Smart Farming Using Robots in IoT to Increase Agriculture Yields: A Systematic Literature Review

Mochammad Haldi Widianto, Budi Juarto

Abstract


Robots are beneficial in everyday life, especially in helping food security in the agricultural industry. Smart farming alone is not enough because smart farming is only automated without mobile hardware. The existence of robots can minimize human involvement in agriculture so that humans can maximize activities outside of farms. This Study aims to review articles regarding robots in smart farming to increase agriclture yields. This article systematically uses the systematic literature review method utilizing the Preferred reporting items for systematic review and meta-analyses (PRISMA) by submitting 3 Research Questions (RQ). According to the authors of the 3 RQs, it is necessary to represent the function and purpose of robots in farms and to be used in the context of the importance of robots in agriculture because of the potential impact of increase agriculture yields. This Research contributes to finding and answering 3 RQ, which are the roots of the use of robots. The results taken, the authors get 116 articles that can be reviewed and answered RQ and achieve goals. RQ 1 was responded to with the article's country of origin, research criteria, and the year of the article. In RQ 2 the author answered that Research often carried out 6 schemes, then the most Research was (Challenge Robots, Ethics, and Opinions in Agriculture) and (Design, Planning, and Robotic Systems in Agriculture). Finally, in RQ 3, the author describes the research scheme based on understanding related Research. The author hopes this basic scheme can be a benchmark or a new direction for future researchers and related agricultural industries to improve agricultural quality.

Keywords


Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA); Robot; Smart Farming; Systematic Literature Review.

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References


M. Hasan, M. U. Islam, and M. J. Sadeq, “Towards technological adaptation of advanced farming through AI, IoT, and Robotics: A Comprehensive overview,” Artificial Intelligence and Smart Agriculture Technology, 2022, doi: 10.1201/9781003299059-2.

M. H. Widianto, R. Ranny, T. E. Suherman, and J. Chiedi, “Internet of things for detection disaster combined with tracking AR navigation,” International Journal of Engineering Trends and Technology, vol. 69, no. 8, pp. 211–217, 2021, doi: 10.14445/22315381/IJETT-V69I8P226.

S. Mohamed, K. Sethom, and A. J. Obaid, “IOT-Based Personalized products recommendation system,” in Journal of Physics: Conference Series, IOP Publishing Ltd, Jul. 2021. doi: 10.1088/1742-6596/1963/1/012088.

T. Popović, N. Latinović, A. Pešić, Ž. Zečević, B. Krstajić, and S. Djukanović, “Architecting an IoT-enabled platform for precision agriculture and ecological monitoring: A case study,” Comput Electron Agric, vol. 140, pp. 255–265, 2017, doi: 10.1016/j.compag.2017.06.008.

J. V. Y. Martnez, A. F. Skarmeta, M. A. Zamora-Izquierdo, and A. P. Ramallo-Gonzlez, “IoT-based data management for Smart Agriculture,” in 2020 Second International Conference on Embedded Distributed Systems (EDiS), pp. 41–46, Nov. 2020, doi: 10.1109/EDiS49545.2020.9296443.

S. Jacob et al., “AI and IoT-Enabled Smart Exoskeleton System for Rehabilitation of Paralyzed People in Connected Communities,” IEEE Access, vol. 9, pp. 80340–80350, 2021, doi: 10.1109/ACCESS.2021.3083093.

F. Zhang, W. Zhang, X. Luo, Z. Zhang, Y. Lu, and B. Wang, “Developing an IoT-Enabled Cloud Management Platform for Agricultural Machinery Equipped with Automatic Navigation Systems,” Agriculture, vol. 12, no. 2, p. 310, Feb. 2022, doi: 10.3390/agriculture12020310.

S. Qazi, B. A. Khawaja, and Q. U. Farooq, “IoT-Equipped and AI-Enabled Next Generation Smart Agriculture: A Critical Review, Current Challenges and Future Trends,” IEEE Access, vol. 10, pp. 21219–21235, 2022, doi: 10.1109/ACCESS.2022.3152544.

S. Kim, M. Lee, and C. Shin, “IoT-Based Strawberry Disease Prediction System for Smart Farming,” Sensors, vol. 18, no. 11, p. 4051, Nov. 2018, doi: 10.3390/s18114051.

M. Bansal, A. Kumar, and A. Virmani, “Green IoT: Current Scenario & Future Prospects,” Journal of Trends in Computer Science and Smart Technology, vol. 2, no. 4, pp. 173–180, 2021, doi: 10.36548/jtcsst.2020.4.001.

E. P. Yadav, E. A. Mittal, and H. Yadav, “IoT: Challenges and Issues in Indian Perspective,” in 2018 3rd International Conference On Internet of Things: Smart Innovation and Usages (IoT-SIU), pp. 1–5, Feb. 2018, doi: 10.1109/IoT-SIU.2018.8519869.

P. Phupattanasilp and S. R. Tong, “Augmented reality in the integrative internet of things (AR-IoT): Application for precision farming,” Sustainability, vol. 11, no. 9, p. 2658, 2019, doi: 10.3390/su11092658.

H. Park and S.-B. Rhee, “IoT-Based Smart Building Environment Service for Occupants’ Thermal Comfort,” J. Sens., vol. 2018, p. 1757409, 2018, doi: 10.1155/2018/1757409.

M. Shakeri, A. Sadeghi-Niaraki, S. M. Choi, and S. M. Riazul Islam, “Performance analysis of iot-based health and environment wsn deployment,” Sensors, vol. 20, no. 20, p. 5923, Oct. 2020, doi: 10.3390/s20205923.

A. M. Ciruela-Lorenzo, A. R. Del-Aguila-Obra, A. Padilla-Meléndez, and J. J. Plaza-Angulo, “Digitalization of agri-cooperatives in the smart agriculture context. Proposal of a digital diagnosis tool,” Sustainability, vol. 12, no. 4, p. 1325, Feb. 2020, doi: 10.3390/su12041325.

