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Tagarakis AC, Bochtis D. Sensors and Robotics for Digital Agriculture. Sensors (Basel) 2023; 23:7255. [PMID: 37631794 PMCID: PMC10457808 DOI: 10.3390/s23167255] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/15/2023] [Accepted: 08/17/2023] [Indexed: 08/27/2023]
Abstract
The latest advances in innovative sensing and data technologies have led to an increasing implementation of autonomous systems in agricultural production processes [...].
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Affiliation(s)
- Aristotelis C. Tagarakis
- Institute for Bio-Economy and Agri-Technology (iBO), Centre for Research and Technology-Hellas (CERTH), 6th km Charilaou-Thermi Rd., 57001 Thessaloniki, Greece;
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Benos L, Moysiadis V, Kateris D, Tagarakis AC, Busato P, Pearson S, Bochtis D. Human-Robot Interaction in Agriculture: A Systematic Review. Sensors (Basel) 2023; 23:6776. [PMID: 37571559 PMCID: PMC10422385 DOI: 10.3390/s23156776] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/22/2023] [Revised: 07/19/2023] [Accepted: 07/27/2023] [Indexed: 08/13/2023]
Abstract
In the pursuit of optimizing the efficiency, flexibility, and adaptability of agricultural practices, human-robot interaction (HRI) has emerged in agriculture. Enabled by the ongoing advancement in information and communication technologies, this approach aspires to overcome the challenges originating from the inherent complex agricultural environments. Τhis paper systematically reviews the scholarly literature to capture the current progress and trends in this promising field as well as identify future research directions. It can be inferred that there is a growing interest in this field, which relies on combining perspectives from several disciplines to obtain a holistic understanding. The subject of the selected papers is mainly synergistic target detection, while simulation was the main methodology. Furthermore, melons, grapes, and strawberries were the crops with the highest interest for HRI applications. Finally, collaboration and cooperation were the most preferred interaction modes, with various levels of automation being examined. On all occasions, the synergy of humans and robots demonstrated the best results in terms of system performance, physical workload of workers, and time needed to execute the performed tasks. However, despite the associated progress, there is still a long way to go towards establishing viable, functional, and safe human-robot interactive systems.
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Affiliation(s)
- Lefteris Benos
- Institute for Bio-Economy and Agri-Technology (IBO), Centre of Research and Technology-Hellas (CERTH), Charilaou-Thermi Rd, 57001 Thessaloniki, Greece; (L.B.); (V.M.); (D.K.); (A.C.T.)
| | - Vasileios Moysiadis
- Institute for Bio-Economy and Agri-Technology (IBO), Centre of Research and Technology-Hellas (CERTH), Charilaou-Thermi Rd, 57001 Thessaloniki, Greece; (L.B.); (V.M.); (D.K.); (A.C.T.)
- Department of Computer Science and Telecommunications, University of Thessaly, 35131 Lamia, Greece
- FarmB Digital Agriculture S.A., 17th November 79, 55534 Thessaloniki, Greece
| | - Dimitrios Kateris
- Institute for Bio-Economy and Agri-Technology (IBO), Centre of Research and Technology-Hellas (CERTH), Charilaou-Thermi Rd, 57001 Thessaloniki, Greece; (L.B.); (V.M.); (D.K.); (A.C.T.)
| | - Aristotelis C. Tagarakis
- Institute for Bio-Economy and Agri-Technology (IBO), Centre of Research and Technology-Hellas (CERTH), Charilaou-Thermi Rd, 57001 Thessaloniki, Greece; (L.B.); (V.M.); (D.K.); (A.C.T.)
| | - Patrizia Busato
- Interuniversity Department of Regional and Urban Studies and Planning (DIST), Polytechnic of Turin, Viale Mattioli 39, 10125 Torino, Italy;
| | - Simon Pearson
- Lincoln Institute for Agri-Food Technology (LIAT), University of Lincoln, Lincoln LN6 7TS, UK;
| | - Dionysis Bochtis
- Institute for Bio-Economy and Agri-Technology (IBO), Centre of Research and Technology-Hellas (CERTH), Charilaou-Thermi Rd, 57001 Thessaloniki, Greece; (L.B.); (V.M.); (D.K.); (A.C.T.)
