Robots in Agriculture: Revolutionizing Farming Practices

Ata Jahangir  Moshayedi; Amir Sohail Khan; Yiguo Yang; Jiandong Hu; Amin Kolahdooz

doi:10.4108/airo.5855

Authors

Ata Jahangir Moshayedi
Amir Sohail Khan
Yiguo Yang
Jiandong Hu
Amin Kolahdooz

DOI:

https://doi.org/10.4108/airo.5855

Keywords:

Agricultural robots, Robotic Structures, Precision Farming, Legged Robot

Abstract

The integration of robotics in modern agriculture represents a revolutionary paradigm shift, enhancing efficiency and sustainability in food production. Agricultural robots, designed to automate various tasks, play a pivotal role in addressing the challenges faced by the industry. These robots are purpose-built for activities such as precision planting, weeding, and harvesting, streamlining processes that were traditionally labor-intensive. Their implementation leads to increased productivity, reduced operational costs, and minimized environmental impact through optimized resource utilization. This paper delves into the intricate landscape of robotic structures employed in agriculture, unraveling the diverse mechanisms and designs that underpin their functionality. It meticulously examines and elucidates the structural nuances of agricultural robots, shedding light on the engineering marvels that enable precision farming. From articulated arms to autonomous drones, the paper navigates through a spectrum of robot architectures, dissecting their roles in automating tasks critical to modern agriculture.

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References

Lagnelöv, O., Dhillon, S., Larsson, G., Nilsson, D., Larsolle, A., & Hansson, P. A. (2021). Cost analysis of autonomous battery electric field tractors in agriculture. biosystems engineering, 204, 358-376.

Del Cerro, J., Cruz Ulloa, C., Barrientos, A., & de León Rivas, J. (2021). Unmanned aerial vehicles in agriculture: A survey. Agronomy, 11(2), 203.

Oliveira, L. F., Moreira, A. P., & Silva, M. F. (2021). Advances in agriculture robotics: A state-of-the-art review and challenges ahead. Robotics, 10(2), 52.

Tudor, E., Matache, M. G., Vasile, I., Sburlan, I. C., & Stefan, V. (2022). Automation and Remote Control of an Aquatic Harvester Electric Vehicle. Sustainability, 14(10), 6360.

Sakai, S., Iida, M., Osuka, K., & Umeda, M. (2008). Design and control of a heavy material handling manipulator for agricultural robots. Autonomous Robots, 25, 189-204.

Ball, D., Upcroft, B., Wyeth, G., Corke, P., English, A., Ross, P., & Bate, A. (2016). Vision-based obstacle detection and navigation for an agricultural robot. Journal of field robotics, 33(8), 1107-1130.

Huang, C. C., & Chang, C. L. (2019). Design and implementation of bio-inspired snake bone-armed robot for agricultural irrigation application. IFAC-PapersOnLine, 52(30), 98-101.

Chebrolu, N., Lottes, P., Läbe, T., & Stachniss, C. (2019, May). Robot localization based on aerial images for precision agriculture tasks in crop fields. In 2019 International Conference on Robotics and Automation (ICRA) (pp. 1787-1793). IEEE.

Mitra, M. (2019). Robotic farmers in agriculture. Advances in Robotics & Mechanical Engineering, (5), 91-93.

Al-Mashhadani, Z., & Chandrasekaran, B. (2020, November). Survey of Agricultural Robot Applications and Implementation. In 2020 11th IEEE Annual Information Technology, Electronics and Mobile Communication Conference (IEMCON) (pp. 0076-0081). IEEE.

Fountas, S., Mylonas, N., Malounas, I., Rodias, E., Hellmann Santos, C., & Pekkeriet, E. (2020). Agricultural robotics for field operations. Sensors, 20(9), 2672.

Mahmud, M. S. A., Abidin, M. S. Z., Emmanuel, A. A., & Hasan, H. S. (2020). Robotics and automation in agriculture: present and future applications. Applications of Modelling and Simulation, 4, 130-140.

Skoczeń, M., Ochman, M., Spyra, K., Nikodem, M., Krata, D., Panek, M., & Pawłowski, A. (2021). Obstacle detection system for agricultural mobile robot application using RGB-D cameras. Sensors, 21(16), 5292.