Z. Shahbazi and Y. C. Byun, “Machine Learning-Based Analysis of Cryptocurrency Market Financial Risk Management,” IEEE Access, vol. 10, pp. 37848–37856, 2022, doi: 10.1109/ACCESS.2022.3162858.

J. Kang, J. Kim, M. Kim, and M. Sohn, “Machine Learning-Based Energy-Saving Framework for Environmental States-Adaptive Wireless Sensor Network,” IEEE Access, vol. 8, pp. 69359–69367, 2020, doi: 10.1109/ACCESS.2020.2986507.

R. Alfred, J. H. Obit, C. P.-Y. Chin, H. Haviluddin, and Y. Lim, “Towards Paddy Rice Smart Farming: A Review on Big Data, Machine Learning, and Rice Production Tasks,” IEEE Access, vol. 9, pp. 50358–50380, 2021, doi: 10.1109/ACCESS.2021.3069449.

M. M. Rathore, S. A. Shah, D. Shukla, E. Bentafat, and S. Bakiras, “The Role of AI, Machine Learning, and Big Data in Digital Twinning: A Systematic Literature Review, Challenges, and Opportunities,” IEEE Access, vol. 9, pp. 32030–32052, 2021, doi: 10.1109/ACCESS.2021.3060863.

K. Pahwa and N. Agarwal, “Stock Market Analysis using Supervised Machine Learning,” in 2019 International Conference on Machine Learning, Big Data, Cloud and Parallel Computing (COMITCon), pp. 197–200, Feb. 2019, doi: 10.1109/COMITCon.2019.8862225.

M. J. Mia, S. K. Maria, S. S. Taki, and A. A. Biswas, “Cucumber disease recognition using machine learning and transfer learning,” Bulletin of Electrical Engineering and Informatics, vol. 10, no. 6, pp. 3432–3443, Dec. 2021, doi: 10.11591/eei.v10i6.3096.

P. Parameswari, N. Rajathi, and K. J. Harshanaa, “Machine Learning Approaches for Crop Recommendation,” in 2021 International Conference on Advancements in Electrical, Electronics, Communication, Computing and Automation (ICAECA), pp. 1–5, Oct. 2021, doi: 10.1109/ICAECA52838.2021.9675480.

K. Arulkumaran, M. P. Deisenroth, M. Brundage, and A. A. Bharath, “Deep Reinforcement Learning: A Brief Survey,” IEEE Signal Process Mag, vol. 34, no. 6, pp. 26–38, Nov. 2017, doi: 10.1109/MSP.2017.2743240.

A. Farhad, D.-H. Kim, J.-S. Yoon, and J.-Y. Pyun, “Deep Learning-Based Channel Adaptive Resource Allocation in LoRaWAN,” in 2022 International Conference on Electronics, Information, and Communication (ICEIC), pp. 1–5, Feb. 2022, doi: 10.1109/ICEIC54506.2022.9748580.

M. Sami et al., “A Deep Learning-Based Sensor Modeling for Smart Irrigation System,” Agronomy, vol. 12, no. 1, p. 212, Jan. 2022, doi: 10.3390/agronomy12010212.

M. A. Khan et al., “A Deep Learning-Based Intrusion Detection System for MQTT Enabled IoT,” Sensors, vol. 21, no. 21, p. 7016, 2021, doi: 10.3390/s21217016.

A. A. Tesfay, E. P. Simon, S. Kharbech, and L. Clavier, “Deep Learning-based Signal Detection for Uplink in LoRa-like Networks,” in 2021 IEEE 32nd Annual International Symposium on Personal, Indoor and Mobile Radio Communications (PIMRC), pp. 617–621, Sep. 2021, doi: 10.1109/PIMRC50174.2021.9569470.

B. Jabir and N. Falih, “Deep learning-based decision support system for weeds detection in wheat fields,” International Journal of Electrical and Computer Engineering, vol. 12, no. 1, pp. 816–825, Feb. 2022, doi: 10.11591/ijece.v12i1.pp816-825.

Q. Li and J. Kim, “A deep learning-based course recommender system for sustainable development in education,” Applied Sciences, vol. 11, no. 19, p. 8993, Oct. 2021, doi: 10.3390/app11198993.

J. Sang, J. Yu, R. Jain, R. Lienhart, P. Cui, and J. Feng, “Deep Learning for Multimedia: Science or Technology?,” in Proceedings of the 26th ACM International Conference on Multimedia, pp. 1354–1355, 2018, doi: 10.1145/3240508.3243931.

L. Chen et al., “Estimating Soil Moisture Over Winter Wheat Fields During Growing Season Using Machine-Learning Methods,” IEEE J Sel Top Appl Earth Obs Remote Sens, vol. 14, pp. 3706–3718, 2021, doi: 10.1109/JSTARS.2021.3067890.

A. Khan, S. Aziz, M. Bashir, and M. U. Khan, “IoT and Wireless Sensor Network based Autonomous Farming Robot,” in 2020 International Conference on Emerging Trends in Smart Technologies (ICETST), pp. 1–5, Mar. 2020, doi: 10.1109/ICETST49965.2020.9080736.

S. Fountas, N. Mylonas, I. Malounas, E. Rodias, C. Hellmann Santos, and E. Pekkeriet, “Agricultural Robotics for Field Operations,” Sensors, vol. 20, no. 9, p. 2672, May 2020, doi: 10.3390/s20092672.

P. Gonzalez-de-Santos, R. Fernández, D. Sepúlveda, E. Navas, L. Emmi, and M. Armada, “Field Robots for Intelligent Farms—Inhering Features from Industry,” Agronomy, vol. 10, no. 11, p. 1638, Oct. 2020, doi: 10.3390/agronomy10111638.

M. J. Page et al., “The PRISMA 2020 statement: An updated guideline for reporting systematic reviews,” BMJ, vol. 372, Mar. 2021, doi: 10.1136/bmj.n71.

