Paddy Crop and Weed Discrimination: A Multiple Classifier System Approach
Joint Authors
Kamath, Radhika
Balachandra, Mamatha
Prabhu, Srikanth
Source
International Journal of Agronomy
Issue
Vol. 2020, Issue 2020 (31 Dec. 2020), pp.1-14, 14 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2020-06-29
Country of Publication
Egypt
No. of Pages
14
Main Subjects
Abstract EN
Weeds are unwanted plants that grow among crops.
These weeds can significantly reduce the yield and quality of the farm output.
Unfortunately, site-specific weed management is not followed in most of the cases.
That is, instead of treating a field with a specific type of herbicide, the field is treated with a broadcast herbicide application.
This broadcast application of the herbicide has resulted in herbicide-resistant weeds and has many ill effects on the natural environment.
This has prompted many research studies to seek the most effective weed management techniques.
One such technique is computer vision-based automatic weed detection and identification.
Using this technique, weeds can be detected and identified and a suitable herbicide can be recommended to the farmers.
Therefore, it is important for the computer vision technique to successfully identify and classify the crops and weeds from the digital images.
This paper investigates the multiple classifier systems built using support vector machines and random forest classifiers for plant classification in classifying paddy crops and weeds from digital images.
Digital images of paddy crops and weeds from the paddy fields were acquired using three different cameras fixed at different heights from the ground.
Texture, color, and shape features were extracted from the digital images after background subtraction and used for classification.
A simple and new method was used as a decision function in the multiple classifier systems.
An accuracy of 91.36% was obtained by the multiple classifier systems and was found to outperform single classifier systems.
American Psychological Association (APA)
Kamath, Radhika& Balachandra, Mamatha& Prabhu, Srikanth. 2020. Paddy Crop and Weed Discrimination: A Multiple Classifier System Approach. International Journal of Agronomy،Vol. 2020, no. 2020, pp.1-14.
https://search.emarefa.net/detail/BIM-1167392
Modern Language Association (MLA)
Kamath, Radhika…[et al.]. Paddy Crop and Weed Discrimination: A Multiple Classifier System Approach. International Journal of Agronomy No. 2020 (2020), pp.1-14.
https://search.emarefa.net/detail/BIM-1167392
American Medical Association (AMA)
Kamath, Radhika& Balachandra, Mamatha& Prabhu, Srikanth. Paddy Crop and Weed Discrimination: A Multiple Classifier System Approach. International Journal of Agronomy. 2020. Vol. 2020, no. 2020, pp.1-14.
https://search.emarefa.net/detail/BIM-1167392
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1167392