Han, J., Liu, L., & Zeng, H. (2021). Design and implementation of intelligent agricultural picking mobile robot based on color sensor. In Journal of Physics: Conference Series (Vol. 1757, No. 1, p. 012157). IOP Publishing.

Chand, A. A., Prasad, K. A., Mar, E., Dakai, S., Mamun, K. A., Islam, F. R., & Kumar, N. M. (2021). Design and analysis of photovoltaic powered battery-operated computer vision-based multi-purpose smart farming 19 EAI Endorsed Transactions Preprint Ata Jahangir Moshayedi robot. Agronomy, 11(3), 530.

Mohammed, I. I., & Jassim, A. R. A. L. (2021). Design and Testing of an Agricultural Robot to Operate a Combined Seeding Machine. Annals of the Romanian Society for Cell Biology, 25(7), 92-106.

Kondoyanni, M., Loukatos, D., Maraveas, C., Drosos, C., & Arvanitis, K. G. (2022). Bio-inspired robots and structures toward fostering the modernization of agriculture. Biomimetics, 7(2), 69.

Moraitis, M., Vaiopoulos, K., & Balafoutis, A. T. (2022). Design and implementation of an urban farming robot. Micromachines, 13(2), 250.

Orkweha, P. (2022). Trajectory Control Design for a Custom-Built Agricultural Robot Considering Slippage Effects (Doctoral dissertation, Southern Illinois University at Edwardsville).

Ghafar, A. S. A., Hajjaj, S. S. H., Gsangaya, K. R., Sultan, M. T. H., Mail, M. F., & Hua, L. S. (2023). Design and development of a robot for spraying fertilizers and pesticides for agriculture. Materials Today: Proceedings, 81, 242-248.

Yang, Q., Du, X., Wang, Z., Meng, Z., Ma, Z., & Zhang, Q. (2023). A review of core agricultural robot technologies for crop productions. Computers and Electronics in Agriculture, 206, 107701.

Ghobadpour, A., Cardenas, A., Monsalve, G., & Mousazadeh, H. (2023). Optimal Design of Energy Sources for a Photovoltaic/Fuel Cell Extended-Range Agricultural Mobile Robot. Robotics, 12(1), 1

Azmi, H. N., Hajjaj, S. S. H., Gsangaya, K. R., Sultan, M. T. H., Mail, M. F., & Hua, L. S. (2023). Design and fabrication of an agricultural robot for crop seeding. Materials Today: Proceedings, 81, 283-289.

Li, Y., Fu, C., Yang, H., Li, H., Zhang, R., Zhang, Y., & Wang, Z. (2023). Design of a Closed Piggery Environmental Monitoring and Control System Based on a Track Inspection Robot. Agriculture, 13(8), 1501.

Duckett, T., Pearson, S., Blackmore, S., Grieve, B., Chen, W. H., Cielniak, G., ... & Yang, G. Z. (2018). Agricultural robotics: the future of robotic agriculture. arXiv preprint arXiv:1806.06762.

Vineet, S., Deshmukh, D., Pratihar, D. K., Deb, A. K., Ray, H., & Bhattacharyya, N. (2021, December). Dynamic Analysis of Tracked Mobile Manipulator Used in Agriculture. In 2021 IEEE 18th India Council International Conference (INDICON) (pp. 1-6). IEEE.

Gao, X., Li, J., Fan, L., Zhou, Q., Yin, K., Wang, J., ... & Wang, Z. (2018). Review of wheeled mobile robots’ navigation problems and application prospects in agriculture. Ieee Access, 6, 49248-49268.

Bellicoso, C. D., Bjelonic, M., Wellhausen, L., Holtmann, K., Günther, F., Tranzatto, M., ... & Hutter, M. (2018). Advances in real-world applications for legged robots. Journal of Field Robotics, 35(8), 1311-1326.

Degieter, M., De Steur, H., Tran, D., Gellynck, X., & Schouteten, J. J. (2023). Farmers’ acceptance of robotics and unmanned aerial vehicles: A systematic review. Agronomy Journal, 115(5), 2159-2173.

