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Towards Artificially Intelligent Robotics for Agriculture – Current Developments and...
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Towards Artificially Intelligent Robotics for Agriculture – Current Developments and New Trends

A special issue of Agronomy (ISSN 2073-4395). This special issue belongs to the section "Farming Sustainability".

Deadline for manuscript submissions: closed (1 March 2021) | Viewed by 51277

Special Issue Editor

Prof. Dr. Pablo Gonzalez-de-Santos

E-Mail Website
Guest Editor
Centre for Automation and Robotics, Spanish National Research Council - (CSIC), 28500 Arganda del Rey, Madrid, Spain
Interests: agriculture; actuators; precision agriculture; automation & robotics; mechatronics
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear colleagues,

Feeding the growing world population with limited natural resources is an exceptional challenge to humanity, the solution of which will certainly require a lot of initiative. More specifically, experts broadly believe that to overcome this issue, we have to adopt smarter farming methods and build more sustainable food production systems while preserving the environment and health.

The mechanization of agriculture tasks has helped in improving some agricultural processes, while automation and robotization have assisted in optimizing crop management practices and reducing chemicals. Currently, Artificial Intelligence (AI) is helping farmers to understand data to improve process efficiency and diminish negative environmental impacts. The undeniable next stage, where many researchers are focusing their efforts, will be the integration of AI and robotics to build artificially intelligent robots capable of seeding following the rules of precision agriculture, minimizing pesticide usage, optimizing weed management by minimizing or eliminating herbicide usage, reducing soil compaction by optimizing the mechanical traffic, optimizing crops by monitoring crops and soil health, harvesting at the right time by analyzing ripe status, improving the quality of product using soft grippers, etc.

Furthermore, we can take advantage of forecasted weather data available in the cloud and leveraging the latest advances in deep learning algorithms, big data methods, and cloud computing techniques.

To compile the current status and new trends on artificially intelligent robotics, this Special Issue of Agronomy welcomes articles related, but not limited, to:

  • Autonomous ground vehicles for agriculture;
  • Autonomous aerial vehicles for agriculture;
  • Automatic and intelligent implements;
  • Manipulation: harvesting, soft manipulation, dual-arm manipulators;
  • Artificial perception for agriculture;
  • ICT for agriculture: IoT, cloud computing, and big data;
  • Artificial intelligence for agriculture.

Any specific application of these topics in agriculture will also be a valuable contribution to this Special Issue.

Prof. Dr. Pablo Gonzalez-de-Santos
Guest Editor

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.

Published Papers (7 papers)