D. Moher, A. Liberati, J. Tetzlaff, and D. G. Altman, “Preferred reporting items for systematic reviews and meta-analyses: the PRISMA statement,” Journal of Clinical Epidemiology, vol. 62, no. 10, pp. 1006–1012, 2009, doi: 10.1016/j.jclinepi.2009.06.005.

M. H. Widianto, I. Ardimansyah, H. I. Pohan, and D. R. Hermanus, “A Systematic Review of Current Trends in Artificial Intelligence for Smart Farming to Enhance Crop Yield,” Journal of Robotics and Control (JRC), vol. 3, no. 3, 2022, doi: 10.18196/jrc.v3i3.13760.

E. Navarro, N. Costa, and A. Pereira, “A Systematic Review of IoT Solutions for Smart Farming,” Sensors, vol. 20, no. 15, p. 4231, Jul. 2020, doi: 10.3390/s20154231.

S. Terence and G. Purushothaman, “Systematic Review of Internet of Things in Smart Farming,” Trans. Emerg. Telecommun. Technol., vol. 31, no. 6, Jun. 2020, doi: 10.1002/ett.3958.

Z. Ünal, “Smart Farming Becomes even Smarter with Deep Learning - A Bibliographical Analysis,” IEEE Access, vol. 8, pp. 105587–105609, 2020, doi: 10.1109/ACCESS.2020.3000175.

E. S. Mohamed, A. A. Belal, S. Kotb Abd-Elmabod, M. A. El-Shirbeny, A. Gad, and M. B. Zahran, “Smart farming for improving agricultural management,” Egyptian Journal of Remote Sensing and Space Science, vol. 24, no. 3, pp. 971–981, Dec. 2021, doi: 10.1016/j.ejrs.2021.08.007.

M. H. Widianto, A. Sinaga, and M. A. Ginting, “A Systematic Review of LPWAN and Short-Range Network using AI to Enhance Internet of Things,” Journal of Robotics and Control (JRC), vol. 3, no. 4, 2022, doi: 10.18196/jrc.v3i4.15419.

W. S. Alaloul, M. Altaf, M. A. Musarat, M. F. Javed, and A. Mosavi, “Systematic Review of Life Cycle Assessment and Life Cycle Cost Analysis for Pavement and a Case Study,” Sustainability, vol. 13, no. 8, p. 4377, Apr. 2021, doi: 10.3390/su13084377.

T. Le, J. G. Omholt Gjevestad, and P. J. From, “Online 3D Mapping and Localization System for Agricultural Robots,” IFAC-PapersOnLine, vol. 52, no. 30, pp. 167–172, 2019, doi: 10.1016/j.ifacol.2019.12.516.

R. Raja, T. T. Nguyen, V. L. Vuong, and D. C. Slaughter, “RTD-SEPs: Real-time detection of stem emerging points and classification of crop-weed for robotic weed control in producing tomato,” Biosyst. Eng., 2020, doi: 10.1016/j.biosystemseng.2020.05.004.

R. Cabello Ruiz, A. Jiménez Ramírez, M. J. Escalona Cuaresma, and J. González Enríquez, “Hybridizing humans and robots: An RPA horizon envisaged from the trenches,” Comput. Ind., vol. 138, Jun. 2022, doi: 10.1016/j.compind.2022.103615.

A. Sharma, S. Saini, J. W. Liu, N. Paliwal, and P. Vanjani, “Image processing-based intelligent robotic system for assistance of agricultural crops,” International Journal of Social and Humanistic Computing, vol. 3, no. 2, p. 191, 2019, doi: 10.1504/ijshc.2019.10023088.

Y. Ge, Y. Xiong, and P. J. From, “Symmetry-based 3D shape completion for fruit localisation for harvesting robots,” Biosyst. Eng., vol. 197, pp. 188–202, Sep. 2020, doi: 10.1016/j.biosystemseng.2020.07.003.

R. Raja, T. T. Nguyen, D. C. Slaughter, and S. A. Fennimore, “Real-time weed-crop classification and localisation technique for robotic weed control in lettuce,” Biosyst. Eng., 2020, doi: 10.1016/j.biosystemseng.2020.02.002.

Y. Du, G. Zhang, D. Tsang, and M. K. Jawed, “Deep-CNN based Robotic Multi-Class Under-Canopy Weed Control in Precision Farming,” in 2022 IEEE International Conference on Robotics and Automation (ICRA), pp. 2273–2279, May 2021, doi: 10.1109/ICRA46639.2022.9812240.

S. Cubero, E. Marco‐noales, N. Aleixos, S. Barbé, and J. Blasco, “RobHortic: A field robot to detect pests and diseases in horticultural crops by proximal sensing,” Agriculture, vol. 10, no. 7, p. 276, Jul. 2020, doi: 10.3390/agriculture10070276.

M. Halstead, C. McCool, S. Denman, T. Perez and C. Fookes, "Fruit Quantity and Ripeness Estimation Using a Robotic Vision System," in IEEE Robotics and Automation Letters, vol. 3, no. 4, pp. 2995-3002, Oct. 2018, doi: 10.1109/LRA.2018.2849514.

S. Erfani, A. Jafari, and A. Hajiahmad, “Comparison of two data fusion methods for localization of wheeled mobile robot in farm conditions,” Artificial Intelligence in Agriculture, vol. 1, pp. 48–55, Mar. 2019, doi: 10.1016/j.aiia.2019.05.002.

D. Gogoll, P. Lottes, J. Weyler, N. Petrinic, and C. Stachniss, "Unsupervised Domain Adaptation for Transferring Plant Classification Systems to New Field Environments, Crops, and Robots," 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 2636-2642, 2020, doi: 10.1109/IROS45743.2020.9341277.

A. Anagnostis, L. Benos, D. Tsaopoulos, A. Tagarakis, N. Tsolakis, and D. Bochtis, “Human activity recognition through recurrent neural networks for human–robot interaction in agriculture,” Applied Sciences, vol. 11, no. 5, p. 2188, Mar. 2021, doi: 10.3390/app11052188.

