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Applied Sciences
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New Trends in Weed Control and Smart Agriculture
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New Trends in Weed Control and Smart Agriculture

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Agricultural Science and Technology".

Deadline for manuscript submissions: closed (20 October 2022) | Viewed by 4990

Special Issue Editors

Prof. Dr. Michele Raffaelli

E-Mail Website1 Website2
Guest Editor
Department of Agriculture, Food and Environment, University of Pisa, 56124 Pisa, Italy
Interests: machines for soil tillage; conservation and no tillage; machines for physical weed control; soil disinfection with physical methods; precision agriculture
Special Issues, Collections and Topics in MDPI journals
Dr. Marco Fontanelli

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Guest Editor
Department of Agriculture, Food and Environment, University of Pisa, Via del Borghetto, 80, 56124 Pisa, Italy
Interests: farm mechanization and farm machinery; precision agriculture; conservation agriculture; nonchemical weed control; machine for turfgrass and landscape management
Special Issues, Collections and Topics in MDPI journals
Dr. Daniele Antichi

E-Mail Website
Guest Editor
Department of Agriculture, Food and Environment, University of Pisa, Via del Borghetto 80, 56124 Pisa, Italy
Interests: sustainable agricultural systems; agroecology; cover crops; intercropping; agroforestry; conservation agriculture; organic farming
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Farming systems are increasingly moving towards sustainable management practices with the aim to reduce chemical inputs. The European Union has been promoting integrated and organic farming systems for years to enhance the wellness and the safety of people and the environment. Weed control is probably the major issue in organic agriculture as chemical herbicides are completely forbidden. Farmers have to find effective alternatives to avoid unacceptable yield losses. On the other hand, the reduction in the use of herbicides is a very important task for integrated farming systems.

Smart agriculture technologies can help the farmers to achieve the goal of more sustainable crop management thanks to the development of “intelligent” machines that are able to optimize the efficiency of each treatment. For example, intra-row weed control can be performed thanks to a vision-based system that recognizes the crop, allowing a specific tool to go in and out from the row. For integrated farming systems, the use of smart sprayers can allow the adjustment of the dose of the herbicide treatment according to a prescription map or according to the response of a proximal sensor. Furthermore, the integration between preventive, cultural, plant-based weed control methods (e.g., ground covering, cover cropping, intercropping) and precision agricultural techniques (e.g., adaptation of permanent groundcover management according to soil/weed prescription maps) is another outstanding issue in this research field.

In this Special Issue, all contributions regarding innovative technologies and machines for sustainable management of weeds in organic and integrated agriculture are welcome, including applications of mechanical weed control, thermal weed control, highly efficient smart sprayers and autonomous machines. Manuscripts describing innovative software and sensors to be applied to the machines or to the agricultural management techniques for weed control are also welcome. Thus, we invite experts and researchers to contribute with original research, reviews and opinion pieces covering the topics of this Special Issue.

Prof. Dr. Michele Raffaelli
Prof. Dr. Marco Fontanelli
Prof. Dr. Daniele Antichi
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. Applied Sciences 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 2400 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

  • innovative strategies and machines for weed control
  • precision agriculture
  • organic agriculture
  • integrated agriculture
  • low rate sprayers
  • autonomous machines for weed control
  • autonomous machines for groundcover and cover crop management
  • herbicides reduction
  • sensors and software for weed-crop discrimination
  • sensors and software for weed-row detection

Published Papers (2 papers)

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Research

14 pages, 2100 KiB  
Article
A Simple Method to Estimate Weed Control Threshold by Using RGB Images from Drones
by Leonardo Ercolini, Nicola Grossi and Nicola Silvestri
Appl. Sci. 2022, 12(23), 11935; https://doi.org/10.3390/app122311935 - 23 Nov 2022
Cited by 1 | Viewed by 1341
Abstract
The estimation of the infestation level in a field and the consequent determination of the economic threshold is a basic requisite to rationalize post-emergence weeding. In this study, a simple and inexpensive procedure to determine the economic threshold based on weed cover is [...] Read more.
The estimation of the infestation level in a field and the consequent determination of the economic threshold is a basic requisite to rationalize post-emergence weeding. In this study, a simple and inexpensive procedure to determine the economic threshold based on weed cover is proposed. By using high-resolution RGB images captured by a low-cost drone, a free downloadable app for image processing and common spreadsheet software to perform the model parametrization, two different methods have been tested. The first method was based on the joint estimation of the two parameters involved in weed cover calculation, whereas the second method required the availability of further images for the separate estimation of the first parameter. The reliability of the two methods has been evaluated through the comparison with observed data and the goodness of fit in parameter calibration has been verified by calculating appropriate quality indices. The results showed an acceptable estimation of the weed cover value for the second method with respect to observed data (0.24 vs. 0.17 m2 and 0.17 vs. 0.14 m2, by processing images captured at 10 and 20 m, respectively), whereas the estimations obtained with the first method were disappointing (0.35 vs. 0.17 m2 and 0.33 vs. 0.14 m2, by processing images captured at 10 and 20 m, respectively). Full article
(This article belongs to the Special Issue New Trends in Weed Control and Smart Agriculture)
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17 pages, 2430 KiB  
Article
Evaluation of Autonomous Mowers Weed Control Effect in Globe Artichoke Field
by Lorenzo Gagliardi, Mino Sportelli, Christian Frasconi, Michel Pirchio, Andrea Peruzzi, Michele Raffaelli and Marco Fontanelli
Appl. Sci. 2021, 11(24), 11658; https://doi.org/10.3390/app112411658 - 08 Dec 2021
Cited by 5 | Viewed by 2395
Abstract
The development of a fully automated robotic weeder is currently hindered by the lack of a reliable technique for weed-crop detection. Autonomous mowers moving with random trajectories rely on simplified computational resources and have shown potential when applied for agricultural purposes. This study [...] Read more.
The development of a fully automated robotic weeder is currently hindered by the lack of a reliable technique for weed-crop detection. Autonomous mowers moving with random trajectories rely on simplified computational resources and have shown potential when applied for agricultural purposes. This study aimed to evaluate the applicability of these autonomous mowers for weed control in globe artichoke. A first trial consisting of the comparison of the performances of three different autonomous mowers (AM1, AM2 and AM3) was carried out evaluating percentage of area mowed and primary energy consumption. The most suitable autonomous mower was tested for its weed control effect and compared with a conventional weed management system. Average weeds height, weed cover percentage, above-ground weed biomass, artichoke yield, primary energy consumption and cost were assessed. All the autonomous mowers achieved a percentage of area mowed around the 80% after 180 min. AM2 was chosen as the best compromise for weed control in the artichoke field (83.83% of area mowed after 180 min of mowing, and a consumption of 430.50 kWh⋅ha−1⋅year−1). The autonomous mower weed management achieved a higher weed control effect (weed biomass of 71.76 vs. 143.67 g d.m.⋅m−2), a lower energy consumption (430.5 vs. 1135.13 kWh⋅ha−1⋅year−1), and a lower cost (EUR 2601.84 vs. EUR 3661.80 ha−1·year−1) compared to the conventional system. Full article
(This article belongs to the Special Issue New Trends in Weed Control and Smart Agriculture)
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