Zarei, M., Moshayedi, A. J., Zhong, Y., Khan, A. S., Kolahdooz, A., & Andani, M. E. (2023, January). Indoor UAV object detection algorithms on three processors: implementation test and comparison. In 2023 3rd International Conference on Consumer Electronics and

Computer Engineering (ICCECE) (pp. 812-819). IEEE.

Moshayedi, A. J., Reza, K. S., Khan, A. S., & Nawaz, A. (2023). Integrating virtual reality and robotic operation system (ROS) for AGV navigation. EAI Endorsed Transactions on AI and Robotics, 2.

Xie, D., Chen, L., Liu, L., Chen, L., & Wang, H. (2022). Actuators and sensors for application in agricultural robots: A review. Machines, 10(10), 913.

Khan, N., Medlock, G., Graves, S., & Anwar, S. (2018). GPS guided autonomous navigation of a small agricultural robot with automated fertilizing system (No. 2018-01-0031). SAE Technical Paper.

Radočaj, D., Plaščak, I., & Jurišić, M. (2023). Global navigation satellite systems as state-of-the-art solutions in precision agriculture: A review of studies indexed in the web of science. Agriculture, 13(7), 1417.

Hiraoka, R., Aoyagi, Y., & Kobayashi, K. (2023). Automatic travelling of agricultural support robot for a fruit farm. Verification of effectiveness of real-time kinematic-global navigation satellite system and developed a simulator for specification design. Journal of

Agricultural Engineering, 54(1).

Moshayedi, A. J., Kazemi, E., Tabatabaei, M., & Liao, L. (2020). Brief modeling equation for metal-oxide; TGS type gas sensors. Filomat, 34(15), 4997-5008.

Senthil, P., & Akila, I. S. (2018, August). Automated robotic moisture monitoring in agricultural fields. In 2018 International Seminar on Intelligent Technology and Its Applications (ISITIA) (pp. 375-380). IEEE.

Shivaprasad, B. S., Ravishankara, M. N., & Shoba, B. N. (2014). Design and implementation of seeding and fertilizing agriculture robot. International Journal of Application or Innovation in Engineering & Management (IJAIEM), 3(6), 251-255.

Kitić, G., Krklješ, D., Panić, M., Petes, C., Birgermajer, S., & Crnojević, V. (2022). Agrobot Lala—An Autonomous Robotic System for Real-Time, In-Field Soil Sampling, and Analysis of Nitrates. Sensors, 22(11), 4207.

Rakhra, M., Singh, A., Fadhaeel, T., Al Ahdal, A., & Pandey, N. (2022, October). Design, Fabrication, and Implementation of an Agriculture robot. In 2022 10th International Conference on Reliability, Infocom Technologies and Optimization (Trends and Future

Directions) (ICRITO) (pp. 1-6). IEEE.

Fentanes, J. P., Gould, I., Duckett, T., Pearson, S., & Cielniak, G. (2018). 3-d soil compaction mapping through kriging-based exploration with a mobile robot. IEEE Robotics and Automation Letters, 3(4), 3066-3072.

Pobkrut, T., & Kerdcharoen, T. (2014, October). Soil sensing survey robots based on electronic nose. In 2014 14th International Conference on Control, Automation and Systems (ICCAS 2014) (pp. 1604-1609). IEEE.

Xing, Y., Vincent, T. A., Cole, M., & Gardner, J. W. (2019). Real-time thermal modulation of high bandwidth MOX gas sensors for mobile robot applications. Sensors, 19(5), 1180.

Moshayedi, A. J., Khan, A. S., Shuxin, Y., Kuan, G., Jiandong, H., Soleimani, M., & Razi, A. (2023). E-Nose design and structures from statistical analysis. Endorsed Transactions on AI and Robotics, 2(1), e1-e1.

Moshayedi, A. J., Sohail Khan, A., Hu, J., Nawaz, A., & Zhu, J. (2023). E-nose-driven advancements in ammonia gas detection: a comprehensive review from traditional to cutting-edge systems in indoor to outdoor agriculture. Sustainability, 15(15), 11601.

Arroyo, P., Gómez-Suárez, J., Herrero, J. L., & Lozano, J. (2022). Electrochemical gas sensing module combined with unmanned aerial vehicles for air quality monitoring. Sensors and Actuators B: Chemical, 364, 131815.

