A Lightweight Deep Learning Architecture for Potato Leaf Disease Detection: A Comprehensive Survey

Abstract

Potato leaf diseases pose a serious challenge to global food security, often leading to considerable yield losses if not detected promptly. The growing maturity of deep learning has enabled automated, high-precision plant disease recognition, even on devices with limited computational resources. In this study, several lightweight convolutional neural network (CNN) models--MobileNetV3 (Small and Large), EfficientNet-Lite, ShuffleNet, and SqueezeNet--are comparatively assessed for the task of potato leaf disease classification. The models were trained under identical preprocessing and fine-tuning conditions, incorporating checkpoint-based training for stability. Among the evaluated networks, ShuffleNet delivered the highest overall performance with 99% accuracy, 0.97 precision, 0.99 recall, and an F1-score of 0.98, making it well-suited for real-time field deployment. EfficientNet-Lite also demonstrated a strong balance between speed and accuracy (91.9%), outperforming both MobileNet variants. Conversely, SqueezeNet, though the most compact model, recorded lower metrics (76% accuracy), indicating limited feature discrimination capability. This analysis underscores the balance between efficiency, robustness, and predictive accuracy, providing practical insights for deploying deep learning models in precision agriculture and low-resource environments.