Abstract
Abstract
Crop disease management calls for integrated sensor networks combining multiple phenotyping modalities for timely detection and continuous agronomic surveillance. Prevailing agricultural informatics systems are limited by reliance on single-source datasets such as optical imagery or satellite spectroscopy, considerably limiting phenotypic inference and neglecting inter-field epidemic transmission pathways. AGRISIGHT AI tackles this challenge by proposing a ConvNeXtV2-based deep learning classifier for RGB crop disease image classification, validated on the PlantVillage dataset. The broader architecture is designed to extend toward a multimodal framework assimilating drone-mounted RGB cameras, Sentinel-2 multispectral acquisitions, meteorology datasets, and soil geodatabases; these additional modalities, along with physics-informed (PINN/PROSAIL) and graph-based (GNN) components, are proposed future additions and are not part of the empirical validation reported here. The ConvNeXtV2 classifier achieves 91.50% classification accuracy and a 90.55% F1-score on PlantVillage test data, with a ROC-AUC of 0.999, and these results were supported by statistical significance testing across repeated runs. Model interpretability is assessed using Grad-CAM and Score-CAM visualizations to confirm the network attends to disease-relevant leaf regions. AGRISIGHT AI displays a strong RGB-based disease classification performance and establishes a modular architecture that can be extended to a multimodal agricultural monitoring incorporating satellite, weather, soil, and graph-based spatial reasoning in future work.