同时预测植物种类与病害，提升农业图像识别准确率。
Multi-output Deep-Supervised Classifier Chains for Plant Pathology
- 用链式结构联合预测植物种类和病害类型
- 在Plant Village和PlantDoc数据集上准确率与F1值更优
- 适合智能农业中多标签植物病害检测场景
植物叶片疾病分类是智慧农业中的关键任务，对可持续生产具有重要意义。现代机器学习方法在此任务中展现出巨大潜力，可节省时间和成本。然而，现有方法多直接使用卷积神经网络，未充分考虑植物种类与病害类型之间的关联对预测性能的影响。本文提出一种新模型——多输出深度监督分类链（Mo-DsCC），通过串联两个标签的输出层，联合预测植物种类与病害。Mo-DsCC由三部分组成：改进的VGG-16骨干网络、深度监督训练机制以及分类链堆叠结构。在Plant Village和PlantDoc两个基准数据集上进行大量实验，结果表明，相比最新的多模型、多标签（Power-set）、多输出和多任务方法，Mo-DsCC在准确率和F1-score上均表现更优。实证研究显示，Mo-DsCC在智慧农业中具有应用价值，可为农场带来实际效益，并为产业与学术界提供新思路。
原文摘要 · Abstract (English)
Plant leaf disease classification is an important task in smart agriculture which plays a critical role in sustainable production. Modern machine learning approaches have shown unprecedented potential in this classification task which offers an array of benefits including time saving and cost reduction. However, most recent approaches directly employ convolutional neural networks where the effect of the relationship between plant species and disease types on prediction performance is not properly studied. In this study, we proposed a new model named Multi-output Deep Supervised Classifier Chains (Mo-DsCC) which weaves the prediction of plant species and disease by chaining the output layers for the two labels. Mo-DsCC consists of three components: A modified VGG-16 network as the backbone, deep supervision training, and a stack of classification chains. To evaluate the advantages of our model, we perform intensive experiments on two benchmark datasets Plant Village and PlantDoc. Comparison to recent approaches, including multi-model, multi-label (Power-set), multi-output and multi-task, demonstrates that Mo-DsCC achieves better accuracy and F1-score. The empirical study in this paper shows that the application of Mo-DsCC could be a useful puzzle for smart agriculture to benefit farms and bring new ideas to industry and academia.
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