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Research

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15 pages, 15968 KiB  
Article
Robotic Fertilisation Using Localisation Systems Based on Point Clouds in Strip-Cropping Fields
by Christyan Cruz Ulloa, Anne Krus, Antonio Barrientos, Jaime Del Cerro and Constantino Valero
Agronomy 2021, 11(1), 11; https://0-doi-org.brum.beds.ac.uk/10.3390/agronomy11010011 - 23 Dec 2020
Cited by 11 | Viewed by 3393
Abstract
The use of robotic systems in organic farming has taken on a leading role in recent years; the Sureveg CORE Organic Cofund ERA-Net project seeks to evaluate the benefits of strip-cropping to produce organic vegetables. This includes, among other objectives, the development of [...] Read more.
The use of robotic systems in organic farming has taken on a leading role in recent years; the Sureveg CORE Organic Cofund ERA-Net project seeks to evaluate the benefits of strip-cropping to produce organic vegetables. This includes, among other objectives, the development of a robotic tool that facilitates the automation of the fertilisation process, allowing the individual treatment (at the plant level). In organic production, the slower nutrient release of the used fertilisers poses additional difficulties, as a tardy detection of deficiencies can no longer be corrected. To improve the detection, as well as counter the additional labour stemming from the strip-cropping configuration, an integrated robotic tool is proposed to detect individual crop deficiencies and react on a single-crop basis. For the development of this proof-of-concept, one of the main objectives of this work is implementing a robust localisation method within the vegetative environment based on point clouds, through the generation of general point cloud maps (G-PC) and local point cloud maps (L-PC) of a crop row. The plants’ geometric characteristics were extracted from the G-PC as a framework in which the robot’s positioning is defined. Through the processing of real-time lidar data, the L-PC is then defined and compared to the predefined reference system previously deduced. Both subsystems are integrated with ROS (Robot Operating System), alongside motion planning, and an inverse kinematics CCD (Cyclic Coordinate Descent) solver, among others. Tests were performed using a simulated environment of the crop row developed in Gazebo, followed by actual measurements in a strip-cropping field. During real-time data-acquisition, the localisation error is reduced from 13 mm to 11 mm within the first 120 cm of measurement. The encountered real-time geometric characteristics were found to coincide with those in the G-PC to an extend of 98.6%. Full article
(This article belongs to the Special Issue Towards Artificially Intelligent Robotics for Agriculture – Current Developments and New Trends)
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18 pages, 6212 KiB  
Article
A Concept of a Compact and Inexpensive Device for Controlling Weeds with Laser Beams
by Ildar Rakhmatulin and Christian Andreasen
Agronomy 2020, 10(10), 1616; https://0-doi-org.brum.beds.ac.uk/10.3390/agronomy10101616 - 21 Oct 2020
Cited by 21 | Viewed by 9665
Abstract
A prototype of a relatively cheap laser-based weeding device was developed and tested on couch grass (Elytrigia repens (L.) Desv. ex Nevski) mixed with tomatoes. Three types of laser were used (0.3 W, 1 W, and 5 W). A neural network was [...] Read more.
A prototype of a relatively cheap laser-based weeding device was developed and tested on couch grass (Elytrigia repens (L.) Desv. ex Nevski) mixed with tomatoes. Three types of laser were used (0.3 W, 1 W, and 5 W). A neural network was trained to identify the weed plants, and a laser guidance system estimated the coordinates of the weed. An algorithm was developed to estimate the energy necessary to harm the weed plants. We also developed a decision model for the weed control device. The energy required to damage a plant depended on the diameter of the plant which was related to plant length. The 1 W laser was not sufficient to eliminate all weed plants and required too long exposure time. The 5 W laser was more efficient but also harmed the crop if the laser beam became split into two during the weeding process. There were several challenges with the device, which needs to be improved upon. In particular, the time of exposure needs to be reduced significantly. Still, the research showed that it is possible to develop a concept for laser weeding using relatively cheap equipment, which can work in complicated situations where weeds and crop are mixed. Full article
(This article belongs to the Special Issue Towards Artificially Intelligent Robotics for Agriculture – Current Developments and New Trends)
