In developing countries, the economy largely depends on agriculture. Crops such as tomato, potato, and chili play a crucial role in ensuring food security and economic stability, making their proper maintenance essential. However, leaf diseases caused by bacteria, fungi, or viruses pose significant threats to crop yield, particularly for small-scale farmers. To address this challenge, we propose a lightweight model, Augmented Enhanced Convolutional Neural Network with Attention (AECNN-A), designed for mobile-friendly and effective plant leaf disease classification. The architecture consists of two convolutional blocks integrated with MaxPooling, Dropout, and an additive attention mechanism, which emphasizes diseased regions to extract more discriminative features. The model can be deployed in real-time and offline on low-resource devices. Performance evaluation was conducted using the PlantVillage dataset (Kaggle), comprising 20,638 images of tomato, potato, and chili leaves across 15 classes, including both healthy and diseased samples. During preprocessing, images were encoded, normalized, and augmented on-the-fly with random flipping, rotation, zoom, and contrast adjustments. AECNN-A was evaluated using accuracy, macro F1-score, and confusion matrix metrics, achieving a test accuracy of 97.50\% and a micro F1-score of 0.98, outperforming models such as CNN, VGG16, VGG19, ResNet50, and FVBC in computational efficiency and classification accuracy. These results demonstrate that AECNN-A enables edge-based devices, real-time, and accurate plant disease detection, offering practical solutions for farmers. It's efficiency and rapid performance make it highly suitable for future research and applications in precision agriculture.