Login / Register Submit Your Article
cover
Journal of Arts, Society, and Education Studies

Volume 4, 2022 - Issue 2

Submit Manuscript Journal Homepage
Latest Issue
Input keywords, title, abstract, author, affiliation etc..
Article Menu
Browse this journal
Vol. 7 (2025)
Vol. 6 (2024)
Vol. 5 (2023)
Vol. 4 (2022)
Vol. 3 (2021)
Vol. 2 (2020)
Vol. 1 (2019)
Recommend Papers
Exploring the Transformative Power of Arts-Based Research in Education: A Journey of Inquiry, Reflection, and Social Change
Art and Diplomacy: Building Bridges Across Cultures and Fostering Global Understanding
The Power of Public Art: Transforming Urban Spaces and Inspiring Social Change
Artistic Interventions in Conflict Zones: Healing Trauma and Building Peace Through Creative Expression
Exploring the Intersection of Art, Technology, and Social Impact: Innovations for a Sustainable Future
Journal Article An open access journal
Journal Article

AI in Agriculture: Revolutionizing Farming Practices and Crop Management

by Mary Rodriguez 1,*
1
Norwegian University of Science and Technology
*
Author to whom correspondence should be addressed.
JASES  2022 4(2):103; https://doi.org/10.xxxx/xxxxxx
Received: 28 April 2022 / Accepted: 27 May 2022 / Published Online: 29 June 2022
View Full-Text
Download PDF

Abstract

This paper explores the transformative impact of artificial intelligence (AI) in agriculture, focusing on how AI technologies are revolutionizing farming practices and crop management. Through an analysis of case studies and industry reports, the study investigates how AI-driven solutions such as precision farming, crop monitoring, and yield prediction are reshaping various aspects of agricultural operations, including planting, irrigation, pest control, and harvesting. It discusses the potential benefits of AI in improving agricultural productivity, optimizing resource utilization, and reducing environmental impact, while also addressing challenges related to data accessibility, technology adoption, and rural infrastructure. Additionally, the paper examines the role of AI in enabling more sustainable and resilient agricultural systems, by providing real-time insights into soil health, weather patterns, and crop disease detection. Furthermore, it discusses the importance of collaboration between farmers, researchers, and technology providers, investment in AI infrastructure, and policy support in harnessing the full potential of AI in agriculture. The findings underscore the transformative power of AI in creating more efficient, sustainable, and food-secure agricultural systems to meet the growing demands of a changing world.

Keywords: Artificial Intelligence; Agriculture; Farming Practices; Crop Management; Precision Farming; Crop Monitoring; Yield Prediction; Sustainability;
Copyright: © 2022 by Rodriguez. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) (Creative Commons Attribution 4.0 International License). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

Share and Cite

ACS Style
Rodriguez, M. AI in Agriculture: Revolutionizing Farming Practices and Crop Management. Journal of Arts, Society, and Education Studies, 2022, 4, 103. doi:10.xxxx/xxxxxx
AMA Style
Rodriguez M. AI in Agriculture: Revolutionizing Farming Practices and Crop Management. Journal of Arts, Society, and Education Studies; 2022, 4(2):103. doi:10.xxxx/xxxxxx
Chicago/Turabian Style
Rodriguez, Mary 2022. "AI in Agriculture: Revolutionizing Farming Practices and Crop Management" Journal of Arts, Society, and Education Studies 4, no.2:103. doi:10.xxxx/xxxxxx

Article Metrics

Article Access Statistics

References

  1. Andrade, R. G., & Smith, B. A. (2018). Applications of artificial intelligence in agriculture. Computers and Electronics in Agriculture, 153, 69-77.
  2. Belgiu, M., & Drautzburg, T. (2018). Deep learning for land use and land cover classification: A review. ISPRS Journal of Photogrammetry and Remote Sensing, 152, 166-179.
  3. Castañeda-Miranda, A. G., & Vargas-Moreno, J. M. (2020). A review of machine learning techniques for agriculture. Computers and Electronics in Agriculture, 176, 105682.
  4. Ge, Y., Wu, Q., & Geng, X. (2019). Artificial intelligence in agriculture: A systematic review. Engineering, 5(5), 850-857.
  5. Kamilaris, A., & Prenafeta-Boldú, F. X. (2018). Deep learning in agriculture: A survey. Computers and Electronics in Agriculture, 147, 70-90.
  6. Mahesh, S., & Pannu, P. K. (2020). Artificial intelligence-based solutions for agriculture: A review. Computers and Electronics in Agriculture, 176, 105687.
  7. Mohanty, S. P., Hughes, D. P., & Salathé, M. (2016). Using deep learning for image-based plant disease detection. Frontiers in Plant Science, 7, 1419.
  8. World Bank. (2019). The future of food: Harnessing digital technologies to improve food system outcomes.