Crop protection from diseases through applications of plant protection products is crucial to secure worldwide food production. Nevertheless, sustainable management of plant diseases is an open challenge with a major role in the economic and environmental impact of agricultural activities. A primary contribution is expected to come from precision crop protection approaches, with treatments tailored to spatial and time-specific needs of the crop, in contrast to the current practice of applying treatments uniformly to fields. In view of this, image-based automatic detection of early disease symptoms is considered a key enabling technology for high throughput scouting of the crop, in order to timely target the treatments on emerging infection spots. Thanks to the unprecedented performance in image-recognition problems, Deep Learning (DL) methods based on Convolutional Neural Networks (CNNs) have recently entered the domain of plant disease detection. This work develops two DL approaches for automatic recognition of powdery mildew disease on cucumber leaves, with a specific focus on exploring unsupervised techniques to overcome the need of large training set of manually labelled images. To this aim, autoencoder networks were implemented for unsupervised detection of disease symptoms through: i) clusterization of features in a compressed space; ii) anomaly detection. The two proposed approaches were applied to multispectral images acquired during in-vivo experiments, and the obtained results were assessed by quantitative indices. The clusterization approach showed only partially capability to provide accurate disease detection, even if it gathered some relevant information. Anomaly detection showed instead to possess a significant potential of discrimination which could be further exploited as a prior step to train more powerful supervised architectures with a very limited number of labelled samples.

中文翻译：

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用于自动检测植物病害的无监督深度学习技术：减少植物图像手动标记的需要

通过应用植物保护产品来保护作物免受疾病侵害对于确保全球粮食生产至关重要。然而，植物病害的可持续管理是一项公开的挑战，在农业活动的经济和环境影响中起着重要作用。预计主要贡献将来自精确的作物保护方法，根据作物的空间和时间特定需求量身定制处理方法，这与目前在田间统一应用处理方法的做法形成鲜明对比。鉴于此，基于图像的早期病害症状自动检测被认为是作物高通量侦察的关键使能技术，以便及时针对新出现的感染点进行治疗。由于在图像识别问题上前所未有的表现，基于卷积神经网络 (CNN) 的深度学习 (DL) 方法最近进入了植物病害检测领域。这项工作开发了两种 DL 方法来自动识别黄瓜叶片上的白粉病，特别侧重于探索无监督技术，以克服对大量手动标记图像训练集的需求。为此，实现了自动编码器网络以通过以下方式无监督地检测疾病症状：i）压缩空间中的特征聚类；ii) 异常检测。将所提出的两种方法应用于体内实验期间获取的多光谱图像，并通过定量指标评估获得的结果。聚类方法仅显示出提供准确疾病检测的部分能力，即使它收集了一些相关信息。相反，异常检测显示出具有显着的歧视潜力，可以进一步利用它作为先验步骤，用非常有限的标记样本训练更强大的监督架构。