Detection of Straw Coverage under Conservation Tillage Based on an Improved Mask Regional Convolutional Neural Network (Mask R-CNN) Article Swipe
Yuanyuan Shao
,
Xianlu Guan
,
Guantao Xuan
,
Hang Liu
,
Xiaoteng Li
,
Fengwei Gu
,
Zhichao Hu
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.3390/agronomy14071409
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.3390/agronomy14071409
Conservation tillage, a crucial method for protecting soil fertility, depends heavily on maintaining adequate straw coverage. The current method of straw coverage detection relies primarily on manual measurement, which is both time-consuming and laborious. This paper introduces a novel straw coverage detection approach based on an improved mask regional convolutional neural network (Mask R-CNN) algorithm. Several images of wheat straw-covered fields were taken, and the dataset was augmented using techniques like image inversion, contrast enhancement, Gaussian noise addition, and translation after cropping the original images. These fields use a crop rotation cycle of wheat and corn. Subsequently, the straw images were annotated using the Labelme annotation tool to obtain the available straw instance segmentation dataset. The Mask R-CNN algorithm was improved by refining the mask generation network structure through a multi-feature fusion strategy, which interweaves features from both the encoder and the mask generation network, enhancing the model’s ability to capture detailed and shape information of the straw. Lastly, using the mask information output by the improved Mask R-CNN algorithm, the straw coverage was calculated by counting the proportion of pixels within each segmented region. In the results, compared to the original Mask R-CNN algorithm, our improved Mask R-CNN algorithm achieved an average improvement of 7.8% in segmentation accuracy, indicating that the improved Mask R-CNN algorithm offers superior segmentation performance. Thus, the new algorithm can achieve straw coverage detection with higher accuracy and can provide a reference for other agricultural applications.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.3390/agronomy14071409
- https://www.mdpi.com/2073-4395/14/7/1409/pdf?version=1720663241
- OA Status
- gold
- Cited By
- 5
- References
- 26
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4400213462
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Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4400213462Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.3390/agronomy14071409Digital Object Identifier
- Title
-
Detection of Straw Coverage under Conservation Tillage Based on an Improved Mask Regional Convolutional Neural Network (Mask R-CNN)Work title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2024Year of publication
- Publication date
-
2024-06-28Full publication date if available
- Authors
-
Yuanyuan Shao, Xianlu Guan, Guantao Xuan, Hang Liu, Xiaoteng Li, Fengwei Gu, Zhichao HuList of authors in order
- Landing page
-
https://doi.org/10.3390/agronomy14071409Publisher landing page
- PDF URL
-
https://www.mdpi.com/2073-4395/14/7/1409/pdf?version=1720663241Direct link to full text PDF
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YesWhether a free full text is available
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-
goldOpen access status per OpenAlex
- OA URL
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https://www.mdpi.com/2073-4395/14/7/1409/pdf?version=1720663241Direct OA link when available
- Concepts
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Computer science, Convolutional neural network, Straw, Artificial intelligence, Segmentation, Feature (linguistics), Pattern recognition (psychology), Agronomy, Linguistics, Philosophy, BiologyTop concepts (fields/topics) attached by OpenAlex
- Cited by
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5Total citation count in OpenAlex
- Citations by year (recent)
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2025: 5Per-year citation counts (last 5 years)
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26Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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| primary_topic.subfield.display_name | Plant Science |
| primary_topic.display_name | Smart Agriculture and AI |
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