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 < 0.05 while maintaining substantially lower inference time.We further discuss generalization limitations due to the controlled nature of PlantVillage and moderate claims regarding edge deployment feasibility based on model size and computational profiling. The study provides a statistically grounded and edge-aware benchmarking framework for plant disease classification models.