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Agronomy
Special Issues
Farm Machinery Automation for Tillage, Planting, Sowing, Cultivation, and Harvesting
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Farm Machinery Automation for Tillage, Planting, Sowing, Cultivation, and Harvesting

A special issue of Agronomy (ISSN 2073-4395). This special issue belongs to the section "Precision and Digital Agriculture".

Deadline for manuscript submissions: closed (31 March 2022) | Viewed by 5033

Special Issue Editors

Dr. Christian Frasconi

E-Mail Website
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
Special Issues, Collections and Topics in MDPI journals
Dr. Marco Fontanelli

E-Mail Website
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

Special Issue Information

Dear Colleagues,

Due to recent and continuous advances of the information and communication technologies, every economic sector is meant to become “smart”, and companies, consequently, will have to adapt to maintain an adequate level of competitiveness. This revolution also concerns agriculture; in fact, some authors introduced the concept of “Agriculture 4.0”, pointing out an epochal change that most farms are going to adopt. Traditional agricultural practices involve common tasks such as soil preparation, planting or sowing, nutrient management, weeding, and harvesting. With the advent of digital technologies, most of these processes could be successfully automated.

Precision agriculture is not a new concept, since in the last decade it has made it possible to increase agricultural process efficiency by performing targeted agronomic interventions by means of GNSS (global navigation satellite systems) technologies and AI (artificial intelligence) software and specific sensors positioned on the implements. According to the trend of the information and communication technologies revolution, this model of modern agriculture could be enriched with a series of new technological applications. In fact, these innovative solutions, mainly based on autonomous robots (farm-bots), sensors, image analysis, and AI software, could allow the automation of most agricultural routines with the aim of optimizing processes and reducing costs.

This Special Issue is focused on collecting recent and innovative research papers, communications, short notes, and reviews concerning the automation of tractors or implements for the management (soil tillage, crop establishment, crop care and harvest) of different agroecosystems (orchard, narrow and wide herbaceous row crop, vegetables, grassland, and pasture). Automation in agriculture will play a key role in the future—in fact, it could not only contribute to reducing costs and improving the quality of agricultural products, but it could also reduce the environmental impact of agricultural processes.

We look forward to receiving your contributions. 

Dr. Christian Frasconi
Prof. Dr. Marco Fontanelli
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

  • agriculture 4.0
  • autonomous tractor
  • smart soil tillage implements
  • farm-bot
  • intelligent sprayer
  • smart fertilizer application
  • autonomous semi-autonomous harvester

Published Papers (2 papers)

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Research

10 pages, 1501 KiB  
Article
Using the Kaplan–Meier Estimator to Assess the Reliability of Agricultural Machinery
by Karol Durczak, Jarosław Selech, Adam Ekielski, Tomasz Żelaziński, Marcin Waleński and Kamil Witaszek
Agronomy 2022, 12(6), 1364; https://doi.org/10.3390/agronomy12061364 - 05 Jun 2022
Cited by 10 | Viewed by 1736
Abstract
Kaplan–Meier analyses can be used in many disciplines, e.g., agricultural engineering. Agricultural machinery and vehicles can be regarded as objects that ‘die’ because, like living creatures, they failed, although after repair they can be used until scrapped. This article presents an example of [...] Read more.
Kaplan–Meier analyses can be used in many disciplines, e.g., agricultural engineering. Agricultural machinery and vehicles can be regarded as objects that ‘die’ because, like living creatures, they failed, although after repair they can be used until scrapped. This article presents an example of using the Kaplan–Meier estimator to plot the reliability function curves of five different models of Zetor farm tractors. The research shows that the median operating time for one of the tested models, which is about 200 engine-operating hours, is 20% lower than for the entire population of analyzed Zetor tractors. This means that the quality of the model, which is very popular in Poland, differs significantly from the other models of this manufacturer. The method cannot be validated, due to a lack of similar functions for other brands of tractors. Progressive automation and digitization of agriculture can contribute to improving the reliability of agriculture work. The user can focus on the correct performance of agrotechnical treatments, and modern control systems will signal in real time, about identified or approaching costly failures. Full article
(This article belongs to the Special Issue Farm Machinery Automation for Tillage, Planting, Sowing, Cultivation, and Harvesting)
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21 pages, 3827 KiB  
Article
Innovative Living Mulch Management Strategies for Organic Conservation Field Vegetables: Evaluation of Continuous Mowing, Flaming, and Tillage Performances
by Mino Sportelli, Christian Frasconi, Marco Fontanelli, Michel Pirchio, Lorenzo Gagliardi, Michele Raffaelli, Andrea Peruzzi and Daniele Antichi
Agronomy 2022, 12(3), 622; https://doi.org/10.3390/agronomy12030622 - 02 Mar 2022
Cited by 11 | Viewed by 2574
Abstract
Organic vegetable production is particularly affected by weed pressure and mechanical weeding is the major tactic implemented by growers to keep weeds under economic thresholds. Living mulch (LM) has been shown to provide several environmental services; however, LM management is required to avoid [...] Read more.
Organic vegetable production is particularly affected by weed pressure and mechanical weeding is the major tactic implemented by growers to keep weeds under economic thresholds. Living mulch (LM) has been shown to provide several environmental services; however, LM management is required to avoid competition between service crops and cash crops. The aim of this trial was to evaluate two innovative LM-based management systems: a system that provided LM growth regulation by means of flaming (LM-FL) and a system where the LM was regularly mowed by an autonomous mower (LM-AM), both compared with a control without LM and based on standard tillage operations (TILL). The three management systems were evaluated in terms of crop production, weed control, and energy consumption on a 2 yr organic crop rotation of cauliflower (Brassica oleracea L. var botrytis) and eggplant (Solanum melongena L.). LM-AM produced an acceptable fresh marketable yield for both vegetable crops. Moreover, the weed dry biomass obtained in LM-AM-managed plots was lower compared to the LM-FL plots and ranged approximately from 200 to 300 kg ha−1. Furthermore, LM-AM management resulted in lower energy consumption (−2330 kWh ha−1 with respect to the TILL system and −7225 kWh ha−1 with respect to the LM-FL system). The results of this trial suggest that autonomous mowers have a great potential to improve LM management and help with implementing sustainable organic vegetable systems. Full article
(This article belongs to the Special Issue Farm Machinery Automation for Tillage, Planting, Sowing, Cultivation, and Harvesting)
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