Design of an Improved Model Using Neuro-Symbolic Encoding and Federated Meta-Adaptation for Plant Disease Detection and Explanation Process

Growing plant diseases, aggravated by climate change and by intensive agricultural practices, continue to warrant the systems for detecting diseases that are robustly interpretable and generalizable. The conventional methods in deep learning are exceedingly accurate, but they fail to capture phenotypic subtlety within the limit of the context of fixed settings. They are also not able to treat the data imbalance or transfer in adaptation for many diverse geographies, or interpretability and actionability, which are requirements for real-world deployment in multifunctional heterogeneous agro-ecological settings. NeuroCausal-FusionNet, a new architecture in a framework and multimodal for end-to-end detection and explanation of plant diseases, is aimed at addressing these limitations. Starting with Multimodal Neuro-Symbolic Reasoning Encoder (MNSRE), it is then coupled with a phenotype knowledge graph to generate biologically-informed latent embeddings where CNN/ViT-based visual features are integrated. Self-Supervised Phyto-Latent Clustering (SSPLC) is then used to process these embeddings. It captures unusual and emergent disease phenotypes through contrastive learning and morphological-aware clustering and thus making class imbalance sets smaller. The Federated Meta-Adaptation with Real-Time Feedback on Crops (FMA-RCF) module also ensures power and cross-region generalizability by decentralized training on IoT-based signals of crop health sets.