A new deep learning-based technique for rice pest detection using remote sensing Article Swipe
Syeda Iqra Hassan
,
Muhammad Mansoor Alam
,
Usman Illahi
,
Mazliham Mohd Su’ud
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.7717/peerj-cs.1167
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.7717/peerj-cs.1167
Background Agriculture plays a vital role in the country’s economy and human society. Rice production is mainly focused on financial improvements as it is demanding worldwide. Protecting the rice field from pests during seedling and after production is becoming a challenging research problem. Identifying the pest at the right time is crucial so that the measures to prevent rice crops from pests can be taken by considering its stage. In this article, a new deep learning-based pest detection model is proposed. The proposed system can detect two types of rice pests (stem borer and Hispa) using an unmanned aerial vehicle (UAV). Methodology The image is captured in real time by a camera mounted on the UAV and then processed by filtering, labeling, and segmentation-based technique of color thresholding to convert the image into greyscale for extracting the region of interest. This article provides a rice pests dataset and a comparative analysis of existing pre-trained models. The proposed approach YO-CNN recommended in this study considers the results of the previous model because a smaller network was regarded to be better than a bigger one. Using additional layers has the advantage of preventing memorization, and it provides more precise results than existing techniques. Results The main contribution of the research is implementing a new modified deep learning model named Yolo-convolution neural network (YO-CNN) to obtain a precise output of up to 0.980 accuracies. It can be used to reduce rice wastage during production by monitoring the pests regularly. This technique can be used further for target spraying that saves applicators (fertilizer water and pesticide) and reduces the adverse effect of improper use of applicators on the environment and human beings.
Related Topics
Concepts
Deep learning
Computer science
Grayscale
Artificial intelligence
Convolutional neural network
Thresholding
Segmentation
Agricultural engineering
Field (mathematics)
Production (economics)
PEST analysis
Paddy field
Machine learning
Image (mathematics)
Mathematics
Agronomy
Engineering
Marketing
Pure mathematics
Economics
Macroeconomics
Business
Biology
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.7717/peerj-cs.1167
- OA Status
- gold
- Cited By
- 16
- References
- 44
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4323318238
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4323318238Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.7717/peerj-cs.1167Digital Object Identifier
- Title
-
A new deep learning-based technique for rice pest detection using remote sensingWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2023Year of publication
- Publication date
-
2023-03-06Full publication date if available
- Authors
-
Syeda Iqra Hassan, Muhammad Mansoor Alam, Usman Illahi, Mazliham Mohd Su’udList of authors in order
- Landing page
-
https://doi.org/10.7717/peerj-cs.1167Publisher landing page
- Open access
-
YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
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https://doi.org/10.7717/peerj-cs.1167Direct OA link when available
- Concepts
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Deep learning, Computer science, Grayscale, Artificial intelligence, Convolutional neural network, Thresholding, Segmentation, Agricultural engineering, Field (mathematics), Production (economics), PEST analysis, Paddy field, Machine learning, Image (mathematics), Mathematics, Agronomy, Engineering, Marketing, Pure mathematics, Economics, Macroeconomics, Business, BiologyTop concepts (fields/topics) attached by OpenAlex
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16Total citation count in OpenAlex
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2025: 6, 2024: 6, 2023: 4Per-year citation counts (last 5 years)
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44Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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