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Agriculture
Special Issues
Integrated Weed Management in Agricultural Systems
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Integrated Weed Management in Agricultural Systems

A special issue of Agriculture (ISSN 2077-0472). This special issue belongs to the section "Crop Protection, Diseases, Pests and Weeds".

Deadline for manuscript submissions: closed (28 February 2021) | Viewed by 16347

Special Issue Editor

Prof. Fabian Menalled

E-Mail Website
Guest Editor
Department of Land Resources and Environmental Sciences, Montana State University - Bozeman, Bozeman, MT 59717, USA
Interests: agroecology; weed ecology; integrated weed management; sustainable agriculture; biodiversity; diversified cropping systems
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

I would like to invite you to submit an article for the Special Issue on “Integrated Weed Management in Agricultural System”, to be published in Agriculture (Impact Factor: 2.072, ISSN 2077-0472).  

The goal of this Special Issue is to assess first biological, evolutionary, and ecological principles of integrated weed management. This knowledge should help weed science to move away from its traditional monodisciplinary perspective aimed at weed eradication towards researching complex problems while embracing collaborations with multiple environmental and social disciplines.

In the framework of this Special Issue, integrated weed management is conceived as an approach to reduce the negative impact of weeds through the combination of effective, environmentally safe, and sociologically acceptable control tactics. As such, integrated weed management programs should consider not only changes in weed abundance and impact but also interaction among economic, environmental, and social dimensions of agroecosystems.  

Studies on integrated weed management, multitrophic interactions, competition of weeds and crops, effects of diversity on weed management, use of cover crops for weed competition, population dynamics modelling, effects of seed predation, plant–soil feedbacks, and herbicide resistance are welcomed to this Special Issue. 

We look forward to your contributions.

Prof. Fabian Menalled
Guest Editor

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. Agriculture 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 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

  • Integrated weed management
  • Agroecology
  • Weed ecology
  • Sustainable agriculture
  • Biodiversity
  • Diversified cropping systems

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Further information on MDPI's Special Issue policies can be found here.

Published Papers (1 paper)

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Research

13 pages, 2814 KiB  
Article
Early Weed Detection Using Image Processing and Machine Learning Techniques in an Australian Chilli Farm
by Nahina Islam, Md Mamunur Rashid, Santoso Wibowo, Cheng-Yuan Xu, Ahsan Morshed, Saleh A. Wasimi, Steven Moore and Sk Mostafizur Rahman
Agriculture 2021, 11(5), 387; https://doi.org/10.3390/agriculture11050387 - 25 Apr 2021
Cited by 165 | Viewed by 15561
Abstract
This paper explores the potential of machine learning algorithms for weed and crop classification from UAV images. The identification of weeds in crops is a challenging task that has been addressed through orthomosaicing of images, feature extraction and labelling of images to train [...] Read more.
This paper explores the potential of machine learning algorithms for weed and crop classification from UAV images. The identification of weeds in crops is a challenging task that has been addressed through orthomosaicing of images, feature extraction and labelling of images to train machine learning algorithms. In this paper, the performances of several machine learning algorithms, random forest (RF), support vector machine (SVM) and k-nearest neighbours (KNN), are analysed to detect weeds using UAV images collected from a chilli crop field located in Australia. The evaluation metrics used in the comparison of performance were accuracy, precision, recall, false positive rate and kappa coefficient. MATLAB is used for simulating the machine learning algorithms; and the achieved weed detection accuracies are 96% using RF, 94% using SVM and 63% using KNN. Based on this study, RF and SVM algorithms are efficient and practical to use, and can be implemented easily for detecting weed from UAV images. Full article
(This article belongs to the Special Issue Integrated Weed Management in Agricultural Systems)
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