Comparative evaluation of deep learning models for plant disease classification with edge-aware performance analysis

Abstract

Agricultural disease monitoring remains a critical challenge in precision farming, particularly when deploying computer vision systems on resource-constrained platforms. This study presents a rigorous comparative evaluation of four deep learning architectures—ResNet50, DenseNet121, a Binarized Neural Network (BNN), and YOLOv8-cls—for multi-class plant disease classification using the PlantVillage dataset (15 classes). Unlike prior benchmarking studies, we incorporate statistical validation through repeated stratified experiments (5 runs) and report mean ± standard deviation for accuracy, precision, recall, and F1-score. Results show that while DenseNet121 achieves high classification accuracy (99.48)% ± 0.12), it exhibits significantly higher inference latency. The BNN achieves minimal latency but suffers substantial performance degradation (88.31% ± 0.45). YOLOv8-cls provides the best trade-off, achieving 99.64% ± 0.09 accuracy with low latency (3.3 ms ± 0.2). Statistical comparison using paired t-tests confirms that YOLOv8 significantly outperforms ResNet50 p

Plant phenotyping relevance

植物病害を画像から分類する深層学習モデルを比較・反復実験・統計検証しており、植物の病害状態を推定する画像ベース表現型手法の技術評価が中心です。

Code and data availability

The authors' Data Availability statement points to a public Zenodo deposit containing the dataset, source code, and models used for this paper's plant disease classification experiments. The PlantDoc Kaggle dataset and Ultralytics GitHub repositories are cited third-party resources, not paper-specific assets.