Fumian, F., Di Giovanni, D., Martellucci, L., Rossi, R., & Gaudio, P. (2020). Application of miniaturized sensors to unmanned aerial systems, a new pathway for the survey of polluted areas: Preliminary results. Atmosphere, 11(5), 471.

Al-Mashhadani, Z., & Chandrasekaran, B. (2020, November). Survey of Agricultural Robot Applications and Implementation. In 2020 11th IEEE Annual Information Technology, Electronics and Mobile Communication Conference (IEMCON) (pp. 0076-0081). IEEE.

Moshayedi, A. J., Roy, A. S., Liao, L., Khan, A. S., Kolahdooz, A., & Eftekhari, A. (2024). Design and Development of Foodiebot Robot: from Simulation to Design. IEEE Access.

Tinoco, V., Silva, M. F., Santos, F. N., Valente, A., Rocha, L. F., Magalhães, S. A., & Santos, L. C. (2022). An overview of pruning and harvesting manipulators. Industrial Robot: the international journal of robotics research and application, 49(4), 688-695.

Bechar, A., & Vigneault, C. (2017). Agricultural robots for field operations. Part 2: Operations and systems. Biosystems engineering, 153, 110-128.

Bawden, O., Kulk, J., Russell, R., McCool, C., English, A., Dayoub, F., ... & Perez, T. (2017). Robot for weed species plant-specific management. Journal of Field Robotics, 34(6), 1179-1199.

Moshayedi, A. J., Roy, A. S., Liao, L., & Li, S. (2019, December). Raspberry Pi SCADA zonal based system for agricultural plant monitoring. In 2019 6th International Conference on Information Science and Control Engineering (ICISCE) (pp. 427-433). IEEE.

Vyas, S. D., Subash, V. K., Mepani, M., Venkatesh, S., Sinha, A., Guha, A., & Dey, S. (2024). Energy-and Safety-Aware Operation of Battery-Powered Autonomous Robots in Agriculture. IEEE Transactions on AgriFood Electronics

Gonzalez-de-Soto, M., Emmi, L., Benavides, C., Garcia, I., & Gonzalez-de-Santos, P. (2016). Reducing air pollution with hybrid-powered robotic tractors for precision agriculture. Biosystems Engineering, 143, 79-94.

Zahedi, A., Shafei, A. M., & Shamsi, M. (2023). Application of hybrid robotic systems in crop harvesting: kinematic and dynamic analysis. Computers and Electronics in Agriculture, 209, 107724.

Farooq, M. U., Eizad, A., & Bae, H. K. (2023). Power solutions for autonomous mobile robots: A survey. Robotics and Autonomous Systems, 159, 104285.

Ünal, İ., Kabaş, Ö., Eceoğlu, O., & Moiceanu, G. (2023). Adaptive Multi-Robot Communication System and Collision Avoidance Algorithm for Precision Agriculture. Applied Sciences, 13(15), 8602.

Xie, B., Jin, Y., Faheem, M., Gao, W., Liu, J., Jiang, H., ... & Li, Y. (2023). Research progress of autonomous navigation

technology for multi-agricultural scenes. Computers and Electronics in Agriculture, 211, 107963.

Shalal, N., Low, T., McCarthy, C., & Hancock, N. (2013). A review of autonomous navigation systems in agricultural environments. SEAg 2013: Innovative agricultural technologies for a sustainable future.

Hameed, I. A. (2014). Intelligent coverage path planning for agricultural robots and autonomous machines on three-dimensional terrain. Journal of Intelligent & Robotic Systems, 74(3), 965-983.

Pingzeng, L., Shusheng, B., Guansheng, Z., Wenshan, W., Yushu, G., & Zhenmin, D. (2011, December). Obstacle avoidance system for agricultural robots based on multi-sensor information fusion. In Proceedings of 2011 International Conference on Computer Science and

Network Technology (Vol. 2, pp. 1181-1185). IEEE.

Tsiakas, K., Papadimitriou, A., Pechlivani, E. M., Giakoumis, D., Frangakis, N., Gasteratos, A., & Tzovaras, D. (2023). An Autonomous Navigation Framework for Holonomic Mobile Robots in Confined Agricultural Environments. Robotics, 12(6), 146.

