Real‐time crop leaf disease detection and classification using a hybrid Morlet wavelet interactive attention neural network optimized by the Red‐Billed Blue Magpie algorithm

Abstract

Abstract Traditional disease classification is slow and lab‐dependent. Machine learning aids faster image‐based detection but faces challenges like lighting variations, complex leaf shapes, background noise, and limited labeled data. This research develops a robust image‐based method to automatically classify corn, rice, and wheat leaf diseases under diverse environmental and imaging conditions. A Hybrid Morlet Wavelet Interactive Attention Neural Network optimized by the red‐billed blue magpie optimizer (HMWIANN‐RBBMO) is proposed in this study for accurate classification corn ( Zea mays ), rice ( Oryza sativa ), and wheat ( Triticum aestivum ) leaf diseases. First, a modified square‐root SageHusa adaptive Kalman filter is used to remove noise and improve image quality by image preprocessing. The DeepLabV3+ is used to accurately segment disease‐prone areas, and then the Sharpbelly Fish Optimization is used to identify the most discriminative features in the images. The HMWIANN will combine Morlet wavelet transformation with interactive attention to exhaust the capabilities of the classifier to recognize Healthy (No pathogen), Common Rust ( Puccinia sorghi ), Blight ( Xanthomonas oryzae ), Gray Leaf Spot ( Cercospora zeae‐maydis ), BrownSpot ( Bipolaris oryzae ), Hispa ( Dicladispa armigera ), LeafBlast ( Magnaporthe oryzae ), Stripe rust ( Puccinia striiformis ), and septoria ( Zymoseptoria tritici ). Furthermore, the RBBMO will be used to improve convergence speed, generalization, and classification accuracy. A graph‐based hybrid recommendation system is also incorporated to assist disease management decisions. Experimental evaluation on corn, rice, and wheat leaf disease dataset demonstrates superior performance, achieving 99.70% accuracy, 99.80% precision, 99.50% recall, 99.40% F1‐score, and a low false positive rate of 0.8%, outperforming existing state‐of‐the‐art methods.

Plant phenotyping relevance

植物葉の病害症状を画像から分割・特徴抽出・分類する手法を開発し、病害状態という植物表現型を直接推定して性能評価しているため、方法が中心的である。

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