Edge–Region Collaborative Segmentation of Potato Leaf Disease Images Using Beluga Whale Optimization Algorithm with Danger Sensing Mechanism

• Published in: Agriculture (Basel) Vol. 15; no. 11; p. 1123
• Main Authors: Bei, Jin-Ling, Wang, Ji-Quan
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.06.2025
• Subjects: Accuracy; Agriculture; Algorithms; Background noise; beluga whale optimization; Cetacea; Collaboration; Convergence; Crop diseases; Digital agriculture; Disease; Diseases; Edge detection; Efficiency; Embedded systems; Exploitation; Food security; Food supply; Foraging behavior; Image processing; Image segmentation; Late blight; Mathematical optimization; Medical imaging; Methods; multilevel thresholding; Noise levels; Optimization; Optimization algorithms; Otsu method; Performance evaluation; Plant diseases; potato disease image; Potatoes; Random noise; Sobel edge detection; Whales & whaling
• ISSN: 2077-0472; 2077-0472
• DOI: 10.3390/agriculture15111123

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 S-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.