Edge–Region Collaborative Segmentation of Potato Leaf Disease Images Using Beluga Whale Optimization Algorithm with Danger Sensing Mechanism
Precise detection of potato diseases is critical for food security, yet traditional image segmentation methods struggle with challenges including uneven illumination, background noise, and the gradual color transitions of lesions under complex field conditions. Therefore, a collaborative segmentatio...
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MDPI AG
2025-05-01
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| Series: | Agriculture |
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| Online Access: | https://www.mdpi.com/2077-0472/15/11/1123 |
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| author | Jin-Ling Bei Ji-Quan Wang |
| author_facet | Jin-Ling Bei Ji-Quan Wang |
| author_sort | Jin-Ling Bei |
| collection | DOAJ |
| description | Precise detection of potato diseases is critical for food security, yet traditional image segmentation methods struggle with challenges including uneven illumination, background noise, and the gradual color transitions of lesions under complex field conditions. Therefore, a collaborative segmentation framework of Otsu and Sobel edge detection based on the beluga whale optimization algorithm with a danger sensing mechanism (DSBWO) is proposed. The method introduces an <i>S</i>-shaped control parameter, a danger sensing mechanism, a dynamic foraging strategy, and an improved whale fall model to enhance global search ability, prevent premature convergence, and improve solution quality. DSBWO demonstrates superior optimization performance on the CEC2017 benchmark, with faster convergence and higher accuracy than other algorithms. Experiments on the Berkeley Segmentation Dataset and potato early/late blight images show that DSBWO achieves excellent segmentation performance across multiple evaluation metrics. Specifically, it reaches a maximum IoU of 0.8797, outperforming JSBWO (0.8482) and PSOSHO (0.8503), while maintaining competitive PSNR and SSIM values. Even under different Gaussian noise levels, DSBWO maintains stable segmentation accuracy and low CPU time, confirming its robustness. These findings suggest that DSBWO provides a reliable and efficient solution for automatic crop disease monitoring and can be extended to other smart agriculture applications. |
| format | Article |
| id | doaj-art-9ced2aa90ce9472c8dee921c0b694164 |
| institution | Kabale University |
| issn | 2077-0472 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | MDPI AG |
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| series | Agriculture |
| spelling | doaj-art-9ced2aa90ce9472c8dee921c0b6941642025-08-20T03:46:47ZengMDPI AGAgriculture2077-04722025-05-011511112310.3390/agriculture15111123Edge–Region Collaborative Segmentation of Potato Leaf Disease Images Using Beluga Whale Optimization Algorithm with Danger Sensing MechanismJin-Ling Bei0Ji-Quan Wang1College of Engineering, Northeast Agricultural University, Harbin 150030, ChinaCollege of Engineering, Northeast Agricultural University, Harbin 150030, ChinaPrecise detection of potato diseases is critical for food security, yet traditional image segmentation methods struggle with challenges including uneven illumination, background noise, and the gradual color transitions of lesions under complex field conditions. Therefore, a collaborative segmentation framework of Otsu and Sobel edge detection based on the beluga whale optimization algorithm with a danger sensing mechanism (DSBWO) is proposed. The method introduces an <i>S</i>-shaped control parameter, a danger sensing mechanism, a dynamic foraging strategy, and an improved whale fall model to enhance global search ability, prevent premature convergence, and improve solution quality. DSBWO demonstrates superior optimization performance on the CEC2017 benchmark, with faster convergence and higher accuracy than other algorithms. Experiments on the Berkeley Segmentation Dataset and potato early/late blight images show that DSBWO achieves excellent segmentation performance across multiple evaluation metrics. Specifically, it reaches a maximum IoU of 0.8797, outperforming JSBWO (0.8482) and PSOSHO (0.8503), while maintaining competitive PSNR and SSIM values. Even under different Gaussian noise levels, DSBWO maintains stable segmentation accuracy and low CPU time, confirming its robustness. These findings suggest that DSBWO provides a reliable and efficient solution for automatic crop disease monitoring and can be extended to other smart agriculture applications.https://www.mdpi.com/2077-0472/15/11/1123potato disease imagebeluga whale optimizationmultilevel thresholdingOtsu methodSobel edge detection |
| spellingShingle | Jin-Ling Bei Ji-Quan Wang Edge–Region Collaborative Segmentation of Potato Leaf Disease Images Using Beluga Whale Optimization Algorithm with Danger Sensing Mechanism Agriculture potato disease image beluga whale optimization multilevel thresholding Otsu method Sobel edge detection |
| title | Edge–Region Collaborative Segmentation of Potato Leaf Disease Images Using Beluga Whale Optimization Algorithm with Danger Sensing Mechanism |
| title_full | Edge–Region Collaborative Segmentation of Potato Leaf Disease Images Using Beluga Whale Optimization Algorithm with Danger Sensing Mechanism |
| title_fullStr | Edge–Region Collaborative Segmentation of Potato Leaf Disease Images Using Beluga Whale Optimization Algorithm with Danger Sensing Mechanism |
| title_full_unstemmed | Edge–Region Collaborative Segmentation of Potato Leaf Disease Images Using Beluga Whale Optimization Algorithm with Danger Sensing Mechanism |
| title_short | Edge–Region Collaborative Segmentation of Potato Leaf Disease Images Using Beluga Whale Optimization Algorithm with Danger Sensing Mechanism |
| title_sort | edge region collaborative segmentation of potato leaf disease images using beluga whale optimization algorithm with danger sensing mechanism |
| topic | potato disease image beluga whale optimization multilevel thresholding Otsu method Sobel edge detection |
| url | https://www.mdpi.com/2077-0472/15/11/1123 |
| work_keys_str_mv | AT jinlingbei edgeregioncollaborativesegmentationofpotatoleafdiseaseimagesusingbelugawhaleoptimizationalgorithmwithdangersensingmechanism AT jiquanwang edgeregioncollaborativesegmentationofpotatoleafdiseaseimagesusingbelugawhaleoptimizationalgorithmwithdangersensingmechanism |