C. C. Ulloa, A. Krus, A. Barrientos, J. Del Cerro, and C. Valero, “Robotic fertilisation using localisation systems based on point clouds in strip-cropping fields,” Agronomy, vol. 11, no. 1, p. 11, Dec. 2021, doi: 10.3390/AGRONOMY11010011.

S. Matsuzaki, H. Masuzawa, J. Miura, and S. Oishi, "3D Semantic Mapping in Greenhouses for Agricultural Mobile Robots with Robust Object Recognition Using Robots' Trajectory," 2018 IEEE International Conference on Systems, Man, and Cybernetics (SMC), pp. 357-362, 2018, doi: 10.1109/SMC.2018.00070.

S. Wan and S. Goudos, “Faster R-CNN for multi-class fruit detection using a robotic vision system,” Computer Networks, vol. 168, Feb. 2020, doi: 10.1016/j.comnet.2019.107036.

T. Kounalakis, G. A. Triantafyllidis, and L. Nalpantidis, “Image-based recognition framework for robotic weed control systems,” Multimed. Tools Appl., vol. 77, no. 8, pp. 9567–9594, Apr. 2018, doi: 10.1007/s11042-017-5337-y.

E. Vrochidou et al., “An Autonomous Grape-Harvester Robot: Integrated System Architecture,” Electronics (Basel), vol. 10, no. 9, p. 1056, Apr. 2021, doi: 10.3390/electronics10091056.

K. Ishii et al., “Tomato-Harvesting-Robot Competition Towards Smart Agriculture,” in Proceedings of International Conference on Artificial Life and Robotics, pp. 1–5, Jan. 2021, doi: 10.5954/ICAROB.2021.PS-1.

H. Sori, H. Inoue, H. Hatta, and Y. Ando, “Effect for a paddy weeding robot in wet rice culture,” Journal of Robotics and Mechatronics, vol. 30, no. 2, pp. 198–205, Apr. 2018, doi: 10.20965/jrm.2018.p0198.

A. Roshanianfard and N. Noguchi, “Characterization of pumpkin for a harvesting robot,” IFAC-PapersOnLine, vol. 51, no. 17, pp. 23–30, Jan. 2018, doi: 10.1016/j.ifacol.2018.08.056.

M. Peebles, J. J. Barnett, M. Duke, and S. H. Lim, "Robotic Harvesting of Asparagus using Machine Learning and Time-of-Flight Imaging – Overview of Development and Field Trials," 2020 IEEE 16th International Conference on Automation Science and Engineering (CASE), pp. 1361-1366, 2020, doi: 10.1109/CASE48305.2020.9217006.

T. Yoshida, T. Fukao, and T. Hasegawa, “Cutting point detection using a robot with point clouds for tomato harvesting,” Journal of Robotics and Mechatronics, vol. 32, no. 2, pp. 437–444, 2020, doi: 10.20965/jrm.2020.p0437.

A. Roshanianfard and N. Noguchi, “Kinematics analysis and simulation of a 5DOF articulated robotic arm applied to heavy products harvesting,” Tarim Bilimleri Dergisi, vol. 24, no. 1, pp. 91–104, 2018, doi: 10.15832/ankutbd.446396.

A. Roshanianfard and N. Noguchi, “Pumpkin harvesting robotic end-effector,” Computers and Electronics in Agriculture, vol. 174, p. 105503, 2020, doi: 10.1016/j.compag.2020.105503.

R. K. Megalingam et al., “Amaran: An Unmanned Robotic Coconut Tree Climber and Harvester,” IEEE/ASME Transactions on Mechatronics, vol. 26, no. 1, pp. 288–299, Feb. 2021, doi: 10.1109/TMECH.2020.3014293.

H. Wang, C. J. Hohimer, S. Bhusal, M. Karkee, C. Mo, and J. H. Miller, “Simulation As A Tool In Designing And Evaluating A Robotic Apple Harvesting System⁎,” IFAC-PapersOnLine, vol. 51, no. 17, pp. 135–140, Jan. 2018, doi: 10.1016/j.ifacol.2018.08.076.

T. Wang, X. Xu, C. Wang, Z. Li, and D. Li, “From smart farming towards unmanned farms: A new mode of agricultural production,” Agriculture, vol. 11, no. 2, p. 145, Feb. 2021, doi: 10.3390/agriculture11020145.

A. Yeshmukhametov, K. Koganezawa, Y. Yamamoto, Z. Buribayev, Z. Mukhtar, and Y. Amirgaliyev, “Development of Continuum Robot Arm and Gripper for Harvesting Cherry Tomatoes,” Applied Sciences, vol. 12, no. 14, Jul. 2022, doi: 10.3390/app12146922.

E. Vrochidou, V. N. Tsakalidou, I. Kalathas, T. Gkrimpizis, T. Pachidis, and V. G. Kaburlasos, “An Overview of End Effectors in Agricultural Robotic Harvesting Systems,” Agriculture, vol. 12, no. 8, p. 1240, Aug. 2022, doi: 10.3390/agriculture12081240.

R. Orsini et al., “Setting of a precision farming robotic laboratory for cropping system sustainability and food safety and security: Preliminary results,” in IOP Conference Series: Earth and Environmental Science, vol. 275, p. 012021, 2019. doi: 10.1088/1755-1315/275/1/012021.

E. Abana, M. Pacion, R. Sordilla, D. Montaner, D. Agpaoa, and R. M. Allam, “Rakebot: A robotic rake for mixing paddy in sun drying,” Indonesian Journal of Electrical Engineering and Computer Science, vol. 14, no. 3, pp. 1165–1170, Jun. 2019, doi: 10.11591/ijeecs.v14.i3.pp1165-1170.

M. M. Rahman, K. Ishii, and N. Noguchi, “Optimum harvesting area of convex and concave polygon field for path planning of robot combine harvester,” Intell Serv Robot, vol. 12, no. 2, pp. 167–179, Apr. 2019, doi: 10.1007/s11370-018-00273-4.

