Computer vision methods and algorithms for automatic detection and classification of objects in decision support systems in agriculture

The paper examines aspects of developing and formalizing the task of applying computer vision methods and algorithms using OpenCV (implemented in Python version 3.13 notation) for automatic detection and classification of objects in decision support systems. A software implementation of a modular ex...

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Bibliographic Details
Published in:E3S web of conferences Vol. 548; p. 3023
Main Authors: Yablokova, Alena, Kovalev, Igor, Kovalev, Dmitry, Podoplelova, Valeria, Kobilova, Aziza
Format: Journal Article Conference Proceeding
Language:English
Published: Les Ulis EDP Sciences 01.01.2024
Subjects:
Access control
Accuracy
Agriculture
Algorithms
Artificial intelligence
Classification
Computer vision
Data analysis
Data processing
Decision making
Decision support systems
Deep learning
Digitization
Machine learning
Neural networks
Plant diseases
Python
Sensors
Support vector machines
Uzbekistan
Russia
ISSN:2267-1242, 2555-0403, 2267-1242
Online Access:Get full text
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Summary:The paper examines aspects of developing and formalizing the task of applying computer vision methods and algorithms using OpenCV (implemented in Python version 3.13 notation) for automatic detection and classification of objects in decision support systems. A software implementation of a modular example is provided, enabling automatic detection and classification for the detection of plant diseases based on their external characteristics in decision support systems in agriculture. This approach will facilitate prompt response to plant diseases and the implementation of necessary measures for their treatment.
Bibliography:ObjectType-Conference Proceeding-1
SourceType-Conference Papers & Proceedings-1
content type line 21
ISSN:2267-1242
2555-0403
2267-1242
DOI:10.1051/e3sconf/202454803023