CROP AND WEED SEGMENTATION ON GROUND-BASED IMAGES USING DEEP CONVOLUTIONAL NEURAL NETWORK
<p>Weed management is of crucial importance in precision agriculture to improve productivity and reduce herbicide pollution. In this regard, showing promising results, deep learning algorithms have increasingly gained attention for crop and weed segmentation in agricultural fields. In this pap...
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| Format: | Article |
| Language: | English |
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Copernicus Publications
2023-01-01
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| Series: | ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences |
| Online Access: | https://www.isprs-ann-photogramm-remote-sens-spatial-inf-sci.net/X-4-W1-2022/195/2023/isprs-annals-X-4-W1-2022-195-2023.pdf |
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| author | H. Fathipoor R. Shah-Hosseini H. Arefi |
| author_facet | H. Fathipoor R. Shah-Hosseini H. Arefi |
| author_sort | H. Fathipoor |
| collection | DOAJ |
| description | <p>Weed management is of crucial importance in precision agriculture to improve productivity and reduce herbicide pollution. In this regard, showing promising results, deep learning algorithms have increasingly gained attention for crop and weed segmentation in agricultural fields. In this paper, the U-Net++ network, a state-of-the-art convolutional neural network (CNN) algorithm, which has rarely been used in precision agriculture, was implemented for the semantic segmentation of weed images. Then, we compared the model performance to that of the U-Net algorithm based on various criteria.</p><p>The results show that the U-Net++ outperforms traditional U-Net in terms of overall accuracy, intersection over union (IoU), recall, and F1-Score metrics. Furthermore, the U-Net++ model provided weed IoU of 65%, whereas the U-Net gave weed IoU of 56%. In addition, the results indicate that the U-Net++ is quite capable of detecting small weeds, suggesting that this architecture is more desirable for identifying weeds in the early growing season.</p> |
| first_indexed | 2024-04-10T22:56:11Z |
| format | Article |
| id | doaj.art-c030d26bf67c4bfdb6b98c6559fc86e2 |
| institution | Directory Open Access Journal |
| issn | 2194-9042 2194-9050 |
| language | English |
| last_indexed | 2024-04-10T22:56:11Z |
| publishDate | 2023-01-01 |
| publisher | Copernicus Publications |
| record_format | Article |
| series | ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences |
| spelling | doaj.art-c030d26bf67c4bfdb6b98c6559fc86e22023-01-14T11:02:12ZengCopernicus PublicationsISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences2194-90422194-90502023-01-01X-4-W1-202219520010.5194/isprs-annals-X-4-W1-2022-195-2023CROP AND WEED SEGMENTATION ON GROUND-BASED IMAGES USING DEEP CONVOLUTIONAL NEURAL NETWORKH. Fathipoor0R. Shah-Hosseini1H. Arefi2School of Surveying and Geospatial Engineering, College of Engineering, University of Tehran, IranSchool of Surveying and Geospatial Engineering, College of Engineering, University of Tehran, IranSchool of Technology, Department of Geoinformatics and Surveying, Mainz University of Applied Sciences, Germany<p>Weed management is of crucial importance in precision agriculture to improve productivity and reduce herbicide pollution. In this regard, showing promising results, deep learning algorithms have increasingly gained attention for crop and weed segmentation in agricultural fields. In this paper, the U-Net++ network, a state-of-the-art convolutional neural network (CNN) algorithm, which has rarely been used in precision agriculture, was implemented for the semantic segmentation of weed images. Then, we compared the model performance to that of the U-Net algorithm based on various criteria.</p><p>The results show that the U-Net++ outperforms traditional U-Net in terms of overall accuracy, intersection over union (IoU), recall, and F1-Score metrics. Furthermore, the U-Net++ model provided weed IoU of 65%, whereas the U-Net gave weed IoU of 56%. In addition, the results indicate that the U-Net++ is quite capable of detecting small weeds, suggesting that this architecture is more desirable for identifying weeds in the early growing season.</p>https://www.isprs-ann-photogramm-remote-sens-spatial-inf-sci.net/X-4-W1-2022/195/2023/isprs-annals-X-4-W1-2022-195-2023.pdf |
| spellingShingle | H. Fathipoor R. Shah-Hosseini H. Arefi CROP AND WEED SEGMENTATION ON GROUND-BASED IMAGES USING DEEP CONVOLUTIONAL NEURAL NETWORK ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences |
| title | CROP AND WEED SEGMENTATION ON GROUND-BASED IMAGES USING DEEP CONVOLUTIONAL NEURAL NETWORK |
| title_full | CROP AND WEED SEGMENTATION ON GROUND-BASED IMAGES USING DEEP CONVOLUTIONAL NEURAL NETWORK |
| title_fullStr | CROP AND WEED SEGMENTATION ON GROUND-BASED IMAGES USING DEEP CONVOLUTIONAL NEURAL NETWORK |
| title_full_unstemmed | CROP AND WEED SEGMENTATION ON GROUND-BASED IMAGES USING DEEP CONVOLUTIONAL NEURAL NETWORK |
| title_short | CROP AND WEED SEGMENTATION ON GROUND-BASED IMAGES USING DEEP CONVOLUTIONAL NEURAL NETWORK |
| title_sort | crop and weed segmentation on ground based images using deep convolutional neural network |
| url | https://www.isprs-ann-photogramm-remote-sens-spatial-inf-sci.net/X-4-W1-2022/195/2023/isprs-annals-X-4-W1-2022-195-2023.pdf |
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