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Tagarakis AC, Filippou E, Kalaitzidis D, Benos L, Busato P, Bochtis D. Proposing UGV and UAV Systems for 3D Mapping of Orchard Environments. Sensors (Basel) 2022; 22:s22041571. [PMID: 35214470 PMCID: PMC8877329 DOI: 10.3390/s22041571] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/26/2021] [Revised: 02/13/2022] [Accepted: 02/14/2022] [Indexed: 01/15/2023]
Abstract
During the last decades, consumer-grade RGB-D (red green blue-depth) cameras have gained popularity for several applications in agricultural environments. Interestingly, these cameras are used for spatial mapping that can serve for robot localization and navigation. Mapping the environment for targeted robotic applications in agricultural fields is a particularly challenging task, owing to the high spatial and temporal variability, the possible unfavorable light conditions, and the unpredictable nature of these environments. The aim of the present study was to investigate the use of RGB-D cameras and unmanned ground vehicle (UGV) for autonomously mapping the environment of commercial orchards as well as providing information about the tree height and canopy volume. The results from the ground-based mapping system were compared with the three-dimensional (3D) orthomosaics acquired by an unmanned aerial vehicle (UAV). Overall, both sensing methods led to similar height measurements, while the tree volume was more accurately calculated by RGB-D cameras, as the 3D point cloud captured by the ground system was far more detailed. Finally, fusion of the two datasets provided the most precise representation of the trees.
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Affiliation(s)
- Aristotelis C. Tagarakis
- Institute for Bio-Economy and Agri-Technology (IBO), Centre for Research and Technology-Hellas (CERTH), 6th km Charilaou-Thermi Rd, GR 57001 Thessaloniki, Greece; (E.F.); (D.K.); (L.B.)
- Correspondence: (A.C.T.); (D.B.)
| | - Evangelia Filippou
- Institute for Bio-Economy and Agri-Technology (IBO), Centre for Research and Technology-Hellas (CERTH), 6th km Charilaou-Thermi Rd, GR 57001 Thessaloniki, Greece; (E.F.); (D.K.); (L.B.)
| | - Damianos Kalaitzidis
- Institute for Bio-Economy and Agri-Technology (IBO), Centre for Research and Technology-Hellas (CERTH), 6th km Charilaou-Thermi Rd, GR 57001 Thessaloniki, Greece; (E.F.); (D.K.); (L.B.)
| | - Lefteris Benos
- Institute for Bio-Economy and Agri-Technology (IBO), Centre for Research and Technology-Hellas (CERTH), 6th km Charilaou-Thermi Rd, GR 57001 Thessaloniki, Greece; (E.F.); (D.K.); (L.B.)
| | - Patrizia Busato
- Department of Agriculture, Forestry and Food Science (DISAFA), University of Turin, Largo Braccini 2, 10095 Grugliasco, Italy;
| | - Dionysis Bochtis
- Institute for Bio-Economy and Agri-Technology (IBO), Centre for Research and Technology-Hellas (CERTH), 6th km Charilaou-Thermi Rd, GR 57001 Thessaloniki, Greece; (E.F.); (D.K.); (L.B.)
- FarmB Digital Agriculture P.C., Doiranis 17, GR 54639 Thessaloniki, Greece
- Correspondence: (A.C.T.); (D.B.)
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Anagnostis A, Tagarakis AC, Kateris D, Moysiadis V, Sørensen CG, Pearson S, Bochtis D. Orchard Mapping with Deep Learning Semantic Segmentation. Sensors (Basel) 2021; 21:s21113813. [PMID: 34072975 PMCID: PMC8198531 DOI: 10.3390/s21113813] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/21/2021] [Revised: 05/21/2021] [Accepted: 05/27/2021] [Indexed: 12/19/2022]
Abstract
This study aimed to propose an approach for orchard trees segmentation using aerial images based on a deep learning convolutional neural network variant, namely the U-net network. The purpose was the automated detection and localization of the canopy of orchard trees under various conditions (i.e., different seasons, different tree ages, different levels of weed coverage). The implemented dataset was composed of images from three different walnut orchards. The achieved variability of the dataset resulted in obtaining images that fell under seven different use cases. The best-trained model achieved 91%, 90%, and 87% accuracy for training, validation, and testing, respectively. The trained model was also tested on never-before-seen orthomosaic images or orchards based on two methods (oversampling and undersampling) in order to tackle issues with out-of-the-field boundary transparent pixels from the image. Even though the training dataset did not contain orthomosaic images, it achieved performance levels that reached up to 99%, demonstrating the robustness of the proposed approach.
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Affiliation(s)
- Athanasios Anagnostis
- Institute for Bio-Economy and Agri-Technology (iBO), Centre for Research and Technology–Hellas (CERTH), GR57001 Thessaloniki, Greece; (A.A.); (A.C.T.); (V.M.); (D.B.)
- Department of Computer Science & Telecommunications, University of Thessaly, GR35131 Lamia, Greece
| | - Aristotelis C. Tagarakis
- Institute for Bio-Economy and Agri-Technology (iBO), Centre for Research and Technology–Hellas (CERTH), GR57001 Thessaloniki, Greece; (A.A.); (A.C.T.); (V.M.); (D.B.)
| | - Dimitrios Kateris
- Institute for Bio-Economy and Agri-Technology (iBO), Centre for Research and Technology–Hellas (CERTH), GR57001 Thessaloniki, Greece; (A.A.); (A.C.T.); (V.M.); (D.B.)