J. Lowenberg-DeBoer, I. Y. Huang, V. Grigoriadis, and S. Blackmore, “Economics of robots and automation in field crop production,” Precis Agric, vol. 21, no. 2, pp. 278–299, Apr. 2020, doi: 10.1007/s11119-019-09667-5.

J. Jun, J. Kim, J. Seol, J. Kim, and H. il Son, “Towards an Efficient Tomato Harvesting Robot: 3D Perception, Manipulation, and End-Effector,” IEEE Access, vol. 9, pp. 17631–17640, 2021, doi: 10.1109/ACCESS.2021.3052240.

S. G. Potts, P. Neumann, B. Vaissière, and N. J. Vereecken, “Robotic bees for crop pollination: Why drones cannot replace biodiversity,” Science of The Total Environment, vol. 642, pp. 665–667, 2018, doi: 10.1016/j.scitotenv.2018.06.114.

T. Utstumo et al., “Robotic in-row weed control in vegetables,” Comput. Electron. Agric., vol. 154, pp. 36–45, 2018, doi: 10.1016/j.compag.2018.08.043.

H. Zhou, X. Wang, W. Au, H. Kang, and C. Chen, “Intelligent robots for fruit harvesting: recent developments and future challenges,” Precis. Agric., vol. 23, no. 5, pp. 1856–1907, 2022, doi: 10.1007/s11119-022-09913-3.

H. Durmuş and E. Gunes, “Integration of the Mobile Robot and Internet of Things to Collect Data from the Agricultural Fields,” in 2019 8th International Conference on Agro-Geoinformatics (Agro-Geoinformatics), pp. 1–5, Nov. 2019, doi: 10.1109/Agro-Geoinformatics.2019.8820578.

B. Ranjitha, M. N. Nikhitha, K. Aruna, Afreen, and B. T. V. Murthy, “Solar Powered Autonomous Multipurpose Agricultural Robot Using Bluetooth/Android App,” in 2019 3rd International conference on Electronics, Communication and Aerospace Technology (ICECA), pp. 872–877, Jun. 2019, doi: 10.1109/ICECA.2019.8821919.

T. D. Le, V. R. Ponnambalam, J. G. O. Gjevestad, and P. J. From, “A low-cost and efficient autonomous row-following robot for food production in polytunnels,” J. Field Robot., vol. 37, no. 2, pp. 309–321, Mar. 2020, doi: 10.1002/rob.21878.

E. Barnes et al., “Opportunities for Robotic Systems and Automation in Cotton Production,” AgriEngineering, vol. 3, no. 2, pp. 339–362, May 2021, doi: 10.3390/agriengineering3020023.

L. Cantelli, F. Bonaccorso, D. Longo, C. D. Melita, G. Schillaci, and G. Muscato, “A Small Versatile Electrical Robot for Autonomous Spraying in Agriculture,” AgriEngineering, vol. 1, no. 3, pp. 391–402, Aug. 2019, doi: 10.3390/agriengineering1030029.

K. G. Devi, C. S. Kumar, and B. Kishore, “A Survey on the Design of Autonomous and Semi Autonomous Pesticide Sprayer Robot,” El-Cezeri Journal of Science and Engineering, vol. 9, no. 1, pp. 371–381, 2022. doi: 10.31202/ecjse.1005808.

A. E. Eiben, E. Hart, J. Timmis, A. M. Tyrrell, and A. F. Winfield, “Towards Autonomous Robot Evolution,” in Software Engineering for Robotics, pp. 29–51, 2021, doi: 10.1007/978-3-030-66494-7_2.

R. Osei-Amponsah et al., “Heat stress impacts on lactating cows grazing australian summer pastures on an automatic robotic dairy,” Animals, vol. 10, no. 5, p. 869, May 2020, doi: 10.3390/ani10050869.

L. Grimstad, R. Zakaria, T. Dung Le, and P. J. From, “A Novel Autonomous Robot for Greenhouse Applications,” 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 1-9, 2018, doi: 10.1109/IROS.2018.8594233.

S. Pearson et al., “Robotics and Autonomous Systems for Net Zero Agriculture,” Current Robotics Reports, vol. 3, no. 2, pp. 57–64, Jun. 2022, doi: 10.1007/s43154-022-00077-6.

J. Tian, H. Niu, P. Wang, and Y. Q. Chen, “Smart and Autonomous Farm Field Scouting Service Robot as an edge device under $1000: Challenges and opportunities,” in ASME 2019 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference, vol. 59292, 2019, doi: 10.1115/DETC2019-98259.

L. N. K. Duong et al., “A review of robotics and autonomous systems in the food industry: From the supply chains perspective,” Trends in Food Science & Technology, vol. 106, pp. 355–364, 2020, doi: 10.1016/j.tifs.2020.10.028.

S. Bernardini et al., “A Multi-Robot Platform for the Autonomous Operation and Maintenance of Offshore Wind Farms,” in Proceedings of the 19th International Conference on Autonomous Agents and MultiAgent Systems, pp. 1696–1700, 2020.

M. Linsinger, J. Stecken, J. Kutschinski, and B. Kuhlenkötter, “Situational task change of lightweight robots in hybrid assembly systems,” Procedia CIRP, vol. 81, pp. 81–86, 2019, doi: 10.1016/j.procir.2019.03.015.

J. Franko, S. Du, S. Kallweit, E. Duelberg, and H. Engemann, “Design of a multi-robot system for wind turbine maintenance,” Energies (Basel), vol. 13, no. 10, p. 2552, May 2020, doi: 10.3390/en13102552.

N. Tsolakis, D. Bechtsis, and D. Bochtis, “Agros: A robot operating system based emulation tool for agricultural robotics,” Agronomy, vol. 9, no. 7, p. 403, Jul. 2019, doi: 10.3390/agronomy9070403.

