The continuous growth of the global population and rapid scientific advancements have increased the demand for high precision and efficient agricultural practices. Artificial Intelligence (AI) has emerged as a transformative tool to enhance the speed, accuracy, and automation of various agricultural processes. This study reviews the recent applications of AI, particularly image processing and machine vision techniques, in different stages of agricultural production, including planting, plowing, harvesting, and post-harvest operations such as storage, silage preparation, and drying. The findings indicate that AI assisted systems can effectively detect plant diseases, estimate yield, monitor soil and crop conditions, and optimize machinery operations with minimal human interventions. Furthermore, the integration of AI with Internet of Things (IoT) technologies enables real time data collection and intelligent decision making in smart farming systems. Despite the significant process made. Continuous technological development and the emergence of new agricultural challenges highlight the need for further research into advanced image processing algorithms, deep learning models, and data driven optimization approaches. Overall, AI and machine vision technologies have proven to be essential components for achieving sustainable, precise, and intelligent agricultural production.
| Published in | American Journal of Electrical and Computer Engineering (Volume 9, Issue 2) |
| DOI | 10.11648/j.ajece.20250902.13 |
| Page(s) | 36-44 |
| Creative Commons |
This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited. |
| Copyright |
Copyright © The Author(s), 2025. Published by Science Publishing Group |
Artificial Intelligence, Machine Vision, Agricultural Processes
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APA Style
Moghadam, S., Alimardani, R. (2025). Application of Artificial Intelligence in Agricultural Process. American Journal of Electrical and Computer Engineering, 9(2), 36-44. https://doi.org/10.11648/j.ajece.20250902.13
ACS Style
Moghadam, S.; Alimardani, R. Application of Artificial Intelligence in Agricultural Process. Am. J. Electr. Comput. Eng. 2025, 9(2), 36-44. doi: 10.11648/j.ajece.20250902.13
AMA Style
Moghadam S, Alimardani R. Application of Artificial Intelligence in Agricultural Process. Am J Electr Comput Eng. 2025;9(2):36-44. doi: 10.11648/j.ajece.20250902.13
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Download@article{10.11648/j.ajece.20250902.13,
author = {Sina Moghadam and Reza Alimardani},
title = {Application of Artificial Intelligence in Agricultural Process},
journal = {American Journal of Electrical and Computer Engineering},
volume = {9},
number = {2},
pages = {36-44},
doi = {10.11648/j.ajece.20250902.13},
url = {https://doi.org/10.11648/j.ajece.20250902.13},
eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ajece.20250902.13},
abstract = {The continuous growth of the global population and rapid scientific advancements have increased the demand for high precision and efficient agricultural practices. Artificial Intelligence (AI) has emerged as a transformative tool to enhance the speed, accuracy, and automation of various agricultural processes. This study reviews the recent applications of AI, particularly image processing and machine vision techniques, in different stages of agricultural production, including planting, plowing, harvesting, and post-harvest operations such as storage, silage preparation, and drying. The findings indicate that AI assisted systems can effectively detect plant diseases, estimate yield, monitor soil and crop conditions, and optimize machinery operations with minimal human interventions. Furthermore, the integration of AI with Internet of Things (IoT) technologies enables real time data collection and intelligent decision making in smart farming systems. Despite the significant process made. Continuous technological development and the emergence of new agricultural challenges highlight the need for further research into advanced image processing algorithms, deep learning models, and data driven optimization approaches. Overall, AI and machine vision technologies have proven to be essential components for achieving sustainable, precise, and intelligent agricultural production.},
year = {2025}
}
TY - JOUR T1 - Application of Artificial Intelligence in Agricultural Process AU - Sina Moghadam AU - Reza Alimardani Y1 - 2025/12/09 PY - 2025 N1 - https://doi.org/10.11648/j.ajece.20250902.13 DO - 10.11648/j.ajece.20250902.13 T2 - American Journal of Electrical and Computer Engineering JF - American Journal of Electrical and Computer Engineering JO - American Journal of Electrical and Computer Engineering SP - 36 EP - 44 PB - Science Publishing Group SN - 2640-0502 UR - https://doi.org/10.11648/j.ajece.20250902.13 AB - The continuous growth of the global population and rapid scientific advancements have increased the demand for high precision and efficient agricultural practices. Artificial Intelligence (AI) has emerged as a transformative tool to enhance the speed, accuracy, and automation of various agricultural processes. This study reviews the recent applications of AI, particularly image processing and machine vision techniques, in different stages of agricultural production, including planting, plowing, harvesting, and post-harvest operations such as storage, silage preparation, and drying. The findings indicate that AI assisted systems can effectively detect plant diseases, estimate yield, monitor soil and crop conditions, and optimize machinery operations with minimal human interventions. Furthermore, the integration of AI with Internet of Things (IoT) technologies enables real time data collection and intelligent decision making in smart farming systems. Despite the significant process made. Continuous technological development and the emergence of new agricultural challenges highlight the need for further research into advanced image processing algorithms, deep learning models, and data driven optimization approaches. Overall, AI and machine vision technologies have proven to be essential components for achieving sustainable, precise, and intelligent agricultural production. VL - 9 IS - 2 ER -
Department of Agricultural Machinery, College of Agriculture and Natural Resources of University of Tehran, Karaj, Iran
Department of Agricultural Machinery, College of Agriculture and Natural Resources of University of Tehran, Karaj, Iran
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