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24 pages, 21470 KiB  
Article
Evaluation of a Stereo Vision System for Cotton Row Detection and Boll Location Estimation in Direct Sunlight
by Kadeghe Fue, Wesley Porter, Edward Barnes, Changying Li and Glen Rains
Agronomy 2020, 10(8), 1137; https://0-doi-org.brum.beds.ac.uk/10.3390/agronomy10081137 - 05 Aug 2020
Cited by 13 | Viewed by 3807
Abstract
Cotton harvesting is performed by using expensive combine harvesters which makes it difficult for small to medium-size cotton farmers to grow cotton economically. Advances in robotics have provided an opportunity to harvest cotton using small and robust autonomous rovers that can be deployed [...] Read more.
Cotton harvesting is performed by using expensive combine harvesters which makes it difficult for small to medium-size cotton farmers to grow cotton economically. Advances in robotics have provided an opportunity to harvest cotton using small and robust autonomous rovers that can be deployed in the field as a “swarm” of harvesters, with each harvester responsible for a small hectarage. However, rovers need high-performance navigation to obtain the necessary precision for harvesting. Current precision harvesting systems depend heavily on Real-Time Kinematic Global Navigation Satellite System (RTK-GNSS) to navigate rows of crops. However, GNSS cannot be the only method used to navigate the farm because for robots to work as a coordinated multiagent unit on the same farm because they also require visual systems to navigate, avoid collisions, and to accommodate plant growth and canopy changes. Hence, the optical system remains to be a complementary method for increasing the efficiency of the GNSS. In this study, visual detection of cotton rows and bolls was developed, demonstrated, and evaluated. A pixel-based algorithm was used to calculate and determine the upper and lower part of the canopy of the cotton rows by assuming the normal distribution of the high and low depth pixels. The left and right rows were detected by using perspective transformation and pixel-based sliding window algorithms. Then, the system determined the Bayesian score of the detection and calculated the center of the rows for the smooth navigation of the rover. This visual system achieved an accuracy of 92.3% and an F1 score of 0.951 for the detection of cotton rows. Furthermore, the same stereo vision system was used to detect the location of the cotton bolls. A comparison of the cotton bolls’ distances above the ground to the manual measurements showed that the system achieved an average R2 value of 99% with a root mean square error (RMSE) of 9 mm when stationary and 95% with an RMSE of 34 mm when moving at approximately 0.64 km/h. The rover might have needed to stop several times to improve its detection accuracy or move more slowly. Therefore, the accuracy obtained in row detection and boll location estimation is favorable for use in a cotton harvesting robotic system. Future research should involve testing of the models in a large farm with undefoliated plants. Full article
(This article belongs to the Special Issue Towards Artificially Intelligent Robotics for Agriculture – Current Developments and New Trends)
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22 pages, 2858 KiB  
Article
A Design Criterion Based on Shear Energy Consumption for Robotic Harvesting Tools
by Eduardo Navas, Roemi Fernandez, Delia Sepúlveda, Manuel Armada and Pablo Gonzalez-de-Santos
Agronomy 2020, 10(5), 734; https://0-doi-org.brum.beds.ac.uk/10.3390/agronomy10050734 - 20 May 2020
Cited by 16 | Viewed by 3351
Abstract
Smart and precise agriculture has increasingly been developed in the last decade, and with that, the idea of optimizing the tools commonly used in this field. One way to improve these devices, particularly cutting tools conceived for harvesting purposes, is to measure the [...] Read more.
Smart and precise agriculture has increasingly been developed in the last decade, and with that, the idea of optimizing the tools commonly used in this field. One way to improve these devices, particularly cutting tools conceived for harvesting purposes, is to measure the shear energy consumption required for a particular plant. The aim of this research is to establish both a design criterion for cutting grippers and a quantifiable way to evaluate and classify a harvesting tool for a specific crop. This design criterion could help to minimize energy consumption in future harvesting robots, making them more energy-efficient. Full article
(This article belongs to the Special Issue Towards Artificially Intelligent Robotics for Agriculture – Current Developments and New Trends)
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Review

