An Ensemble of EfficientNetV2B3 and EfficientNetB4 for Crop Disease Detection Using the PlantVillage Dataset

Abstract

Crop diseases continue to threaten global food security, causing annual yield losses of 20–40% worldwide. Farmers in developing nations often lack timely access to ex-pert diagnosis, leading to delayed interventions and reduced harvests. This study presents a deep learning-based solution that automates crop disease identification using leaf images. We trained and evaluated two state-of-the-art convolutional neural networks—EfficientNetV2B3 and EfficientNetB4—on the publicly available PlantVillage dataset, which contains 54,303 images spanning 38 disease categories across 14 crop species. To improve classification robustness, we developed an ensemble model that combines the predictions of both architectures via weighted averaging. EfficientNetV2B3 achieved 98.0% accuracy individually, while EfficientNetB4 reached 94.0%. The proposed ensemble model attained an accuracy of 98.5% and an area under the curve (AUC) of 0.98, outperforming both parent models and several established baselines, including VGG16, ResNet50, InceptionV3, MobileNetV2, and DenseNet121. Beyond model development, we deployed the ensemble inside a Flask-based web application with user authentication, confidence scoring, and a searchable disease knowledge base. This end-to-end system bridges the gap between research and practice, offering farmers an accessible tool for rapid, reliable disease diagnosis.

Plant phenotyping relevance

葉画像から植物病害を分類する深層学習モデルを開発・評価し、実用的な診断アプリにも実装しているため、植物の病害状態を対象とするフェノタイピング手法が中心である。

Code and data availability

The paper uses the public PlantVillage dataset, but that is a generic benchmark, not a paper-specific asset. No author code, trained model checkpoints, or data deposit with a public URL is mentioned anywhere in the supplied blocks; only hyperparameters and splits are documented.

No evidence-backed public reproduction asset is currently recorded.