Geospatial-based mapping and monitoring of pest and disease outbreaks utilizing machine learning
(2025) p.213-255- Abstract
For this project, machine learning and cutting-edge remote sensing technologies are used to create a geospatial mapping and monitoring system for agricultural pest and disease outbreaks. By combining satellite data, unmanned aerial vehicle hyperspectral evaluation, and environmental factors, the system can identify the spread of pests and diseases in real time. Data evaluation and outbreak prediction activities are carried out by three machine learning algorithms: random forest, support vector machine, and neural networks. The system’s architecture helps regulate resources for greater environmental preservation while producing targeted pest-management suggestions. By using geographic data and predictive methods to carry out focused... (More)
For this project, machine learning and cutting-edge remote sensing technologies are used to create a geospatial mapping and monitoring system for agricultural pest and disease outbreaks. By combining satellite data, unmanned aerial vehicle hyperspectral evaluation, and environmental factors, the system can identify the spread of pests and diseases in real time. Data evaluation and outbreak prediction activities are carried out by three machine learning algorithms: random forest, support vector machine, and neural networks. The system’s architecture helps regulate resources for greater environmental preservation while producing targeted pest-management suggestions. By using geographic data and predictive methods to carry out focused interventions and facilitate sustainable agriculture management practices, the system improves the identification of possible hazards. Because the project developers are always working on system refinement through field verification and user input, problems with data quality, model correctness, and real-time monitoring require constant attention. Researchers are currently looking at how blockchain technology and Internet of Things device integration might improve system scalability and guarantee data integrity. The created system represents a significant advancement in the management of pests and diseases via the use of machine learning and geospatial technology applications, which offer scalable solutions for global agricultural problems and increase the resilience of the food chain.
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- author
- Goenka, Deep Kumar and Pal, Mahendra LU
- organization
- publishing date
- 2025-01
- type
- Chapter in Book/Report/Conference proceeding
- publication status
- published
- subject
- keywords
- Geospatial mapping, hyperspectral imaging, machine learning, pest and disease monitoring, remote sensing
- host publication
- Agricultural Insights from Space : Machine Learning Applications in Satellite Data Analysis - Machine Learning Applications in Satellite Data Analysis
- pages
- 43 pages
- publisher
- Elsevier
- external identifiers
-
- scopus:105026964040
- ISBN
- 9780443341144
- 9780443341137
- DOI
- 10.1016/B978-0-443-34113-7.00009-2
- language
- English
- LU publication?
- yes
- id
- 7bff6166-332d-439a-803d-c7e9ea988e3a
- date added to LUP
- 2026-02-16 09:33:30
- date last changed
- 2026-02-16 09:34:46
@inbook{7bff6166-332d-439a-803d-c7e9ea988e3a,
abstract = {{<p>For this project, machine learning and cutting-edge remote sensing technologies are used to create a geospatial mapping and monitoring system for agricultural pest and disease outbreaks. By combining satellite data, unmanned aerial vehicle hyperspectral evaluation, and environmental factors, the system can identify the spread of pests and diseases in real time. Data evaluation and outbreak prediction activities are carried out by three machine learning algorithms: random forest, support vector machine, and neural networks. The system’s architecture helps regulate resources for greater environmental preservation while producing targeted pest-management suggestions. By using geographic data and predictive methods to carry out focused interventions and facilitate sustainable agriculture management practices, the system improves the identification of possible hazards. Because the project developers are always working on system refinement through field verification and user input, problems with data quality, model correctness, and real-time monitoring require constant attention. Researchers are currently looking at how blockchain technology and Internet of Things device integration might improve system scalability and guarantee data integrity. The created system represents a significant advancement in the management of pests and diseases via the use of machine learning and geospatial technology applications, which offer scalable solutions for global agricultural problems and increase the resilience of the food chain.</p>}},
author = {{Goenka, Deep Kumar and Pal, Mahendra}},
booktitle = {{Agricultural Insights from Space : Machine Learning Applications in Satellite Data Analysis}},
isbn = {{9780443341144}},
keywords = {{Geospatial mapping; hyperspectral imaging; machine learning; pest and disease monitoring; remote sensing}},
language = {{eng}},
pages = {{213--255}},
publisher = {{Elsevier}},
title = {{Geospatial-based mapping and monitoring of pest and disease outbreaks utilizing machine learning}},
url = {{http://dx.doi.org/10.1016/B978-0-443-34113-7.00009-2}},
doi = {{10.1016/B978-0-443-34113-7.00009-2}},
year = {{2025}},
}