A Hierarchical Deep Learning Framework for Robust Cassava Crop Verification and Disease Diagnosis

Abstract

Abstract Cassava represents a crucial staple crop in sub-Saharan Africa, whose productivity is severely threatened by viral and bacterial diseases. While deep learning approaches show promise for automated disease detection, they often operate under unrealistic assumptions and suffer from dataset limitations including class imbalance and label noise. This paper presents a novel hierarchical deep learning framework that addresses these challenges through a three-stage pipeline: crop verification, health assessment, and disease classification. Our approach achieves computational efficiency through conditional execution while maintaining diagnostic accuracy. On the Cassava Leaf Disease Classification dataset comprising 21,397 images, our framework achieves 91.8\% overall accuracy with statistically significant improvements over flat classification baselines (mean difference: 1.7\%, bootstrap 95\% CI: 1.2-2.2\%, p

Code and data availability

The paper claims complete reproducibility via a public GitHub repository with code, model weights, and dataset splits, but no repository URL or identifier is provided anywhere in the supplied blocks, and no allowed URL corresponds to an authors' deposit. The Cassava Leaf Disease Classification dataset and PlantVillage,

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