T. Dewi, P. Risma, and Y. Oktarina, “Fruit sorting robot based on color and size for an agricultural product packaging system,” Bulletin of Electrical Engineering and Informatics, vol. 9, no. 4, pp. 1438–1445, Aug. 2020, doi: 10.11591/eei.v9i4.2353.

W. Coral, C. Rossi, O. M. Curet, and D. Castro, “Design and assessment of a flexible fish robot actuated by shape memory alloys,” Bioinspiration & Biomimetics, vol. 13, no. 5, Jul. 2018, doi: 10.1088/1748-3190/aad0ae.

Ö. Akyazı, E. Şahin, T. Özsoy, and M. Algül, “A Solar Panel Cleaning Robot Design and Application,” European Journal of Science and Technology, pp. 343–348, Oct. 2019, doi: 10.31590/ejosat.638291.

C. Cariou and Z. Gobor, “Trajectory planning for robotic maintenance of pasture based on approximation algorithms,” Biosyst Eng, vol. 174, pp. 219–230, Oct. 2018, doi: 10.1016/j.biosystemseng.2018.07.009.

J. Strader et al., “Flower Interaction Subsystem for a Precision Pollination Robot,” in 2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 5534–5541, Nov. 2019, doi: 10.1109/IROS40897.2019.8967752.

P. Kumar and G. Ashok, “Design and fabrication of smart seed sowing robot,” Materials Today: Proceedings, vol. 39, pp. 354–358, 2020, doi: 10.1016/j.matpr.2020.07.432.

N. Noguchi, “Agricultural vehicle robot,” Journal of Robotics and Mechatronics, vol. 30, no. 2, pp. 165–172, Apr. 2018, doi: 10.20965/jrm.2018.p0165.

A. A. Chand et al., “Design and analysis of photovoltaic powered battery-operated computer vision-based multi-purpose smart farming robot,” Agronomy, vol. 11, no. 3, p. 530, Mar. 2021, doi: 10.3390/agronomy11030530.

H. Wang and N. Noguchi, “Adaptive turning control for an agricultural robot tractor,” International Journal of Agricultural and Biological Engineering, vol. 11, no. 6, pp. 113–119, 2018, doi: 10.25165/j.ijabe.20181106.3605.

T. Dewi, S. Nurmaini, P. Risma, Y. Oktarina, and M. Roriz, “Inverse kinematic analysis of 4 DOF pick and place arm robot manipulator using fuzzy logic controller,” International Journal of Electrical and Computer Engineering, vol. 10, no. 2, pp. 1376–1386, 2020, doi: 10.11591/ijece.v10i2.pp1376-1386.

M. Kondoyanni, D. Loukatos, C. Maraveas, C. Drosos, and K. G. Arvanitis, “Bio‐Inspired Robots and Structures toward Fostering the Modernization of Agriculture,” Biomimetics, vol. 7, no. 2, p. 69, May 2022, doi: 10.3390/biomimetics7020069.

J. Kim and H. il Son, “A Voronoi Diagram-Based Workspace Partition for Weak Cooperation of Multi-Robot System in Orchard,” IEEE Access, vol. 8, pp. 20676–20686, 2020, doi: 10.1109/ACCESS.2020.2969449.

R. Berenstein, A. Wallach, P. E. Moudio, P. Cuellar, and K. Goldberg, “An Open-Access Passive Modular Tool Changing System for Mobile Manipulation Robots,” in 2018 IEEE 14th International Conference on Automation Science and Engineering (CASE), Aug. 2018, pp. 592–598. doi: 10.1109/COASE.2018.8560398.

L. C. Santos, F. N. Santos, E. J. Solteiro Pires, A. Valente, P. Costa, and S. Magalhães, “Path Planning for ground robots in agriculture: a short review,” 2020 IEEE International Conference on Autonomous Robot Systems and Competitions (ICARSC), 2020, pp. 61-66, doi: 10.1109/ICARSC49921.2020.9096177.

N. Zlatov, G. Hristov, D. Kinaneva, Y. Yotov, and P. Zahariev, “Research of the Present and Emerging Applications of Smart Robots and Unmanned Aerial Vehicles in the Agriculture Domain,” International Journal of Mechatronics and Applied Mechanics, vol. 10, 2021, doi: 10.17683/ijomam/issue10/v1.11.

A. E. B. Velasquez, V. A. H. Higuti, M. V. Gasparino, A. N. Sivakumar, M. Becker, and G. Chowdhary, “Multi-Sensor Fusion based Robust Row Following for Compact Agricultural Robots,” Field Robotics, Jun. 2022, doi: 10.55417/fr.2022043.

M. Mazar, M. Sahnoun, B. Bettayeb, N. Klement, and A. Louis, “Simulation and optimization of robotic tasks for UV treatment of diseases in horticulture,” Operational Research, vol. 22, pp. 49-75, Mar. 2020, doi: 10.1007/s12351-019-00541-w.

D. Xie, L. Chen, L. Liu, L. Chen, and H. Wang, “Actuators and Sensors for Application in Agricultural Robots: A Review,” Machines, vol. 10, no. 10, p. 913, Oct. 2022, doi: 10.3390/machines10100913.

R. Sheikh, A. Milioto, P. Lottes, C. Stachniss, M. Bennewitz, and T. Schultz, “Gradient and Log-based Active Learning for Semantic Segmentation of Crop and Weed for Agricultural Robots,” 2020 IEEE International Conference on Robotics and Automation (ICRA), pp. 1350-1356, 2020, doi: 10.1109/ICRA40945.2020.9196722.

A. Yeshmukhametov, K. Koganezawa, and Y. Yamamoto, “Design and Kinematics of Cable-Driven Continuum Robot Arm with Universal Joint Backbone,” in 2018 IEEE International Conference on Robotics and Biomimetics, ROBIO 2018, pp. 2444–2449, Jul. 2018, doi: 10.1109/ROBIO.2018.8665186.

