ABSTRACT The early detection of foliar diseases such as early blight and late blight is crucial for securing potato yield and quality, yet traditional manual diagnosis remains labor‐intensive, inconsistent, and unsuitable for large‐scale agricultural settings. To address this, the present study investigates a CNN‐based automatic recognition framework for potato leaf diseases using RGB imagery, with a focus on enhancing both model generalization and deployment feasibility. Rather than proposing a novel network architecture, this work systematically benchmarks three representative backbones—AlexNet, MobileNetV3‐Large, and ResNet152—under a unified training pipeline. Two methodological innovations are introduced: (1) a composite Mixup–CutMix data augmentation strategy tailored for small‐scale agricultural datasets, and (2) an ant colony optimization (ACO)‐driven training strategy that jointly tunes transfer learning, L2 regularization, and Dropout. The dataset, expanded from 3000 to 18,000 images through augmentation, supports robust training under conditions of occlusion, lighting variation, and background clutter. Experimental results show that the Mixup–CutMix augmentation improves baseline accuracy by 3.3 percentage points, while the ACO‐optimized training pipeline contributes an additional 1.2 points. ResNet152 achieved the highest performance with 99.83% accuracy and an F1‐score of 0.9982 on a hold‐out test set. Grad‐CAM visualizations confirm biologically meaningful attention to lesion areas. The best‐performing model has been deployed in a lightweight PyQt5‐based GUI, and a compressed variant runs successfully in real time on Raspberry Pi 4B. This study contributes a robust augmentation‐training pipeline, an interpretable and deployable diagnostic system, and a demonstration of feasibility on edge hardware. Future work will extend the model to additional pathogens, quantify on‐device latency and FPS, and integrate multi‐modal cues for broader crop health monitoring.

Seed expansion tasks play an important role in various network applications such as recommendation systems, social network analysis, and bioinformatics. Given a network and a small group of examples as seeds, these tasks involve identifying additional members of interest from the same community. While most existing expansion methods focus on defining a fixed metric function based on the network structure alone, they often overlook the rich content associated with nodes in attributed networks.