Hybrid Optimized AgriShieldNet Framework for an Interpretable Multi‐Crop Leaf Disease Classification

ABSTRACT Early detection of plant leaf diseases is essential for improving crop productivity, reducing financial losses, and enhancing sustainability in modern agricultural practices. However, most automated systems struggle to accurately capture diverse morphological variations of plant leaf diseases across multiple crop types, while manual inspection methods are time‐consuming, labor‐intensive, and prone to human error. To address these challenges, this paper proposes Hy‐OptiASNet, a hybrid optimized deep learning framework for interpretable multi‐crop leaf disease classification. The proposed framework utilizes EfficientNet‐B0 for robust spatial feature extraction; a Long Short‐Term Memory (LSTM) network is employed to model sequential feature dependencies and improve structured feature learning, followed by a Morphology‐Aware Vision Embedding (MAVE) module to enhance representation of fine‐grained morphological disease patterns. In addition, a novel Sandpiper Optimization Algorithm (SPOA) is incorporated for feature refinement and hyperparameter optimization, thereby improving generalization capability and classification stability. To enhance interpretability, visualization techniques are used to highlight biologically relevant disease regions, enabling improved understanding of model decisions. Extensive experiments were conducted on multi‐crop datasets consisting of apple, tomato, and grape leaf images. The experimental results demonstrate that the proposed Hy‐OptiASNet framework achieves classification accuracies of 98.98%, 98.21%, and 98.35% for apple, tomato, and grape datasets, respectively, outperforming several existing state‐of‐the‐art methods. These findings indicate that the proposed framework provides an effective and reliable solution for precision agriculture and real‐world plant disease monitoring applications.