Stronzek-Pfeifer, D., Chemmadan, F. M., Patel, S., & Büskens, C. (2023). LiDAR-based object detection for agricultural robots. PAMM, 23(3), e202300128.

Heidari, A., & Parian, J. A. (2021). Greenhouse Mobile robot navigation using wheel revolution encoding and learning algorithm. Journal of Agricultural Machinery, 11(1), 1-15.

Han, C., Wu, W., Luo, X., & Li, J. (2023). Visual Navigation and Obstacle Avoidance Control for Agricultural Robots via LiDAR and Camera. Remote Sensing, 15(22), 5402.

Ding, H., Zhang, B., Zhou, J., Yan, Y., Tian, G., & Gu, B. (2022). Recent developments and applications of simultaneous localization and mapping in agriculture. Journal of Field Robotics, 39(6), 956-983.

Bini, D., Pamela, D., & Prince, S. (2020, March). Machine vision and machine learning for intelligent agrobots: A review. In 2020 5th International conference on devices, circuits and systems (ICDCS) (pp. 12-16). IEEE.

Gonzalez-de-Santos, P., Fernández, R., Sepúlveda, D., Navas, E., Emmi, L., & Armada, M. (2020). Field robots for intelligent farms—Inhering features from industry. Agronomy, 10(11), 1638.

Nikander, J., Manninen, O., & Laajalahti, M. (2020). Requirements for cybersecurity in agricultural communication networks. Computers and electronics in agriculture, 179, 105776.

Adamides, G., & Edan, Y. (2023). Human–robot collaboration systems in agricultural tasks: A review and roadmap. Computers and Electronics in Agriculture, 204, 107541

Redhead, F., Snow, S., Vyas, D., Bawden, O., Russell, R., Perez, T., & Brereton, M. (2015, April). Bringing the farmer perspective to agricultural robots. In Proceedings of the 33rd Annual ACM Conference Extended Abstracts on Human Factors in Computing Systems (pp. 1067-1072).

Mahadhir, K. A., Tan, S. C., Low, C. Y., Dumitrescu, R., Amin, A. T. M., & Jaffar, A. (2014). Terrain classification for track-driven agricultural robots. Procedia Technology, 15, 775-782

Xu, G., Khan, A. S., Moshayedi, A. J., Zhang, X., & Shuxin, Y. (2022). The object detection, perspective and obstacles in robotic: a review. EAI Endorsed Transactions on AI and Robotics, 1(1).

Botta, A., Cavallone, P., Baglieri, L., Colucci, G., Tagliavini, L., & Quaglia, G. (2022). A review of robots, perception, and tasks in precision agriculture. Applied Mechanics, 3(3), 830-854.

Chang, C. L., Chen, H. W., & Ke, J. Y. (2023). Robust Guidance and Selective Spraying Based on Deep Learning for an Advanced Four-Wheeled Farming Robot. Agriculture, 14(1), 57.

Gravalos, I., Avgousti, A., Gialamas, T., Alfieris, N., & Paschalidis, G. (2019). A robotic irrigation system for urban gardening and agriculture. Journal of agricultural engineering, 50(4), 198-207.

Krishna, K. L., Silver, O., Malende, W. F., & Anuradha, K. (2017, February). Internet of Things application for implementation of smart agriculture system. In 2017 International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud)(I-SMAC) (pp. 54-59). IEEE.

Marinoudi, V., Lampridi, M., Kateris, D., Pearson, S., Sørensen, C. G., & Bochtis, D. (2021). The future of agricultural jobs in view of robotization. Sustainability, 13(21), 12109.

Gupta, N., & Gupta, P. K. (2024). Robotics and Artificial Intelligence (AI) in Agriculture with Major Emphasis on Food Crops. Digital Agriculture: A Solution for Sustainable Food and Nutritional Security, 577-605.

Meshram, A. T., Vanalkar, A. V., Kalambe, K. B., & Badar, A. M. (2022). Pesticide spraying robot for precision agriculture: A categorical literature review and future trends. Journal of Field Robotics, 39(2), 153-171.