S. Chakraborty, D. Elangovan, P. L. Govindarajan, M. F. ELnaggar, M. M. Alrashed, and S. Kamel, “A Comprehensive Review of Path Planning for Agricultural Ground Robots,” Sustainability, vol. 14, no. 15, p. 9156, Jul. 2022, doi: 10.3390/su14159156.

T. Řezník et al., “Towards the development and verification of a 3d‐based advanced optimized farm machinery trajectory algorithm,” Sensors, vol. 21, no. 9, p. 2980, Apr. 2021, doi: 10.3390/s21092980.

X. Tian Yan et al., “The AgriRover: A Reinvented Mechatronic Platform from Space Robotics for Precision Farming,” Reinventing Mechatronics, pp. 55-73, 2020, doi: 10.1007/978-3-030-29131-0_5.

L. C. Santos, A. S. Aguiar, F. N. Santos, A. Valente, and M. Petry, “Occupancy grid and topological maps extraction from satellite images for path planning in agricultural robots,” Robotics, vol. 9, no. 4, p. 77, Sep. 2020, doi: 10.3390/robotics9040077.

S. Y. and B. A. Nair Ashwin S. and Nof, “Emerging Directions of Precision Agriculture and Agricultural Robotics,” in Innovation in Agricultural Robotics for Precision Agriculture: A Roadmap for Integrating Robots in Precision Agriculture, pp. 177–210, 2021, doi: 10.1007/978-3-030-77036-5_8.

M. Xaud, A. Candea Leite, and P. From, “Thermal Image Based Navigation System for Skid-Steering Mobile Robots in Sugarcane Crops*,” in 2019 International Conference on Robotics and Automation (ICRA), pp. 1808–1814, Nov. 2019, doi: 10.1109/ICRA.2019.8794354.

L. F. P. Oliveira, A. P. Moreira, and M. F. Silva, “Advances in agriculture robotics: A state-of-the-art review and challenges ahead,” Robotics, vol. 10, no. 2. p. 52, Mar. 01, 2021, doi: 10.3390/robotics10020052.

A. Ghobadpour, G. Monsalve, A. Cardenas, and H. Mousazadeh, “Off-Road Electric Vehicles and Autonomous Robots in Agricultural Sector: Trends, Challenges, and Opportunities,” Vehicles, vol. 4, no. 3, pp. 843–864, Aug. 2022, doi: 10.3390/vehicles4030047.

S. van der Burg, M. J. Bogaardt, and S. Wolfert, “Ethics of smart farming: Current questions and directions for responsible innovation towards the future,” NJAS - Wageningen Journal of Life Sciences, vol. 90–91, Dec. 2019, doi: 10.1016/j.njas.2019.01.001.

K. Legun and K. Burch, “Robot-ready: How apple producers are assembling in anticipation of new AI robotics,” Journal of Rural Studies, vol. 82, pp. 380–390, Feb. 2021, doi: 10.1016/j.jrurstud.2021.01.032.

O. Spykman, A. Gabriel, M. Ptacek, and M. Gandorfer, “Farmers’ perspectives on field crop robots – Evidence from Bavaria, Germany,” Computers and Electronics in Agriculture, vol. 186, Jul. 2021, doi: 10.1016/j.compag.2021.106176.

G. Yan, M. Feng, W. Lin, Y. Huang, R. Tong, and Y. Cheng, “Review and Prospect for Vegetable Grafting Robot and Relevant Key Technologies,” Agriculture, vol. 12, no. 10, p. 1578, Sep. 2022, doi: 10.3390/agriculture12101578.

R. Xu and C. Li, “A Review of High-Throughput Field Phenotyping Systems: Focusing on Ground Robots,” Plant Phenomics, vol. 2022, 2022, doi: 10.34133/2022/9760269.

T. J. Erinle, D. H. Oladebeye, and I. O. Oladipo, “A Review of the Agricultural Robot as a Viable Device for Productive Mechanized Farming Engineering and Technology: Catalyst for National Survival,” in Proceedings of 13th Engineering Forum of School of Engineering, 2021.

Y. Li, Z. Guo, F. Shuang, M. Zhang, and X. Li, “Key technologies of machine vision for weeding robots: A review and benchmark,” Comput. Electron. Agric., vol. 196, May 2022, doi: 10.1016/j.compag.2022.106880.

D. N. Burrell, C. Nobles, M. Dawson, T. McDowell, and A. M. Hines, “A public policy discussion of food security and emerging food production management technologies that include drones, robots, and new technologies,” Perspectives of Innovations, Economics and Business, vol. 18, no. 2, pp. 71–87, Jul. 2018, doi: 10.15208/pieb.2018.6.

C. Charatsari, E. D. Lioutas, M. De Rosa, and A. Papadaki-Klavdianou, “Extension and advisory organizations on the road to the digitalization of animal farming: An organizational learning perspective,” Animals, vol. 10, no. 11, p. 2056, Nov. 2020, doi: 10.3390/ani10112056.

A. Botta, P. Cavallone, L. Baglieri, G. Colucci, L. Tagliavini, and G. Quaglia, “A Review of Robots, Perception, and Tasks in Precision Agriculture,” Applied Mechanics, vol. 3, no. 3, pp. 830–854, Jul. 2022, doi: 10.3390/applmech3030049.

J. P. Vasconez, G. A. Kantor, and F. A. Auat Cheein, “Human–robot interaction in agriculture: A survey and current challenges,” Biosystems Engineering, vol. 179, pp. 35–48, Mar. 2019, doi: 10.1016/j.biosystemseng.2018.12.005.

A. Patel and J. Bhalani, “A Review on in Field Cotton Recognition for Cotton Harvesting Robot based on Image Processing Technique,” International Journal of Computer Applications, vol. 180, no. 27, pp. 6–8, Mar. 2018, doi: 10.5120/ijca2018916642.

J. Vik, E. P. Stræte, B. G. Hansen, and T. Nærland, “The political robot – The structural consequences of automated milking systems (AMS) in Norway,” NJAS - Wageningen Journal of Life Sciences, vol. 90–91, Dec. 2019, doi: 10.1016/j.njas.2019.100305.

