AGRICULTURAL ROBOTICS: AUTOMATING THE FUTURE OF FARMING

Authors

  • Alpa Yadav
  • Deepak Kholiya
  • Manjeet
  • Ramakrushna Bastia
  • Bhavana
  • Jayanti Ballabh
  • Basant Sah
  • Priyanka Bankoti

DOI:

https://doi.org/10.52783/jns.v14.3976

Keywords:

Agricultural Automation, Agricultural Robotics, Artificial Intelligence, Autonomous Farming, Computer Vision, Machine Learning, Precision Agriculture, Robotics in Farming, Sensor Technology, Smart Agriculture, Unmanned Aerial Vehicles, Yield Optimization

Abstract

The agricultural industry faces increasing pressure to enhance productivity, reduce labour costs, and adopt sustainable practices to meet the demands of a growing global population (Sarah Moore, 2022). Agricultural robotics has emerged as a transformative solution, offering the potential to automate various farming tasks, improve efficiency, and minimize environmental impact (Sarah Moore, 2022). This essay explores the current state of agricultural robotics, highlighting key applications such as harvesting, weed control, planting, and crop monitoring (Agri Guide, 2024). It examines the benefits of using robots in farming, including increased efficiency, reduced labour costs, improved precision, and enhanced sustainability (Agri Guide, 2024). The essay also addresses the challenges associated with the adoption of agricultural robotics, such as high initial costs, technical complexity, and potential job displacement (Agri Guide, 2024). Finally, it discusses future trends in the field, including the integration of artificial intelligence, the development of multi-functional robots, and the increasing use of data-driven decision-making (Agri Guide, 2024). Ultimately, the essay argues that agricultural robotics holds immense promise for automating the future of farming, ensuring food security, and promoting sustainable agricultural practices (Sarah Moore, 2022).

Downloads

Download data is not yet available.

Metrics

Metrics Loading ...

References

. Oliveira, G. A., Sousa, A. J., & Moreira, A. P. (2021). Advances in agriculture robotics: A state-of-the-art review and challenges ahead. Robotics, 10(2), 52. https://doi.org/10.3390/robotics10020052MDPI

. Fue, K. G., Porter, W. M., & Barnes, E. M. (2020). An extensive review of mobile agricultural robotics for field operations: Focus on cotton harvesting. Agriculture, 2(1), 10. https://doi.org/10.3390/agriculture2010010MDPI+1MDPI+1

. Kim, S., Kim, D., & Kim, H. (2019). Unmanned aerial vehicles in agriculture: A review of perspective of platform, control, and applications. Agriculture, 9(10), 349. https://doi.org/10.3390/agriculture9100349

. Xiong, Y., Ge, Y., Grimstad, L., & From, P. J. (2020). An autonomous strawberry-harvesting robot: Design, development, integration, and field evaluation. Journal of Field Robotics, 37(2), 202–224. https://doi.org/10.1002/rob.21922MDPI

. Lehnert, C., English, A., McCool, C., Tow, A. W., Perez, T., & Upcroft, B. (2020). Autonomous sweet pepper harvesting for protected cropping systems. IEEE Robotics and Automation Letters, 5(2), 1127–1134. https://doi.org/10.1109/LRA.2020.2964160

. Sepúlveda, D., Cheein, F. A., & Billingsley, J. (2020). Dual-arm manipulation for robotic harvesting: A realistic simulation approach for robotic aubergine harvesting. Computers and Electronics in Agriculture, 169, 105181. https://doi.org/10.1016/j.compag.2019.105181

. Kang, H., Chen, C., & Noguchi, N. (2020). Visual perception and modeling for autonomous apple harvesting. Journal of Field Robotics, 37(2), 322–337. https://doi.org/10.1002/rob.21930

. Yu, Y., Zhang, K., Yang, L., & Zhang, D. (2020). Fruit detection for strawberry harvesting robot in non-structural environment based on Mask-RCNN. Computers and Electronics in Agriculture, 176, 105652. https://doi.org/10.1016/j.compag.2020.105652

. Leu, S. Y., Chen, C. Y., & Tsai, Y. C. (2017). Robotic green asparagus selective harvesting using multi-spectral imaging and robotic arm. Computers and Electronics in Agriculture, 142, 536–545. https://doi.org/10.1016/j.compag.2017.10.002

. Ge, Y., Xiong, Y., & From, P. J. (2019). Fruit localization and environment perception for strawberry harvesting robots. IEEE Access, 7, 147642–147652. https://doi.org/10.1109/ACCESS.2019.2946351

. Tsouros, D. C., Bibi, S., & Sarigiannidis, P. G. (2019). A review on UAV-based applications for precision agriculture. Information, 10(11), 349. https://doi.org/10.3390/info10110349MDPI+2MDPI+2MDPI+2

. Raparelli, E., & Bajocco, S. (2019). A bibliometric analysis on the use of unmanned aerial vehicles in agricultural and forestry studies. International Journal of Remote Sensing, 40(24), 9070–9083. https://doi.org/10.1080/01431161.2019.1569793

. Norasma, C. Y., Shamsuddin, J., & Hashim, S. Z. M. (2019). Applications of unmanned aerial vehicle in precision agriculture: A review. IOP Conference Series: Materials Science and Engineering, 506, 012063. https://doi.org/10.1088/1757-899X/506/1/012063

. Hou, G., Chen, H., Jiang, M., & Niu, R. (2023). An overview of the application of machine vision in recognition and localization of fruit and vegetable harvesting robots. Agriculture, 13(9), 1814. https://doi.org/10.3390/agriculture13091814MDPI

. Kurpaska, S., Slipek, Z., & Zaborowicz, M. (2023). A review of perception technologies for berry fruit-picking robots. Agriculture, 14(8), 1346. https://doi.org/10.3390/agriculture14081346

Downloads

Published

2025-04-17

How to Cite

1.
Yadav A, Kholiya D, Manjeet M, Bastia R, Bhavana B, Ballabh J, Sah B, Bankoti P. AGRICULTURAL ROBOTICS: AUTOMATING THE FUTURE OF FARMING. J Neonatal Surg [Internet]. 2025Apr.17 [cited 2025Sep.19];14(6):538-45. Available from: https://www.jneonatalsurg.com/index.php/jns/article/view/3976

Most read articles by the same author(s)