Article
Plant disease detection using quantum image processing
Plant diseases pose a significant threat to agricultural productivity and food security, making early and accurate detection essential. Traditional image processing and machine learning techniques often struggle to handle variations in lighting, background noise, and complex disease patterns. To address these challenges, this paper proposes a hybrid plant disease prediction system that integrates quantum-inspired image processing with deep neural networks. The proposed approach utilizes angle-based quantum encoding to transform leaf images into quantum-like representations, enabling efficient and discriminative feature extraction. Statistical measurements derived from these representations are combined with color and texture features to form a robust feature set. These features are then fed into a deep neural network model for multi-class disease classification. The system is designed to be scalable and robust, capable of handling diverse plant disease categories under varying environmental conditions. Experimental results demonstrate that the proposed method significantly improves classification accuracy and reliability compared to conventional approaches. The developed framework can be effectively used in real-world agricultural decision support systems for early disease diagnosis and crop management.
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