ENHANCED CROSS-CROP PLANT DISEASE RECOGNITION MODEL VIA DOMAIN-SPECIFIC ALIGNMENT NETWORK AND ELASTIC-MIXUP
DOI:
https://doi.org/10.15282/Keywords:
Domain Shift, Plant Disease Recognition, DsAN, Elastic Mixup, Semantic Embedding, Zero-Shot LearningAbstract
ABSTRACT - Reliable identification of crop diseases across different species is often limited by variations in imaging conditions and feature distribution differences between crops. This study specifically targets cross-crop and zero-shot recognition settings where target-domain labels are unavailable, introducing a domain-invariant plant-disease recognition framework that combines Elastic-Mixup and a Domain-Specific Alignment Network (DsAN). A pre-trained MobileNetV2 acts as the visual encoder, producing 1280-dimensional features projected into a 300-dimensional semantic space derived from Word2Vec representations of disease names. The Elastic-Mixup module jointly interpolates visual and semantic embeddings under a shared Beta (0.4, 0.4) distribution, promoting smoother transitions and richer feature diversity across domains. Meanwhile, the DsAN aligns class-conditional subdomains through a localized maximum-mean-discrepancy criterion and a weak gradient-reversal mechanism, enabling balanced adaptation without margin collapse. Comprehensive experiments on the PlantVillage dataset (tomato to potato transfer) demonstrate the effectiveness of the proposed approach, achieving 97.58 % accuracy, 96.60 % precision, 96.59 % recall, and 96.59 % F1-score, representing an 88.9 % reduction in classification error compared to existing zero-shot transfer methods. These results highlight the study’s principal contribution demonstrating that the cooperative integration of Elastic-Mixup and DsAN yields a scalable, semantically consistent, and domain-invariant solution for intelligent agricultural diagnostics.
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