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Journals
Smart Cities
Special Issues
Sustainable Agricultures and Food Production in Smart Cities
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Sustainable Agricultures and Food Production in Smart Cities

A special issue of Smart Cities (ISSN 2624-6511). This special issue belongs to the section "Smart Urban Agriculture".

Deadline for manuscript submissions: closed (31 December 2020) | Viewed by 10170

Special Issue Editors

Prof. Dr. Christopher Bryant

E-Mail Website
Guest Editor
Université de Montréal, Québec, Canada & School of Environmental Design and Rural Development; University of Guelph, Ontario, Canada
Interests: agricultural adaptation to climate change; agriculture around and inside urban agglomerations; sustainable agricultures; land use planning; strategic development planning for agricultural and food development; food security
Dr. Antonia Bousbaine

E-Mail Website
Guest Editor
Université de Liège, Liège, Belgium
Interests: healthy food production; sustainable agricultures; roles of different actors in food production; food security

Special Issue Information

Dear Colleagues,

Agriculture and food production in and around cities have become increasingly known as urban agriculture. This urban agriculture is amazingly varied, and includes roof-top gardens, community gardens, as well as intensive horticultural production, small lots for agricultural production and larger scale farm operations. These forms of agriculture and their food production processes can all potentially be sustainable. This sustainability also increasingly includes the integration of consumers and their values concerning healthy foodstuffs and the development of sustainable agriculture. This has led to a variety of ways to integrate farmers and consumers, both informally as well as through formal networks and social organizations. In addition, different forms of this urban agriculture also support functions other than food production, such as providing opportunities for migrants to produce their own foodstuffs and integrate more effectively into the urban population, supporting in some instances the management of ‘green’ spaces in the city and its surrounding areas, and providing opportunities for school students to learn about healthy agriculture, among other functions. This Special Issue of Smart Cities will focus on all of these issues and demonstrate what different types of production and formal and informal approaches to linking sustainable food production with consumers can achieve, and how they can contribute substantially to improving food security in smart cities.

Prof. Dr. Christopher Bryant
Dr. Antonia Bousbaine
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. Smart Cities 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 2000 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

  • sustainable agriculture
  • healthy food produce
  • short circuits linking consumers and farmers
  • human values
  • food projects

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Published Papers (1 paper)

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Review

26 pages, 2829 KiB  
Review
Advanced Machine Learning in Point Spectroscopy, RGB- and Hyperspectral-Imaging for Automatic Discriminations of Crops and Weeds: A Review
by Wen-Hao Su
Smart Cities 2020, 3(3), 767-792; https://doi.org/10.3390/smartcities3030039 - 1 Aug 2020
Cited by 73 | Viewed by 9236
Abstract
Crop productivity is readily reduced by competition from weeds. It is particularly important to control weeds early to prevent yield losses. Limited herbicide choices and increasing costs of weed management are threatening the profitability of crops. Smart agriculture can use intelligent technology to [...] Read more.
Crop productivity is readily reduced by competition from weeds. It is particularly important to control weeds early to prevent yield losses. Limited herbicide choices and increasing costs of weed management are threatening the profitability of crops. Smart agriculture can use intelligent technology to accurately measure the distribution of weeds in the field and perform weed control tasks in selected areas, which cannot only improve the effectiveness of pesticides, but also increase the economic benefits of agricultural products. The most important thing for an automatic system to remove weeds within crop rows is to utilize reliable sensing technology to achieve accurate differentiation of weeds and crops at specific locations in the field. In recent years, there have been many significant achievements involving the differentiation of crops and weeds. These studies are related to the development of rapid and non-destructive sensors, as well as the analysis methods for the data obtained. This paper presents a review of the use of three sensing methods including spectroscopy, color imaging, and hyperspectral imaging in the discrimination of crops and weeds. Several algorithms of machine learning have been employed for data analysis such as convolutional neural network (CNN), artificial neural network (ANN), and support vector machine (SVM). Successful applications include the weed detection in grain crops (such as maize, wheat, and soybean), vegetable crops (such as tomato, lettuce, and radish), and fiber crops (such as cotton) with unsupervised or supervised learning. This review gives a brief introduction into proposed sensing and machine learning methods, then provides an overview of instructive examples of these techniques for weed/crop discrimination. The discussion describes the recent progress made in the development of automated technology for accurate plant identification as well as the challenges and future prospects. It is believed that this review is of great significance to those who study automatic plant care in crops using intelligent technology. Full article
(This article belongs to the Special Issue Sustainable Agricultures and Food Production in Smart Cities)
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