TinyBayes让边缘设备实时识别可可叶病害，模型仅9.5MB且带不确定性分析。
TinyBayes: Closed-Form Bayesian Inference via Jacobi Prior for Real-Time Image Classification on Edge Devices
- 用雅可比先验实现闭式贝叶斯分类，无需迭代计算
- 在9.5MB模型下达78.7%准确率，单图推理<150ms
- 适合资源受限的农业边缘部署，支持无网运行
可可（Theobroma cacao）是西非数百万小农户的重要经济作物，但可可肿枝病毒病（CSSVD）和炭疽病造成严重减产。从叶片图像自动检测病害对早期干预至关重要，但在资源受限环境下部署需模型小型化、快速且无需联网。现有边缘植物病害系统多采用端到端深度学习而缺乏不确定性量化，而面向边缘的贝叶斯方法则聚焦硬件架构而非农业应用。我们提出TinyBayes，首个将闭式贝叶斯分类器与移动端计算机视觉流水线结合的作物病害检测框架。该流程使用YOLOv8-Nano（5.9 MB）进行病斑定位，MobileNetV3-Small（3.5 MB）提取特征，并引入雅可比先验（Jacobi prior）——一种通过投影实现闭式非迭代估计的贝叶斯方法。雅可比-DMR（分布式多项式回归）分类器仅增加13.5 KB，使总模型大小控制在9.5 MB以内，在Amini可可污染挑战数据集上达到78.7%准确率，支持端到端CPU推理时间低于150毫秒/图。我们对比了七种分类器（随机森林、SVM、岭回归、Lasso、弹性网络、XGBoost、雅可比-GP），证明雅可比-DMR在精度、模型尺寸与推理速度间具有最优权衡。已验证其渐近等价性、一致性、渐近正态性及偏差校正性。所有数据与代码见：https://github.com/shouvik-sardar/TinyBayes
原文摘要 · Abstract (English)
Cocoa (Theobroma cacao) is a critical cash crop for millions of smallholder farmers in West Africa, where Cocoa Swollen Shoot Virus Disease (CSSVD) and anthracnose cause devastating yield losses. Automated disease detection from leaf images is essential for early intervention, yet deploying such systems in resource-constrained settings demands models that are small, fast, and require no internet connectivity. Existing edge-deployable plant disease systems rely on end-to-end deep learning without uncertainty quantification, while Bayesian methods for edge devices focus on hardware-level inference architectures rather than agricultural applications. We bridge this gap with TinyBayes, the first framework to combine a closed-form Bayesian classifier with a mobile-grade computer vision pipeline for crop disease detection. Our pipeline uses YOLOv8-Nano (5.9 MB) for lesion localisation, MobileNetV3-Small (3.5 MB) for feature extraction, and the Jacobi prior; a Bayesian method that provides a closed form non-iterative estimators via projection, for the classification. The Jacobi-DMR (Distributed Multinomial Regression) classifier adds only 13.5 KB to the pipeline, bringing the total model size within 9.5 MB, while achieving 78.7% accuracy on the Amini Cocoa Contamination Challenge dataset and enabling end-to-end CPU inference under 150 ms per image. We benchmark against seven classifiers including Random Forest, SVM, Ridge, Lasso, Elastic Net, XGBoost, and Jacobi-GP, and demonstrate that the Jacobi-DMR offers the best trade-off between accuracy, model size, and inference speed for edge deployment. We have proved the asymptotic equivalence and consistency, asymptotic normality and the bias correction of Jacobi-DMR. All data and codes are available here: https://github.com/shouvik-sardar/TinyBayes
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