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Machines
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Current and Future Applications of Agricultural Machines Based on Intelligent...
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Current and Future Applications of Agricultural Machines Based on Intelligent Methods

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

Deadline for manuscript submissions: closed (31 May 2024) | Viewed by 5935

Special Issue Editors

Dr. Salik Ram Khanal

E-Mail Website
Guest Editor
Center for Precision & Automated Agricultural Systems, Washington State University, Pullman, WA, USA
Interests: machine vision; field robotics; computer vision; machine learning; industry technology 4.0
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Manoj Karkee

E-Mail Website
Guest Editor
Biological Systems Engineering Department, Center for Precision & Automated Agricultural Systems, Washington State University, Pullman, WA, USA
Interests: machine vision; field robotics; human–machine interaction; agriculture system modeling
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The growth of the world population, urbanization, climate change, and food consumption habits of individuals lead to an increase in the food demand and supply ratio. One of the most important factors affecting food production is the shortage of agricultural labor all around the world. In recent years, Artificial Intelligence technology has made it possible to make operations easier, faster, and more efficient. Replacing field workers with modern intelligent machines in the agriculture field solves the labor shortage problem and increases work efficiency and overall profit.

Using machines in agriculture fields has been practiced for a long time and can be made more effective by integrating Artificial Intelligence in it. Intelligent machines can also decide which action needs to be taken by sensing the background information.

The main objective of this Special Issue is to encourage researchers to investigate the challenges and opportunities of intelligent machines in agriculture. This Special Issue aims to provide an opportunity to present and promote recent activities in AI-based agriculture machines.    

Dr. Salik Ram Khanal
Prof. Dr. Manoj Karkee
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

  • agricultural robotics 
  • modeling, simulation, and controls 
  • field robotics 
  • agriculture automation 
  • machine vision 
  • vision-based sensing 
  • autonomous operations 
  • robotic harvesting
  • vision-based autonomous machines

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Published Papers (2 papers)

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21 pages, 9820 KiB  
Article
In-Depth Evaluation of Automated Fruit Harvesting in Unstructured Environment for Improved Robot Design
by Sadaf Zeeshan, Tauseef Aized and Fahid Riaz
Machines 2024, 12(3), 151; https://doi.org/10.3390/machines12030151 - 22 Feb 2024
Cited by 2 | Viewed by 2673
Abstract
Using modern machines like robots comes with its set of challenges when encountered with unstructured scenarios like occlusion, shadows, poor illumination, and other environmental factors. Hence, it is essential to consider these factors while designing harvesting robots. Fruit harvesting robots are modern automatic [...] Read more.
Using modern machines like robots comes with its set of challenges when encountered with unstructured scenarios like occlusion, shadows, poor illumination, and other environmental factors. Hence, it is essential to consider these factors while designing harvesting robots. Fruit harvesting robots are modern automatic machines that have the ability to improve productivity and replace labor for repetitive and laborious harvesting tasks. Therefore, the aim of this paper is to design an improved orange-harvesting robot for a real-time unstructured environment of orchards, mainly focusing on improved efficiency in occlusion and varying illumination. The article distinguishes itself with not only an efficient structural design but also the use of an enhanced convolutional neural network, methodologically designed and fine-tuned on a dataset tailored for oranges integrated with position visual servoing control system. Enhanced motion planning uses an improved rapidly exploring random tree star algorithm that ensures the optimized path for every robot activity. Moreover, the proposed machine design is rigorously tested to validate the performance of the fruit harvesting robot. The unique aspect of this paper is the in-depth evaluation of robots to test five areas of performance that include not only the accurate detection of the fruit, time of fruit picking, and success rate of fruit picking, but also the damage rate of fruit picked as well as the consistency rate of the robot picking in varying illumination and occlusion. The results are then analyzed and compared with the performance of a previous design of fruit harvesting robot. The study ensures improved results in most aspects of the design for performance in an unstructured environment. Full article
(This article belongs to the Special Issue Current and Future Applications of Agricultural Machines Based on Intelligent Methods)
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Review

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24 pages, 5794 KiB  
Review
Applications of Autonomous Navigation Technologies for Unmanned Agricultural Tractors: A Review
by Jiwei Qu, Zhe Zhang, Zheyu Qin, Kangquan Guo and Dan Li
Machines 2024, 12(4), 218; https://doi.org/10.3390/machines12040218 - 25 Mar 2024
Cited by 3 | Viewed by 2482
Abstract
The development of unmanned agricultural tractors (UAT) represents a significant step towards intelligent agricultural equipment. UAT technology is expected to lighten the workload of laborers and enhance the accuracy and efficiency of mechanized operations. Through the investigation of 123 relevant studies in the [...] Read more.
The development of unmanned agricultural tractors (UAT) represents a significant step towards intelligent agricultural equipment. UAT technology is expected to lighten the workload of laborers and enhance the accuracy and efficiency of mechanized operations. Through the investigation of 123 relevant studies in the literature published in recent years, this article reviews three aspects of autonomous navigation technologies for UATs: perception, path planning and tracking, and motion control. The advantages and deficiencies of these technologies in the context of UATs are clarified by analyzing technical principles and the status of current research. We conduct summaries and analyses of existing unmanned navigation solutions for different application scenarios in order to identify current bottleneck issues. Based on the analysis of the applicability of autonomous navigation technologies in UATs, it can be seen that fruitful research progress has been achieved. The review also summarizes the common problems seen in current UAT technologies. The application of research to the sharing and integrating of multi-source data for autonomous navigation has so far been relatively weak. There is an urgent need for high-precision and high-stability sensing equipment. The universality of path planning methods and the efficiency and precision of path tracking need to be improved, and it is also necessary to develop highly reliable electrical control modules to enhance motion control performance. Overall, advanced sensors, high-performance intelligent algorithms, and reliable electrical control hardware are key factors in promoting the development of UAT technology. Full article
(This article belongs to the Special Issue Current and Future Applications of Agricultural Machines Based on Intelligent Methods)
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