{
  "annotation_id": "ae322136-4e27-4827-a27e-b075b9de11e3",
  "date_created": "2026-02-17T05:53:12.527000Z",
  "date_modified": "2026-02-17T05:53:12.527000Z",
  "file_hash": "15d69768c7f03485c218f6a1ec8c2c3f5c13f97f63d4dd9eb8d914e7e42cb61d",
  "private": false,
  "record": {
    "abstract": "Maize disease classification plays a vital role in mitigating yield losses and ensuring food security. However, the deployment of traditional disease detection models in resource-constrained environments, such as those using smartphones and drones, faces challenges due to high computational costs. To address these challenges, we propose LWMSCNN-SE, a lightweight convolutional neural network (CNN) that integrates multi-scale feature extraction, depthwise separable convolutions, and squeeze-and-Excitation (SE) attention mechanisms. This novel combination enables the model to achieve 96.63% classification accuracy with only 241,348 parameters and 0.666 GFLOPs, making it suitable for real-time deployment in field applications. Our approach addresses the accuracy--efficiency trade-off by delivering high accuracy while maintaining low computational costs, demonstrating its potential for efficient maize disease diagnosis on edge devices in precision farming systems.",
    "arxiv_id": "2601.07957",
    "authors": [
      "Fikadu Weloday",
      "Jianmei Su"
    ],
    "categories": [
      "cs.CV",
      "cs.AI"
    ],
    "license": "http://arxiv.org/licenses/nonexclusive-distrib/1.0/",
    "title": "LWMSCNN-SE: A Lightweight Multi-Scale Network for Efficient Maize Disease Classification on Edge Devices",
    "url": "https://arxiv.org/abs/2601.07957",
    "version": "v1"
  },
  "schema_id": "dorsal/arxiv",
  "source": {
    "execution_id": "dc18d668-80e7-4c0d-a99f-8e8616b02826",
    "id": "arXiv Dataset",
    "type": "Model",
    "variant": "snapshot-2026-01-17",
    "version": "0.1.0"
  },
  "user_id": 1000002
}