LodgeNet: an automated framework for precise detection and classification of wheat lodging severity levels in precision farming
Wheat lodging is a serious problem affecting grain yield, plant health, and grain quality. Addressing the lodging issue in wheat is a desirable task in breeding programs. Precise detection of lodging levels during wheat screening can aid in selecting lines with resistance to lodging. Traditional app...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2023-11-01
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| Series: | Frontiers in Plant Science |
| Subjects: | |
| Online Access: | https://www.frontiersin.org/articles/10.3389/fpls.2023.1255961/full |
| _version_ | 1797449220933812224 |
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| author | Nisar Ali Ahmed Mohammed Abdul Bais Jatinder S. Sangha Yuefeng Ruan Richard D. Cuthbert |
| author_facet | Nisar Ali Ahmed Mohammed Abdul Bais Jatinder S. Sangha Yuefeng Ruan Richard D. Cuthbert |
| author_sort | Nisar Ali |
| collection | DOAJ |
| description | Wheat lodging is a serious problem affecting grain yield, plant health, and grain quality. Addressing the lodging issue in wheat is a desirable task in breeding programs. Precise detection of lodging levels during wheat screening can aid in selecting lines with resistance to lodging. Traditional approaches to phenotype lodging rely on manual data collection from field plots, which are slow and laborious, and can introduce errors and bias. This paper presents a framework called ‘LodgeNet,’ that facilitates wheat lodging detection. Using Unmanned Aerial Vehicles (UAVs) and Deep Learning (DL), LodgeNet improves traditional methods of detecting lodging with more precision and efficiency. Using a dataset of 2000 multi-spectral images of wheat plots, we have developed a novel image registration technique that aligns the different bands of multi-spectral images. This approach allows the creation of comprehensive RGB images, enhancing the detection and classification of wheat lodging. We have employed advanced image enhancement techniques to improve image quality, highlighting the important features of wheat lodging detection. We combined three color enhancement transformations into two presets for image refinement. The first preset, ‘Haze & Gamma Adjustment,’ minimize atmospheric haze and adjusts the gamma, while the second, ‘Stretching Contrast Limits,’ extends the contrast of the RGB image by calculating and applying the upper and lower limits of each band. LodgeNet, which relies on the state-of-the-art YOLOv8 deep learning algorithm, could detect and classify wheat lodging severity levels ranging from no lodging (Class 1) to severe lodging (Class 9). The results show the mean Average Precision (mAP) of 0.952% @0.5 and 0.641% @0.50-0.95 in classifying wheat lodging severity levels. LodgeNet promises an efficient and automated high-throughput solution for real-time crop monitoring of wheat lodging severity levels in the field. |
| first_indexed | 2024-03-09T14:22:55Z |
| format | Article |
| id | doaj.art-96a075d720024818ad05e066ffc6e633 |
| institution | Directory Open Access Journal |
| issn | 1664-462X |
| language | English |
| last_indexed | 2024-03-09T14:22:55Z |
| publishDate | 2023-11-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Plant Science |
| spelling | doaj.art-96a075d720024818ad05e066ffc6e6332023-11-28T09:40:38ZengFrontiers Media S.A.Frontiers in Plant Science1664-462X2023-11-011410.3389/fpls.2023.12559611255961LodgeNet: an automated framework for precise detection and classification of wheat lodging severity levels in precision farmingNisar Ali0Ahmed Mohammed1Abdul Bais2Jatinder S. Sangha3Yuefeng Ruan4Richard D. Cuthbert5Faculty of Engineering and Applied Science, University of Regina, Regina, SK, CanadaFaculty of Engineering and Applied Science, University of Regina, Regina, SK, CanadaFaculty of Engineering and Applied Science, University of Regina, Regina, SK, CanadaSwift Current Research and Development Centre, Agriculture and Agri-Food Canada, Swift Current, SK, CanadaSwift Current Research and Development Centre, Agriculture and Agri-Food Canada, Swift Current, SK, CanadaSwift Current Research and Development Centre, Agriculture and Agri-Food Canada, Swift Current, SK, CanadaWheat lodging is a serious problem affecting grain yield, plant health, and grain quality. Addressing the lodging issue in wheat is a desirable task in breeding programs. Precise detection of lodging levels during wheat screening can aid in selecting lines with resistance to lodging. Traditional approaches to phenotype lodging rely on manual data collection from field plots, which are slow and laborious, and can introduce errors and bias. This paper presents a framework called ‘LodgeNet,’ that facilitates wheat lodging detection. Using Unmanned Aerial Vehicles (UAVs) and Deep Learning (DL), LodgeNet improves traditional methods of detecting lodging with more precision and efficiency. Using a dataset of 2000 multi-spectral images of wheat plots, we have developed a novel image registration technique that aligns the different bands of multi-spectral images. This approach allows the creation of comprehensive RGB images, enhancing the detection and classification of wheat lodging. We have employed advanced image enhancement techniques to improve image quality, highlighting the important features of wheat lodging detection. We combined three color enhancement transformations into two presets for image refinement. The first preset, ‘Haze & Gamma Adjustment,’ minimize atmospheric haze and adjusts the gamma, while the second, ‘Stretching Contrast Limits,’ extends the contrast of the RGB image by calculating and applying the upper and lower limits of each band. LodgeNet, which relies on the state-of-the-art YOLOv8 deep learning algorithm, could detect and classify wheat lodging severity levels ranging from no lodging (Class 1) to severe lodging (Class 9). The results show the mean Average Precision (mAP) of 0.952% @0.5 and 0.641% @0.50-0.95 in classifying wheat lodging severity levels. LodgeNet promises an efficient and automated high-throughput solution for real-time crop monitoring of wheat lodging severity levels in the field.https://www.frontiersin.org/articles/10.3389/fpls.2023.1255961/fullwheat lodgingclassificationmulti-spectral imagingUnmanned Aerial Vehicledeep learning |
| spellingShingle | Nisar Ali Ahmed Mohammed Abdul Bais Jatinder S. Sangha Yuefeng Ruan Richard D. Cuthbert LodgeNet: an automated framework for precise detection and classification of wheat lodging severity levels in precision farming Frontiers in Plant Science wheat lodging classification multi-spectral imaging Unmanned Aerial Vehicle deep learning |
| title | LodgeNet: an automated framework for precise detection and classification of wheat lodging severity levels in precision farming |
| title_full | LodgeNet: an automated framework for precise detection and classification of wheat lodging severity levels in precision farming |
| title_fullStr | LodgeNet: an automated framework for precise detection and classification of wheat lodging severity levels in precision farming |
| title_full_unstemmed | LodgeNet: an automated framework for precise detection and classification of wheat lodging severity levels in precision farming |
| title_short | LodgeNet: an automated framework for precise detection and classification of wheat lodging severity levels in precision farming |
| title_sort | lodgenet an automated framework for precise detection and classification of wheat lodging severity levels in precision farming |
| topic | wheat lodging classification multi-spectral imaging Unmanned Aerial Vehicle deep learning |
| url | https://www.frontiersin.org/articles/10.3389/fpls.2023.1255961/full |
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