Artificial Intelligence for Agricultural Robotics

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

Deadline for manuscript submissions: closed (30 July 2021) | Viewed by 11794

Special Issue Editors


E-Mail Website
Guest Editor
Department of Plant Sciences, Wageningen University & Research
Interests: machine vision and robotics in agricultural applications

E-Mail Website
Guest Editor
Australian Centre for Field Robotics (ACFR), The University of Sydney, Sydney, NSW 2006, Australia
Interests: machine learning; data science; field robotics

Special Issue Information

Dear Colleagues,

Global demand for agricultural products is growing rapidly. To meet this demand for food, feed, fuel and fibres, it is expected production will need to grow 50% by 2050. At the same time, labour shortages in agriculture are increasing due to an aging rural workforce, urbanization, and tough working conditions. To meet increasing demands with diminishing labour, there is a strong pressure to increase agricultural productivity. Artificial intelligence (AI) and robotics holds the potential to play a large role in this transformation. Despite increasing mechanisation and sophistication, many agricultural tasks are still dominated by human labour.

Recent advances in AI and robotics create new opportunities for addressing three significant challenges in automating agriculture. The first lies in dealing with various types of variation; natural variation in the plants and produce, variation in environmental conditions, variation in cultivation systems, and variation in the tasks that need to be performed. Secondly, robots have to deal with incomplete information caused by a complex, unpredictable, and evolving environment. Finally, agricultural robots need to be safe when interacting with humans, livestock, and delicate crops.

The goal of this Special Issue is to disseminate the state-of-the-art findings in AI and robotics for agriculture. We welcome contributions related to all aspects of AI and robotics for agriculture spanning perception, learning, planning, control, and manipulation. Similarly, contributions addressing any agricultural task are encouraged, such as soil preparation, autonomous weeding, monitoring and harvesting. Contributions must be evaluated in (or on data from) a real agricultural environment.

Dr. Gert Kootstra
Dr. Asher Bender
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

  • Agricultural robotics
  • Artificial intelligence
  • Machine learning
  • Perception
  • Planning
  • Control
  • Robotic learning
  • Manipulation
  • Harvesting

Published Papers (2 papers)

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

Research

16 pages, 16988 KiB  
Article
Automatic Phenotyping of Tomatoes in Production Greenhouses Using Robotics and Computer Vision: From Theory to Practice
by Hubert Fonteijn, Manya Afonso, Dick Lensink, Marcel Mooij, Nanne Faber, Arjan Vroegop, Gerrit Polder and Ron Wehrens
Agronomy 2021, 11(8), 1599; https://doi.org/10.3390/agronomy11081599 - 11 Aug 2021
Cited by 20 | Viewed by 3749
Abstract
High-throughput phenotyping is playing an increasingly important role in many areas of agriculture. Breeders will use it to obtain values for the traits of interest so that they can estimate genetic value and select promising varieties; growers may be interested in having predictions [...] Read more.
High-throughput phenotyping is playing an increasingly important role in many areas of agriculture. Breeders will use it to obtain values for the traits of interest so that they can estimate genetic value and select promising varieties; growers may be interested in having predictions of yield well in advance of the actual harvest. In most phenotyping applications, image analysis plays an important role, drastically reducing the dependence on manual labor while being non-destructive. An automatic phenotyping system combines a reliable acquisition system, a high-performance segmentation algorithm for detecting fruits in individual images, and a registration algorithm that brings the images (and the corresponding detected plants or plant components) into a coherent spatial reference frame. Recently, significant advances have been made in the fields of robotics, image registration, and especially image segmentation, which each individually have improved the prospect of developing a fully integrated automatic phenotyping system. However, so far no complete phenotyping systems have been reported for routine use in a production environment. This work catalogs the outstanding issues that remain to be resolved by describing a prototype phenotyping system for a production tomato greenhouse, for many reasons a challenging environment. Full article
(This article belongs to the Special Issue Artificial Intelligence for Agricultural Robotics)
Show Figures

Figure 1

19 pages, 6629 KiB  
Article
A Field-Tested Harvesting Robot for Oyster Mushroom in Greenhouse
by Jiacheng Rong, Pengbo Wang, Qian Yang and Feng Huang
Agronomy 2021, 11(6), 1210; https://doi.org/10.3390/agronomy11061210 - 15 Jun 2021
Cited by 41 | Viewed by 6436
Abstract
The fully autonomous harvesting of oyster mushrooms in the greenhouse requires the development of a reliable and robust harvesting robot. In this paper, we propose an oyster-mushroom-harvesting robot, which can realize harvesting operations in the entire greenhouse. The two crucial components of the [...] Read more.
The fully autonomous harvesting of oyster mushrooms in the greenhouse requires the development of a reliable and robust harvesting robot. In this paper, we propose an oyster-mushroom-harvesting robot, which can realize harvesting operations in the entire greenhouse. The two crucial components of the harvesting robot are the perception module and the end-effector. Intel RealSense D435i is adopted to collect RGB images and point cloud images in real time; an improved SSD algorithm is proposed to detect mushrooms, and finally, the existing soft gripper is manipulated to grasp oyster mushrooms. Field experiments exhibit the feasibility and robustness of the proposed robot system, in which the success rate of the mushroom recognition success rate reaches 95%, the harvesting success rate reaches 86.8% (without considering mushroom damage), and the harvesting time for a single mushroom is 8.85 s. Full article
(This article belongs to the Special Issue Artificial Intelligence for Agricultural Robotics)
Show Figures

Figure 1

Back to TopTop