Halachmi, I., Guarino, M., Bewley, J., & Pastell, M. (2019). Smart animal agriculture: application of real-time sensors to improve animal well-being and production. Annual review of animal biosciences, 7, 403-425.

Łukowska, A., Tomaszuk, P., Dzierżek, K., & Magnuszewski, Ł. (2019, May). Soil sampling mobile platform for Agriculture 4.0. In 2019 20th International Carpathian Control Conference (ICCC) (pp. 1-4). IEEE.

Wang, C., Ding, F., Ling, L., & Li, S. (2023). Design of a teat cup attachment robot for automatic milking systems. Agriculture, 13(6), 1273.

Dewi, T., Risma, P., & Oktarina, Y. (2020). Fruit sorting robot based on color and size for an agricultural product packaging system. Bulletin of Electrical Engineering and Informatics, 9(4), 1438-1445.

Santos, A. S. D., Dalzot, J. C. T., Pereira, G. A., Cunha, J. G. D., Evangelista, T. Y. L., Fonseca, W. L., ... & Abd Elgawad, H. (2023). Pruning and fruit thinning of Psidium guajava cv. Paluma under a seasonal tropical climate. Agriculture, 13(8), 1537.

Roldán, J. J., del Cerro, J., Garzón-Ramos, D., Garcia-Aunon, P., Garzón, M., De León, J., & Barrientos, A. (2018). Robots in agriculture: State of art and practical experiences. Service robots, 67-90.

McAllister, W., Osipychev, D., Davis, A., & Chowdhary, G. (2019). Agbots: Weeding a field with a team of autonomous robots. Computers and Electronics in Agriculture, 163, 104827.

Bilal, M., Rubab, F., Hussain, M., & Shah, S. A. R. (2023, November). Agriculture Revolutionized by Artificial Intelligence: Harvesting the Future. In Biology and Life Sciences Forum (Vol. 30, No. 1, p. 11). MDPI.

Aravindaguru, I., Mathan, C., Sharmila, B., Nagarajapandian, M., & Veeramani, P. (2023). Environmental Drones for Autonomous Air Pollution Investigation, Detection, and Remediation. In Drone Data Analytics in Aerial Computing (pp. 107-118). Singapore: Springer Nature Singapore.

Bale, A. S., Varsha, S. N., Naidu, A. S., Vinay, N., & Tiwari, S. (2024). Autonomous Aerial Robots Application for Crop Survey and Mapping. In Precision Agriculture for Sustainability (pp. 123-145). Apple Academic Press.

Tomar, P., & Krishnan, S. (2022, August). Development of a Drone with Spraying Mechanism for Agricultural Work. In Biennial International Conference on Future Learning Aspects of Mechanical Engineering (pp. 295-308). Singapore: Springer Nature Singapore.

Malo, M. (2020). Artificial intelligence in agriculture: strengthening the future of farming. AGRICULTURE & FOOD e-NEWSLETTER, 71.

Panwar, K., Prasad, S., & Semwal, A. (2022). Different Approaches of Robotic Spraying Drones for Agricultural Activities. resmilitaris, 12(5), 1377-1384.

Escandón-Panchana, P., Herrera-Franco, G., Martínez Cuevas, S., & Morante-Carballo, F. (2023, October). Prospects of UAVs in Agricultural Mapping. In International Conference on Applied Informatics (pp. 309-322). Cham: Springer Nature Switzerland.

Chinnasamy, S., Ramachandran, M., & Sriram, S. (2022). An Detailed Study on Unmanned Aerial Vehicle and Its Surveillance‖. Environmental Science and Engineering, 1(1), 41-47.

Shvets, Y., Morkovkin, D., Basova, M., Yashchenko, A., & Petrusevich, T. (2024). Robotics in agriculture: Advanced technologies in livestock farming and crop cultivation. In E3S Web of Conferences (Vol. 480, p. 03024). EDP Sciences.

Basri, R., Islam, F., Shorif, S. B., & Uddin, M. S. (2021). Robots and drones in agriculture—a survey. Computer vision and machine learning in agriculture, 9-29.

