposed algorithm for thematic processing of multispectral aerial imagery data. Multispectral data were collected using a DJI Matrice 200 v2 UAV (Dajiang Innovation Technology Co., Shenzhen, China) and a MicaSense Altum multispectral camera (MicaSense Inc., Seattle, WA, USA). Between 2020 and 2022, 146 sites in the Moscow region of the Russian Federation, covering 304,631 hectares, were monitored. Digital maps of all sites were created, including 19 digital maps (orthophoto, 5 spectral maps, and 13 vegetation indices) for four experimental sites. The collected samples included 1080 points categorized into HS, grass cover, and trees. Student’s t-test showed significant differences in vegetation indices between HS, grass, and trees. A method was developed to determine and map HS-growing areas using the selected vegetation indices NDVI > 0.3, MCARI > 0.76, user index BS1 > 0.10, and spectral channel green > 0.14. This algorithm detected HS in an area of 146.664 hectares. This method can be used to monitor and map the dynamics of HS distribution in the central region of the Russian Federation and to plan the required volume of pesticides for its eradication. Full article
(This article belongs to the Special Issue Site-Specific Weed Management (SSWM): Integrating Weed Population Dynamics with Information-Communications Technologies (ICTs))
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