Rice leaf disease (RLD) is one of the major factors that cause the decline in production, and the automatic recognition of such diseases under natural field conditions is of great significance for timely targeted rice management. Although many machine learning approaches have been proposed for RLD recognition, scale variation is still a challenging problem that affects prediction accuracy, especially in uncontrolled environments, such as natural fields. Also, the existing RLD data sets are collected in laboratory environments or with a constant scale, which cannot be used to develop the RLD classification algorithms under natural field conditions. To tackle these particular challenges, we propose a multiscale voting mechanism for RLD recognition under natural field conditions. First, data from 26 rice fields were collected to build a data set containing 6046 images of RLD. Afterwards, a feature pyramid was embedded into a mainstream classification architecture (EfficientNet) with a bottom-up and top-down pathway for feature fusion at different scales. To further reduce the inconsistency among multiscaled features, a multiscale voting strategy with regard to probability distribution was proposed to integrate the decisions from various scales. Each proposed module was carefully validated through an ablation study to demonstrate its effectiveness, and the proposed method was compared with a few state-of-the-art algorithms, including the Single Shot MultiBox Detector, Feature Pyramid Networks, Path Aggregation Network, and Bidirectional Feature Pyramid Network. Experimental results have shown that the classification accuracy of our model can reach 90.24%, which is 4.48% higher than that of the original EfficientNet-b0 model and 1.08% higher than that of existing multiscale networks. Finally, we exploit and demonstrate a visualized explanation for the boosted performance from the proposed model. As an extra outcome, our data set and codes are available at http://github.com/huanghsheng/multiscale-voting-mechanism to benefit the whole research community.

中文翻译：

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自然田间条件下水稻叶病识别的多尺度投票机制

水稻叶病（RLD）是导致水稻减产的主要因素之一，在自然田间条件下自动识别此类病害对水稻及时进行针对性管理具有重要意义。尽管已经提出了许多用于 RLD 识别的机器学习方法，但尺度变化仍然是一个影响预测准确性的具有挑战性的问题，尤其是在自然领域等不受控制的环境中。此外，现有的 RLD 数据集是在实验室环境中或以恒定比例收集的，不能用于开发自然野外条件下的 RLD 分类算法。为了应对这些特殊挑战，我们提出了一种在自然场条件下识别 RLD 的多尺度投票机制。第一的，收集了 26 个稻田的数据，构建了一个包含 6046 张 RLD 图像的数据集。之后，特征金字塔被嵌入到主流分类架构（EfficientNet）中，具有自下而上和自上而下的路径，用于不同尺度的特征融合。为了进一步减少多尺度特征之间的不一致性，提出了一种关于概率分布的多尺度投票策略来整合来自不同尺度的决策。每个提出的模块都通过消融研究进行了仔细验证，以证明其有效性，并将提出的方法与一些最先进的算法进行了比较，包括单发多盒检测器、特征金字塔网络、路径聚合网络和双向特征金字塔网络。实验结果表明，我们模型的分类准确率可以达到90.24%，比原来的EfficientNet-b0模型提高了4.48%，比现有的多尺度网络提高了1.08%。最后，我们利用并展示了对所提出模型的提升性能​​的可视化解释。作为额外的成果，我们的数据集和代码可在 http://github.com/huanghsheng/multiscale-voting-mechanism 上获得，以造福整个研究社区。