APPLICATION OF ARTIFICIAL INTELLIGENCE AND REMOTE SENSING TECHNOLOGIES IN EARLY PLANT DISEASE DETECTION

Authors

  • Komilova Mohinabonu x

DOI:

https://doi.org/10.5281/zenodo.20322399

Keywords:

Artificial Intelligence, Remote Sensing, Plant Disease Detection, Machine Learning, Deep Learning, Multispectral Imaging, Digital Agriculture, Convolutional Neural Networks (CNN).

Abstract

This article comprehensively analyzes the scientific and practical potential of artificial intelligence and remote sensing technologies in the early detection of plant diseases. The study examines the significance of modern digital monitoring methods, including multispectral imaging, drone technologies, satellite data analysis, and machine learning algorithms in the fields of botany and agriculture. The primary objective of the research is to evaluate the effectiveness of automated diagnostic systems based on artificial intelligence in identifying the early stages of plant diseases, particularly in conditions where visual detection by humans is limited or inefficient.

The findings demonstrate that remote sensing technologies significantly improve the ability to monitor the physiological condition of plants in real time, detect disease symptoms at an early stage, and forecast factors that may negatively affect crop productivity. Furthermore, the study scientifically confirms that systems operating on convolutional neural networks (CNNs), machine learning, and deep learning models can substantially increase the accuracy of plant disease detection.

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References

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Published

2026-05-21

Issue

Vol. 5 No. 5 (2026): Journal of Multidisciplinary Sciences and Innovations

Section

Articles

License

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

Authors retain the copyright of their manuscripts, and all Open Access articles are disseminated under the terms of the Creative Commons Attribution License 4.0 (CC-BY), which licenses unrestricted use, distribution, and reproduction in any medium, provided that the original work is appropriately cited. The use of general descriptive names, trade names, trademarks, and so forth in this publication, even if not specifically identified, does not imply that these names are not protected by the relevant laws and regulations.

How to Cite

APPLICATION OF ARTIFICIAL INTELLIGENCE AND REMOTE SENSING TECHNOLOGIES IN EARLY PLANT DISEASE DETECTION. (2026). Journal of Multidisciplinary Sciences and Innovations, 5(5), 1498-1505. https://doi.org/10.5281/zenodo.20322399

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