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

翻译：可可（Theobroma cacao）是西非数百万小农户的关键经济作物，而可可肿枝病毒病（CSSVD）和炭疽病导致毁灭性产量损失。利用叶片图像实现自动化病害检测对早期干预至关重要，然而在资源受限环境中部署此类系统要求模型体积小、速度快且无需互联网连接。现有可部署于边缘设备的植物病害系统依赖端到端深度学习缺乏不确定性量化，针对边缘设备的贝叶斯方法则侧重于硬件级推理架构而非农业应用。我们通过TinyBayes填补了这一空白，这是首个将封闭形式贝叶斯分类器与移动级计算机视觉流水线相结合的作物病害检测框架。该流水线采用YOLOv8-Nano（5.9 MB）进行病斑定位，MobileNetV3-Small（3.5 MB）提取特征，并利用雅可比先验（一种通过投影提供封闭形式非迭代估计量的贝叶斯方法）进行分类。雅可比-DMR（分布式多项式回归）分类器仅为流水线增加13.5 KB参数，使模型总大小控制在9.5 MB以内，同时在Amini可可污染挑战数据集上达到78.7%的准确率，并在CPU上实现每张图像低于150毫秒的端到端推理。我们与包括随机森林、支持向量机、岭回归、套索回归、弹性网络、XGBoost及雅可比-高斯过程在内的七种分类器进行基准测试，证明雅可比-DMR在边缘部署中实现了准确率、模型大小与推理速度的最佳权衡。本文证明了雅可比-DMR的渐近等价性与一致性、渐近正态性及偏差校正。所有数据和代码已开源：https://github.com/shouvik-sardar/TinyBayes