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19 pages, 1132 KiB  
Review
Unmanned Aerial Vehicles in Agriculture: A Survey
by Jaime del Cerro, Christyan Cruz Ulloa, Antonio Barrientos and Jorge de León Rivas
Agronomy 2021, 11(2), 203; https://0-doi-org.brum.beds.ac.uk/10.3390/agronomy11020203 - 21 Jan 2021
Cited by 89 | Viewed by 12136
Abstract
The number of tasks that nowadays are accomplished by using unmanned aerial vehicles is rising across many civil applications, including agriculture. Thus, this work aims at providing the reader with an overview of the agronomical use of unmanned aerial vehicles. The work starts [...] Read more.
The number of tasks that nowadays are accomplished by using unmanned aerial vehicles is rising across many civil applications, including agriculture. Thus, this work aims at providing the reader with an overview of the agronomical use of unmanned aerial vehicles. The work starts with a historical analysis of the use of aircrafts in agriculture, as pioneers of their use in modern precision agriculture techniques, currently applied by a high number of users. This survey has been carried out by providing a classification of the vehicles according to their typology and main sensorial and performance features. An extensive review of the most common applications and the advantages of using unmanned aerial vehicles is the core of the work. Finally, a brief summary of the key points of the legislation applicable to civil drones that could affect to agricultural applications is analyzed. Full article
(This article belongs to the Special Issue Towards Artificially Intelligent Robotics for Agriculture – Current Developments and New Trends)
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18 pages, 561 KiB  
Review
Crop Management with the IoT: An Interdisciplinary Survey
by Giuliano Vitali, Matteo Francia, Matteo Golfarelli and Maurizio Canavari
Agronomy 2021, 11(1), 181; https://0-doi-org.brum.beds.ac.uk/10.3390/agronomy11010181 - 18 Jan 2021
Cited by 28 | Viewed by 5982
Abstract
In this study, we analyze how crop management will benefit from the Internet of Things (IoT) by providing an overview of its architecture and components from agronomic and technological perspectives. The present analysis highlights that IoT is a mature enabling technology with articulated [...] Read more.
In this study, we analyze how crop management will benefit from the Internet of Things (IoT) by providing an overview of its architecture and components from agronomic and technological perspectives. The present analysis highlights that IoT is a mature enabling technology with articulated hardware and software components. Cheap networked devices can sense crop fields at a finer grain to give timeliness warnings on the presence of stress conditions and diseases to a wider range of farmers. Cloud computing allows reliable storage, access to heterogeneous data, and machine-learning techniques for developing and deploying farm services. From this study, it emerges that the Internet of Things will draw attention to sensor quality and placement protocols, while machine learning should be oriented to produce understandable knowledge, which is also useful to enhance cropping system simulation systems. Full article
(This article belongs to the Special Issue Towards Artificially Intelligent Robotics for Agriculture – Current Developments and New Trends)
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24 pages, 6074 KiB  
Review
Field Robots for Intelligent Farms—Inhering Features from Industry
by Pablo Gonzalez-de-Santos, Roemi Fernández, Delia Sepúlveda, Eduardo Navas, Luis Emmi and Manuel Armada
Agronomy 2020, 10(11), 1638; https://0-doi-org.brum.beds.ac.uk/10.3390/agronomy10111638 - 24 Oct 2020
Cited by 79 | Viewed by 11857
Abstract
Estimations of world population growth urgently require improving the efficiency of agricultural processes, as well as improving safety for people and environmental sustainability, which can be opposing characteristics. Industry is pursuing these objectives by developing the concept of the “intelligent factory” (also referred [...] Read more.
Estimations of world population growth urgently require improving the efficiency of agricultural processes, as well as improving safety for people and environmental sustainability, which can be opposing characteristics. Industry is pursuing these objectives by developing the concept of the “intelligent factory” (also referred to as the “smart factory”) and, by studying the similarities between industry and agriculture, we can exploit the achievements attained in industry for agriculture. This article focuses on studying those similarities regarding robotics to advance agriculture toward the concept of “intelligent farms” (smart farms). Thus, this article presents some characteristics that agricultural robots should gain from industrial robots to attain the intelligent farm concept regarding robot morphologies and features as well as communication, computing, and data management techniques. The study, restricted to robotics for outdoor farms due to the fact that robotics for greenhouse farms deserves a specific study, reviews different structures for robot manipulators and mobile robots along with the latest techniques used in intelligent factories to advance the characteristics of robotics for future intelligent farms. This article determines similarities, contrasts, and differences between industrial and field robots and identifies some techniques proven in the industry with an extraordinary potential to be used in outdoor farms such as those derived from methods based on artificial intelligence, cyber-physical systems, Internet of Things, Big Data techniques, and cloud computing procedures. Moreover, different types of robots already in use in industry and services are analyzed and their advantages in agriculture reported (parallel, soft, redundant, and dual manipulators) as well as ground and aerial unmanned robots and multi-robot systems. Full article
(This article belongs to the Special Issue Towards Artificially Intelligent Robotics for Agriculture – Current Developments and New Trends)
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