Abstract
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
Abstract (translated)
尽管基于人工智能的植物诊断技术取得了进展，但资源匮乏地区甘蔗种植者仍易受到叶片疾病的威胁，因为缺乏可扩展、高效且易于解释的工具。许多深度学习模型在实际条件下的泛化能力较差，并需要大量计算资源，这限制了它们在资源受限地区的应用。 本文介绍了SugarcaneLD-BD（用于甘蔗叶病分类的数据集）、SugarcaneShuffleNet（一种优化后的轻量级模型，可在设备上快速进行诊断）和SugarcaneAI（一款适用于现场部署的渐进式Web应用程序）。SugarcaneLD-BD包含638张经过精心挑选的图像，涵盖五个类别，包括四种主要的甘蔗疾病，在孟加拉国的各种田间条件下收集，并由专家病理学家进行了验证。为了增强多样性，我们结合了两个额外的数据集，形成了一个更大、更具代表性的数据集合。 我们的优化模型SugarcaneShuffleNet在实时设备上诊断的速度和准确性之间提供了最佳的权衡。这款9.26MB大小的模型达到了98.02%的准确率，F1评分为0.98，并且每张图像的平均推理时间为4.14毫秒。作为比较，我们通过迁移学习和贝叶斯优化对另外五种轻量级卷积神经网络（MnasNet、EdgeNeXt、EfficientNet-Lite、MobileNet 和 SqueezeNet）进行了微调。尽管MnasNet和EdgeNeXt在准确率方面与SugarcaneShuffleNet相当，但它们需要更多的参数、内存及计算资源，限制了其在低资源配置环境中的适用性。 我们将SugarcaneShuffleNet集成到SugarcaneAI中，在现场提供了基于Grad-CAM的解释。这些贡献共同提供了一个多样化的基准测试、适用于低资源环境的有效模型以及一个实用的甘蔗疾病分类工具。它涵盖了各种照明条件、背景和田间使用的设备。
URL
https://arxiv.org/abs/2508.17107