- Correspondence: ; Tel.: +30-242-109-6740
| | - Vasileios Moysiadis
- Institute for Bio-Economy and Agri-Technology (iBO), Centre for Research and Technology–Hellas (CERTH), GR57001 Thessaloniki, Greece; (A.A.); (A.C.T.); (V.M.); (D.B.)
| | - Claus Grøn Sørensen
- Department of Electrical and Computer Engineering, Aarhus University, DK-8000 Aarhus C, Denmark;
| | - Simon Pearson
- Lincoln Institute for Agri-Food Technology (LIAT), University of Lincoln, Lincoln LN6 7TS, UK;
| | - Dionysis Bochtis
- Institute for Bio-Economy and Agri-Technology (iBO), Centre for Research and Technology–Hellas (CERTH), GR57001 Thessaloniki, Greece; (A.A.); (A.C.T.); (V.M.); (D.B.)
- farmB Digital Agriculture P.C., Doiranis 17, GR54639 Thessaloniki, Greece
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Benos L, Tagarakis AC, Dolias G, Berruto R, Kateris D, Bochtis D. Machine Learning in Agriculture: A Comprehensive Updated Review. Sensors (Basel) 2021; 21:3758. [PMID: 34071553 PMCID: PMC8198852 DOI: 10.3390/s21113758] [Citation(s) in RCA: 56] [Impact Index Per Article: 18.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/06/2021] [Revised: 05/21/2021] [Accepted: 05/24/2021] [Indexed: 01/05/2023]
Abstract
The digital transformation of agriculture has evolved various aspects of management into artificial intelligent systems for the sake of making value from the ever-increasing data originated from numerous sources. A subset of artificial intelligence, namely machine learning, has a considerable potential to handle numerous challenges in the establishment of knowledge-based farming systems. The present study aims at shedding light on machine learning in agriculture by thoroughly reviewing the recent scholarly literature based on keywords' combinations of "machine learning" along with "crop management", "water management", "soil management", and "livestock management", and in accordance with PRISMA guidelines. Only journal papers were considered eligible that were published within 2018-2020. The results indicated that this topic pertains to different disciplines that favour convergence research at the international level. Furthermore, crop management was observed to be at the centre of attention. A plethora of machine learning algorithms were used, with those belonging to Artificial Neural Networks being more efficient. In addition, maize and wheat as well as cattle and sheep were the most investigated crops and animals, respectively. Finally, a variety of sensors, attached on satellites and unmanned ground and aerial vehicles, have been utilized as a means of getting reliable input data for the data analyses. It is anticipated that this study will constitute a beneficial guide to all stakeholders towards enhancing awareness of the potential advantages of using machine learning in agriculture and contributing to a more systematic research on this topic.
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Affiliation(s)
- Lefteris Benos
- Centre of Research and Technology-Hellas (CERTH), Institute for Bio-Economy and Agri-Technology (IBO), 6th km Charilaou-Thermi Rd, GR 57001 Thessaloniki, Greece; (L.B.); (A.C.T.); (G.D.); (D.K.)
| | - Aristotelis C. Tagarakis
- Centre of Research and Technology-Hellas (CERTH), Institute for Bio-Economy and Agri-Technology (IBO), 6th km Charilaou-Thermi Rd, GR 57001 Thessaloniki, Greece; (L.B.); (A.C.T.); (G.D.); (D.K.)
| | - Georgios Dolias
- Centre of Research and Technology-Hellas (CERTH), Institute for Bio-Economy and Agri-Technology (IBO), 6th km Charilaou-Thermi Rd, GR 57001 Thessaloniki, Greece; (L.B.); (A.C.T.); (G.D.); (D.K.)
| | - Remigio Berruto
- Department of Agriculture, Forestry and Food Science (DISAFA), University of Turin, Largo Braccini 2, 10095 Grugliasco, Italy;
| | - Dimitrios Kateris
- Centre of Research and Technology-Hellas (CERTH), Institute for Bio-Economy and Agri-Technology (IBO), 6th km Charilaou-Thermi Rd, GR 57001 Thessaloniki, Greece; (L.B.); (A.C.T.); (G.D.); (D.K.)
| | - Dionysis Bochtis
- Centre of Research and Technology-Hellas (CERTH), Institute for Bio-Economy and Agri-Technology (IBO), 6th km Charilaou-Thermi Rd, GR 57001 Thessaloniki, Greece; (L.B.); (A.C.T.); (G.D.); (D.K.)
- FarmB Digital Agriculture P.C., Doiranis 17, GR 54639 Thessaloniki, Greece
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