An Integrated Multi-Model Fusion System for Automatically Diagnosing the Severity of Wheat Fusarium Head Blight
Fusarium has become a major impediment to stable wheat production in many regions worldwide. Infected wheat plants not only experience reduced yield and quality but their spikes generate toxins that pose a significant threat to human and animal health. Currently, there are two primary methods for ef...
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| Format: | Article |
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MDPI AG
2023-07-01
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| Series: | Agriculture |
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| Online Access: | https://www.mdpi.com/2077-0472/13/7/1381 |
| _version_ | 1797590657567555584 |
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| author | Ya-Hong Wang Jun-Jiang Li Wen-Hao Su |
| author_facet | Ya-Hong Wang Jun-Jiang Li Wen-Hao Su |
| author_sort | Ya-Hong Wang |
| collection | DOAJ |
| description | Fusarium has become a major impediment to stable wheat production in many regions worldwide. Infected wheat plants not only experience reduced yield and quality but their spikes generate toxins that pose a significant threat to human and animal health. Currently, there are two primary methods for effectively controlling Fusarium head blight (FHB): spraying quantitative chemical agents and breeding disease-resistant wheat varieties. The premise of both methods is to accurately diagnosis the severity of wheat FHB in real time. In this study, a deep learning-based multi-model fusion system was developed for integrated detection of FHB severity. Combination schemes of network frameworks and backbones for wheat spike and spot segmentation were investigated. The training results demonstrated that Mobilev3-Deeplabv3+ exhibits strong multi-scale feature refinement capabilities and achieved a high segmentation accuracy of 97.6% for high-throughput wheat spike images. By implementing parallel feature fusion from high- to low-resolution inputs, w48-Hrnet excelled at recognizing fine and complex FHB spots, resulting in up to 99.8% accuracy. Refinement of wheat FHB grading classification from the perspectives of epidemic control (zero to five levels) and breeding (zero to 14 levels) has been accomplished. In addition, the effectiveness of introducing HSV color feature as a weighting factor into the evaluation model for grading of wheat spikes was verified. The multi-model fusion algorithm, developed specifically for the all-in-one process, successfully accomplished the tasks of segmentation, extraction, and classification, with an overall accuracy of 92.6% for FHB severity grades. The integrated system, combining deep learning and image analysis, provides a reliable and nondestructive diagnosis of wheat FHB, enabling real-time monitoring for farmers and researchers. |
| first_indexed | 2024-03-11T01:24:34Z |
| format | Article |
| id | doaj.art-80cfa80aeb734658aa1f0a3c9ac346ba |
| institution | Directory Open Access Journal |
| issn | 2077-0472 |
| language | English |
| last_indexed | 2024-03-11T01:24:34Z |
| publishDate | 2023-07-01 |
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| series | Agriculture |
| spelling | doaj.art-80cfa80aeb734658aa1f0a3c9ac346ba2023-11-18T17:53:02ZengMDPI AGAgriculture2077-04722023-07-01137138110.3390/agriculture13071381An Integrated Multi-Model Fusion System for Automatically Diagnosing the Severity of Wheat Fusarium Head BlightYa-Hong Wang0Jun-Jiang Li1Wen-Hao Su2College of Engineering, China Agricultural University, 17 Qinghua East Road, Haidian, Beijing 100083, ChinaSchool of Mechanical Engineering, Xi’an Jiaotong University, 28 Xianning West Road, Xi’an 710048, ChinaCollege of Engineering, China Agricultural University, 17 Qinghua East Road, Haidian, Beijing 100083, ChinaFusarium has become a major impediment to stable wheat production in many regions worldwide. Infected wheat plants not only experience reduced yield and quality but their spikes generate toxins that pose a significant threat to human and animal health. Currently, there are two primary methods for effectively controlling Fusarium head blight (FHB): spraying quantitative chemical agents and breeding disease-resistant wheat varieties. The premise of both methods is to accurately diagnosis the severity of wheat FHB in real time. In this study, a deep learning-based multi-model fusion system was developed for integrated detection of FHB severity. Combination schemes of network frameworks and backbones for wheat spike and spot segmentation were investigated. The training results demonstrated that Mobilev3-Deeplabv3+ exhibits strong multi-scale feature refinement capabilities and achieved a high segmentation accuracy of 97.6% for high-throughput wheat spike images. By implementing parallel feature fusion from high- to low-resolution inputs, w48-Hrnet excelled at recognizing fine and complex FHB spots, resulting in up to 99.8% accuracy. Refinement of wheat FHB grading classification from the perspectives of epidemic control (zero to five levels) and breeding (zero to 14 levels) has been accomplished. In addition, the effectiveness of introducing HSV color feature as a weighting factor into the evaluation model for grading of wheat spikes was verified. The multi-model fusion algorithm, developed specifically for the all-in-one process, successfully accomplished the tasks of segmentation, extraction, and classification, with an overall accuracy of 92.6% for FHB severity grades. The integrated system, combining deep learning and image analysis, provides a reliable and nondestructive diagnosis of wheat FHB, enabling real-time monitoring for farmers and researchers.https://www.mdpi.com/2077-0472/13/7/1381deep learningwheatfusarium head blightimage segmentationall-in-one detection |
| spellingShingle | Ya-Hong Wang Jun-Jiang Li Wen-Hao Su An Integrated Multi-Model Fusion System for Automatically Diagnosing the Severity of Wheat Fusarium Head Blight Agriculture deep learning wheat fusarium head blight image segmentation all-in-one detection |
| title | An Integrated Multi-Model Fusion System for Automatically Diagnosing the Severity of Wheat Fusarium Head Blight |
| title_full | An Integrated Multi-Model Fusion System for Automatically Diagnosing the Severity of Wheat Fusarium Head Blight |
| title_fullStr | An Integrated Multi-Model Fusion System for Automatically Diagnosing the Severity of Wheat Fusarium Head Blight |
| title_full_unstemmed | An Integrated Multi-Model Fusion System for Automatically Diagnosing the Severity of Wheat Fusarium Head Blight |
| title_short | An Integrated Multi-Model Fusion System for Automatically Diagnosing the Severity of Wheat Fusarium Head Blight |
| title_sort | integrated multi model fusion system for automatically diagnosing the severity of wheat fusarium head blight |
| topic | deep learning wheat fusarium head blight image segmentation all-in-one detection |
| url | https://www.mdpi.com/2077-0472/13/7/1381 |
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