轻量模型+真实数据，让手机端快速识别甘蔗叶病
SugarcaneShuffleNet: A Very Fast, Lightweight Convolutional Neural Network for Diagnosis of 15 Sugarcane Leaf Diseases
- 用轻量化网络SugarcaneShuffleNet实现低资源部署
- 98.02%准确率，单图推理仅需4.14毫秒
- 支持田间实时诊断，带可解释性热力图
尽管人工智能在植物病害诊断方面取得进展，但资源匮乏地区的甘蔗农民仍易受叶病影响，因缺乏可扩展、高效且可解释的工具。许多深度学习模型在真实条件下泛化能力差，计算需求高，限制了其在资源有限区域的应用。本文提出SugarcaneLD-BD数据集，包含638张经专家验证的甘蔗叶病图像，涵盖5个类别，来自孟加拉国多样的田间条件；提出SugarcaneShuffleNet轻量模型，实现快速本地诊断；并开发SugarcaneAI渐进式网页应用用于田间部署。该9.26 MB模型达98.02%准确率，F1-score为0.98，平均单图推理时间仅4.14毫秒。对比其他5个轻量模型（MnasNet、EdgeNeXt、EfficientNet-Lite、MobileNet、SqueezeNet），SugarcaneShuffleNet在精度与效率间表现最佳。通过融合额外数据集增强多样性。集成梯度加权类激活映射（Grad-CAM）提供诊断解释。整体方案适用于光照、背景和设备差异大的实际农田场景。
原文摘要 · Abstract (English)
Despite progress in AI-based plant diagnostics, sugarcane farmers in low-resource regions remain vulnerable to leaf diseases due to the lack of scalable, efficient, and interpretable tools. Many deep learning models fail to generalize under real-world conditions and require substantial computational resources, limiting their use in resource-constrained regions. In this paper, we present SugarcaneLD-BD, a curated dataset for sugarcane leaf-disease classification; SugarcaneShuffleNet, an optimized lightweight model for rapid on-device diagnosis; and SugarcaneAI, a Progressive Web Application for field deployment. SugarcaneLD-BD contains 638 curated images across five classes, including four major sugarcane diseases, collected in Bangladesh under diverse field conditions and verified by expert pathologists. To enhance diversity, we combined SugarcaneLD-BD with two additional datasets, yielding a larger and more representative corpus. Our optimized model, SugarcaneShuffleNet, offers the best trade-off between speed and accuracy for real-time, on-device diagnosis. This 9.26 MB model achieved 98.02% accuracy, an F1-score of 0.98, and an average inference time of 4.14 ms per image. For comparison, we fine-tuned five other lightweight convolutional neural networks: MnasNet, EdgeNeXt, EfficientNet-Lite, MobileNet, and SqueezeNet via transfer learning and Bayesian optimization. MnasNet and EdgeNeXt achieved comparable accuracy to SugarcaneShuffleNet, but required significantly more parameters, memory, and computation, limiting their suitability for low-resource deployment. We integrate SugarcaneShuffleNet into SugarcaneAI, delivering Grad-CAM-based explanations in the field. Together, these contributions offer a diverse benchmark, efficient models for low-resource environments, and a practical tool for sugarcane disease classification. It spans varied lighting, backgrounds and devices used on-farm
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。