RDHCNet – Residual Depthwise Hybrid Convolutional Network for Robust Crop Disease Diagnosis

Abstract

Crop diseases are a major danger to the world's food security because they reduce crop productivity and farmer revenue. Early detection and preventative measures can reduce these losses. This study suggests CropNet Hybrid, a deep learning model that can identify 38 crop disease classes in a variety of plant species after being trained on the PlantVillage dataset. In contrast to earlier research that only looks at classification, our system incorporates a carefully chosen knowledge-based prevention module, giving farmers practical advice. On test data, the model, which was implemented as a hybrid CNN architecture with depthwise separable convolutions and residual blocks, achieved an accuracy of more than 93.27%. The framework is a useful tool for smart agriculture since it is implemented as a FastAPI microservice and offers real-time detection and prevention guidance.

Plant phenotyping relevance

植物画像から病害状態を分類する深層学習モデルの開発が中心であり、植物病害フェノタイピング手法に該当する。

Code and data availability

The paper uses the public PlantVillage dataset, but that is a generic third-party benchmark, not a paper-specific asset. No author code, model checkpoints, or data deposits are mentioned; the FastAPI file (main_fastapi_app.py) is named but no public URL or availability statement is given. allowed_urls is empty, so no e

No evidence-backed public reproduction asset is currently recorded.