Maize seed classification is a pivotal task in modern agriculture, directly impacting crop productivity and food security. This research investigates application of learning with attention for maize seed classification, with a focus on enhancing accuracy and efficiency. A comparative analysis of four models is performed – VGG16, Inceptionv3, ResNet50, and the proposed model (ResNet50 enhanced with the Convolutional Block Attention Module). Experimentation involved training and evaluating these models on a comprehensive dataset comprising “Good” and “Bad” maize seed images. The results of experiments demonstrated the performance disparities among the models, shedding light on their strengths and weaknesses. ResNet50 with CBAM achieved an accuracy of 0.958, precision of 0.965, recall of 0.962, and F1-score of 0.968. The achieved performance can be attributed to the integration of CBAM, which enhances feature representations through attention mechanisms. CBAM allowed the model to focus on salient features within the images, leading to superior classification accuracy.