The rising incidence of plant diseases seriously threatens agricultural productivity. Early and precise identification is crucial to minimize production loss, but real-world recognition is hindered by fluctuating lighting, cluttered backdrops, and the domain gap between lab and field imagery. We offer a drone-assisted plant disease diagnostic and remedy prescription system emphasizing deployability, reproducibility, and dependability in order to overcome these difficulties. To avert near-duplicate overlap and provide leakage-safe data partitions, our mechanism uses perceptual hashing (pHash). We employ EfficientNetV2-M in our staged training pipeline, which consists of a final PlantDOC (PD)-only adaptation stage and gradual fine-tuning. In comparison to earlier research, where the accuracy rates are 90-99%, our work takes a different and practical approach. Together with domain-specific and per-class studies, we present accuracy and some evaluation protocols for examples like macro-F1, macro-AUC, and calibration measures like Expected Calibration Error (ECE). Our optimal ensemble model has a macro-AUC of 0.883, a macro-F1 of 0.310, and an ECE of 0.040, demonstrating strong class-ranking capability despite the significant domain shift. Additionally, the system incorporates a Streamlit interface that provides real-time drone-based live broadcasting and it also integrates a rule-based remedy module that maps predicted diseases to agriculturally expert-validated chemical prescriptions via a lookup table. Due to hardware limitations, field-level drone testing was not carried out; nonetheless, GPU latency evaluations verified near-real-time viability.