R. Nimmo, “Replacing cheap nature? Sustainability, capitalist future-making and political ecologies of robotic pollination,” Environment and Planning E: Nature and Space, vol. 5, no. 1, pp. 426–446, Mar. 2022, doi: 10.1177/2514848620987368.

C. Lytridis et al., “An overview of cooperative robotics in agriculture,” Agronomy, vol. 11, no. 9. p. 1818, Sep. 01, 2021. doi: 10.3390/agronomy11091818.

J. A. Gonzalez-Aguirre et al., “Service robots: Trends and technology,” Applied Sciences, vol. 11, no. 22. p. 10702, Nov. 01, 2021. doi: 10.3390/app112210702.

M. Ryan, S. van der Burg, and M.-J. Bogaardt, “Identifying key ethical debates for autonomous robots in agri-food: a research agenda,” AI and Ethics, vol. 2, no. 3, pp. 493–507, Aug. 2022, doi: 10.1007/s43681-021-00104-w.

K. Kleeberger, R. Bormann, W. Kraus, and M. F. Huber, “A Survey on Learning-Based Robotic Grasping,” Current Robotics Reports, vol. 1, no. 4, pp. 239–249, Dec. 2020, doi: 10.1007/s43154-020-00021-6.

L. Romeo, A. Petitti, R. Marani, and A. Milella, “Internet of robotic things in smart domains: Applications and challenges,” Sensors, vol. 20, no. 12, p. 3355, Jun. 2020, doi: 10.3390/s20123355.

T. Martin, P. Gasselin, N. Hostiou, G. Feron, L. Laurens, and F. Purseigle, “Robots and Transformations of Work on Farms: A Systematic Review,” in The 2nd International Symposium on Work in AgricultureAt: Clermont-Ferrand, 2021, doi: 10.15454/2dwm-x990.

D. Albiero, A. Pontin Garcia, C. Kiyoshi Umezu, and R. Leme de Paulo, “Swarm robots in mechanized agricultural operations: A review about challenges for research,” Comput Electron Agric, vol. 193, p. 106608, 2022, doi: 10.1016/j.compag.2021.106608.

M. Saravanamohan, D. Aswini, and G. S. Thanish, “Role of IOT in the development of Industry 4.0 and Robot technology-A State of the Art,” in 2021 International Conference on Advancements in Electrical, Electronics, Communication, Computing and Automation (ICAECA), pp. 1–6, Oct. 2021, doi: 10.1109/ICAECA52838.2021.9675634.

W. Zhang, Z. Miao, N. Li, C. He, and T. Sun, “Review of Current Robotic Approaches for Precision Weed Management,” Current Robotics Reports, vol. 3, no. 3, pp. 139–151, 2022, doi: 10.1007/s43154-022-00086-5.

L. Benos, C. G. Sørensen, and D. Bochtis, “Field Deployment of Robotic Systems for Agriculture in Light of Key Safety, Labor, Ethics and Legislation Issues,” Current Robotics Reports, vol. 3, no. 2, pp. 49–56, 2022, doi: 10.1007/s43154-022-00074-9.

T. Martin et al., “Robots and transformations of work in farm: a systematic review of the literature and a research agenda,” Agronomy for Sustainable Development, vol. 42, no. 66, 2022, doi: 10.1007/s13593-022-00796-2.

R. Sparrow and M. Howard, “Robots in agriculture: prospects, impacts, ethics, and policy,” Precision Agriculture, vol. 22, no. 3, pp. 818–833, 2021, doi: 10.1007/s11119-020-09757-9.

D. Arulkirubakaran et al., “Robotic Utilization in Farming Field—A Review,” Recent Advances in Materials and Modern Manufacturing: Select Proceedings of ICAMMM 2021, pp. 61-73, 2022.

S. Bochtis Dionysis and Moustakidis, “Mobile Robots: Current Advances and Future Perspectives,” in Innovation in Agricultural Robotics for Precision Agriculture: A Roadmap for Integrating Robots in Precision Agriculture, pp. 1–15, 2021, doi: 10.1007/978-3-030-77036-5_1.

O. P. Bodunde, U. C. Adie, O. M. Ikumapayi, J. O. Akinyoola, and A. A. Aderoba, “Architectural design and performance evaluation of a ZigBee technology based adaptive sprinkler irrigation robot,” Computers and Electronics in Agriculture, vol. 160, pp. 168–178, May 2019, doi: 10.1016/j.compag.2019.03.021.

P. O. Dusadeerungsikul and S. Y. Nof, “A collaborative control protocol for agricultural robot routing with online adaptation,” Computers & Industrial Engineering, vol. 135, pp. 456–466, Sep. 2019, doi: 10.1016/j.cie.2019.06.037.

J. Spranger, R. Buzatoiu, A. Polydoros, L. Nalpantidis, and E. Boukas, “Human-Machine Interface for Remote Training of Robot Tasks,” 2018 IEEE International Conference on Imaging Systems and Techniques (IST), pp. 1-5, 2018, doi: 10.1109/IST.2018.8577081.

A. Khan, S. Aziz, M. Bashir, and M. U. Khan, “IoT and Wireless Sensor Network based Autonomous Farming Robot,” 2020 International Conference on Emerging Trends in Smart Technologies (ICETST), pp. 1-5, 2020, doi: 10.1109/ICETST49965.2020.9080736.

A. Hassan et al., “A wirelessly controlled robot-based smart irrigation system by exploiting arduino,” Journal of Robotics and Control (JRC), vol. 2, no. 1, pp. 29–34, Jan. 2021, doi: 10.18196/jrc.2148.

C. R. S. Ram et al., “Internet of Green Things with autonomous wireless wheel robots against green houses and farms,” International Journal of Distributed Sensor Networks, vol. 16, no. 6, Jun. 2020, doi: 10.1177/1550147720923477.




DOI: https://doi.org/10.18196/jrc.v4i3.18368

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