Hutsol, T., Kutyrev, A., Kiktev, N., & Biliuk, M. (2023). Robotic technologies in horticulture: analysis and implementation prospects. Agricultural Engineering, 27(1), 113-133.

Waked, J., Sara, G., Todde, G., Pinna, D., Hassoun, G., & Caria, M. (2023, October). Analysis of Factors Affecting Farmers’ Intention to Use Autonomous Ground Vehicles. In International Congress on Agricultural Mechanization and Energy in Agriculture (pp. 423-440). Cham: Springer Nature Switzerland.

Han, J. H., Park, C. H., Kwon, J. H., Lee, J., Kim, T. S., & Jang, Y. Y. (2020). Performance evaluation of autonomous driving control algorithm for a crawler-type agricultural vehicle based on low-cost multi-sensor fusion positioning. Applied Sciences, 10(13), 4667.

Moshayedi, A. J., Li, J., & Liao, L. (2021, June). Simulation study and PID tune of automated guided vehicles (AGV). In 2021 IEEE International Conference on Computational Intelligence and Virtual Environments for Measurement Systems and Applications (CIVEMSA) (pp.1-7). IEEE.

Bonadies, S., Lefcourt, A., & Gadsden, S. A. (2016, May). A survey of unmanned ground vehicles with applications to agricultural and environmental sensing. In Autonomous air and ground sensing systems for agricultural optimization and phenotyping (Vol. 9866, pp. 142-155). SPIE.

Jensen, K., Larsen, M., Nielsen, S. H., Larsen, L. B., Olsen, K. S., & Jørgensen, R. N. (2014). Towards an open software platform for field robots in precision agriculture. Robotics, 3(2), 207-234.

Albiero, D., Garcia, A. P., Umezu, C. K., & de Paulo, R. L. (2022). Swarm robots in mechanized agricultural operations: A review about challenges for research. Computers and Electronics in Agriculture, 193, 106608.

Xu, R., & Li, C. (2022). A modular agricultural robotic system (MARS) for precision farming: Concept and implementation. Journal of Field Robotics, 39(4), 387-409.

Tangarife, H. I., & Díaz, A. E. (2017, October). Robotic applications in the automation of agricultural production under greenhouse: A review. In 2017 IEEE 3rd Colombian Conference on Automatic Control (CCAC) (pp. 1-6). IEEE.

Tzachor, A., Devare, M., King, B., Avin, S., & Ó hÉigeartaigh, S. (2022). Responsible artificial intelligence in agriculture requires systemic understanding of risks and externalities. Nature Machine Intelligence, 4(2), 104-109.

Küçüktopcu, E., & Cemek, B. (2021). Comparison of neuro-fuzzy and neural networks techniques for estimating ammonia concentration in poultry farms. Journal of Environmental Chemical Engineering, 9(4), 105699.

Achmad, A., & Syarif, S. (2021, July). Prediction of Ammonia Contamination Levels in Wastewater Management Plant Using the SVM Method. In 2021 International Seminar on Intelligent Technology and Its Applications (ISI-TIA) (pp. 250-255). IEEE.

Geng, K., Ata, J. M., Chen, J., Hu, J., & Zhang, H. (2023, March). ENOSE Performance in Transient Time and Steady State Area of Gas Sensor Response for Ammonia Gas: Comparison and Study. In Proceedings of the 2023 2nd Asia Conference on Algorithms, Computing and Machine Learning (pp. 247-252).

Gil, G., Casagrande, D. E., Cortés, L. P., & Verschae, R. (2023). Why the low adoption of robotics in the farms? Challenges for the establishment of commercial agricultural robots. Smart Agricultural Technology, 3, 100069.

Yang, Q., Du, X., Wang, Z., Meng, Z., Ma, Z., & Zhang, Q. (2023). A review of core agricultural robot technologies for crop productions. Computers and Electronics in Agriculture, 206, 107701.

Mohan, S. S., Venkat, R., Rahaman, S., Vinayak, M., & Babu, B. H. (2023). Role of AI in agriculture: applications, limitations and challenges: A review. Agricultural Reviews, 44(2), 231-237.

Bazargani, K., & Deemyad, T. (2024). Automation’s Impact on Agriculture: Opportunities, Challenges, and Economic Effects. Robotics, 13(2), 33