Digital Farming in Agricultural Machinery: Revolutionizing Farming Through Smart Technologies

A special issue of Machines (ISSN 2075-1702). This special issue belongs to the section "Robotics, Mechatronics and Intelligent Machines".

Deadline for manuscript submissions: 31 October 2025 | Viewed by 1146

Special Issue Editors


E-Mail Website
Guest Editor
Department of Engineering and Aviation Sciences, University of Maryland Eastern Shore, Princess Anne, MD 21853, USA
Interests: engineering education; robotics; precision agriculture; mechatronics; renewable energy

E-Mail Website
Guest Editor
Department of Natural Science, University of Maryland Eastern Shore, Princess Anne, MD 21853, USA
Interests: sustainable robotic agriculture; bioenergy; STEM education

Special Issue Information

Dear Colleagues,

Digital farming is revolutionizing agriculture by integrating smart technologies into both traditional and innovative farming practices. Traditional horizontal farming in medium- to large-scale farms has witnessed technological advancement, often referred to as "precision agriculture", that leverages digital tools to enhance productivity and sustainability. Key technologies include GPS systems, drones, combine harvesters, and sprayers equipped with GPS and other sensors, which enable farmers to monitor and manage crops and livestock with unprecedented accuracy. GPS technology allows for precise planting, agronomic inputs, and efficient harvesting, reducing waste and optimizing yields. These innovations not only streamline operations but also promote resource efficiency, leading to reduced environmental impact. The growing world population and rapid urbanization are also driving a surge in demand for innovative farming solutions, leading to the rise of vertical and indoor farming. As cities expand and arable land becomes scarcer, these methods offer sustainable alternatives to traditional agriculture. Vertical farming, which stacks layers of crops in controlled environments, and indoor farming, which grows plants within buildings, both utilize advanced sensing technologies and intelligent robots and machinery to maximize space and efficiency. By harnessing these smart technologies and adopting advances in artificial intelligence tools, digital farming is helping farmers and other food production practitioners adapt to changing climate conditions and market demands, ultimately leading to more sustainable and profitable agricultural practices. The integration of digital tools in farming machinery is setting a new standard for the industry, driving a transformative shift towards smarter, data-informed agriculture.

We invite submissions from industrial and academic researchers, farming practitioners, and educators for a Special Issue on 'Digital Farming in Agricultural Machinery: Revolutionizing Farming through Smart Technologies', (may be changed), focusing on the integration of AI, precision agriculture, and innovative methods like vertical and indoor farming to address the challenges of growing food for a growing world population in a sustainable manner.

Prof. Dr. Abhijit Nagchaudhuri
Dr. Madhumi Mitra
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. Machines 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 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

  • precision agriculture
  • digital farming
  • GPS
  • drones
  • vertical farming
  • indoor and urban agriculture
  • smart combine harvesters and sprayers
  • agronomic sensors
  • data science and AI
  • sustainability
  • STEM education and agriculture
  • farm machinery and robotics

Benefits of Publishing in a Special Issue

  • Ease of navigation: Grouping papers by topic helps scholars navigate broad scope journals more efficiently.
  • Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
  • Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
  • External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
  • e-Book format: Special Issues with more than 10 articles can be published as dedicated e-books, ensuring wide and rapid dissemination.

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

Published Papers (1 paper)

Order results
Result details
Select all
Export citation of selected articles as:

Research

23 pages, 7100 KiB  
Article
Optimization of the Screw Conveyor Device Based on a GA-BP Neural Network
by Qiang Guo, Yunpeng Zhuang, Houzhuo Xu, Wei Li, Haitao Li and Zhidong Wu
Machines 2025, 13(1), 24; https://doi.org/10.3390/machines13010024 - 1 Jan 2025
Cited by 1 | Viewed by 699
Abstract
As technology advances, so does digital farming, revolutionizing the industry. Drones, sprayers equipped with GPS and other sensors, combine harvesters, and other machinery can greatly improve agricultural productivity. This paper studies the impact of the straw baler screw conveyor on the efficiency of [...] Read more.
As technology advances, so does digital farming, revolutionizing the industry. Drones, sprayers equipped with GPS and other sensors, combine harvesters, and other machinery can greatly improve agricultural productivity. This paper studies the impact of the straw baler screw conveyor on the efficiency of the baler. Via theoretical analysis, GA—BP (Genetic Algorithm—Back Propagation) simulation, and comparative experiments, the structural parameters and rotational speed of the spiral shaft in the screw conveying device are optimized. In this paper, we analyze the force and velocity components acting on the straw, give the design principles for the screw’s conveying parameters under the premise of ensuring maximum conveying capacity and minimum power consumption, and determine the optimal design variables, objective functions, and constraints according to the specific optimization problem; we establish a specific mathematical model, and introduce algorithm optimization for nonlinear problems with many variables and large amounts of calculations. In MATLAB, an optimization calculation and analysis were performed. The optimization results of the traditional BP (Back Propagation) and GA—BP were compared. It was proven that GA—BP could effectively compensate for the deficiencies of the BP neural network and substantially enhance the model’s accuracy. Through an analysis of the optimization results, the conclusion of attaining the optimization objective was drawn. Specifically, when the outer diameter of the spiral for screw conveyance in the straw baler was D=320 mm, the pitch was S=200 mm, and the rotational speed of the pickup shaft was n=138 r/min, the straw baler could achieve the maximum conveying capacity and the minimum power consumption. At this moment, the power consumption was P=0.079 kW, and the conveying capacity was Qm=23.98 t/h. Subsequently, the optimization results were contrasted with those of other mainstream domestic models, and a comparative experiment was conducted. The experimental results indicated that the model’s prediction results were reliable and exhibited higher efficiency compared to other combinations. The results could provide a reference for the research on screw conveyance of balers. Full article
Show Figures

Figure 1

Back to TopTop