Fire blight disease causes significant losses in pear orchards. Fire blight infection is accompanied by visual symptoms that can easily be recognized by a grower or adviser, but such visual inspection is time consuming. The present work focused on the use of convolutional neural networks (CNNs) to identify one type of visual symptom (cankers on the main trunk of dormant trees) as well as autumn blooming, which in Israel plays an important role in the epidemiology of Erwinia amylovora—the bacterium responsible for fire blight. Images of dormant trees were acquired with a tripod-mounted DSLR camera while, for autumn blooming detection, the images were acquired using a small unmanned aerial vehicle flying a few meters above the trees. In both cases, several Faster R-CNNs were trained and tested with several datasets acquired at various locations and over several years. Overall, the CNNs for canker detection achieved precision and recall rates that exceeded 90% while, for autumn blooming detection, the precision and recall rates exceeded 80% in all but one case. These trained CNNs were used to analyze automatically geo-references images, hence generating infection/blooming maps. Such maps could be one of the information layers used by growers for managing the orchard, for instance to determine whether winter sanitation is needed and/or if it was carried out properly, or to decide when and where costly manual removal of autumn flowers or copper application is required.

火疫病给梨园造成重大损失。火疫病感染伴有视觉症状，种植者或顾问很容易识别这些症状，但这种视觉检查非常耗时。目前的工作重点是使用卷积神经网络（CNN）来识别一种视觉症状（休眠树主干上的溃疡病）以及秋季开花，这在以色列的梨火疫病菌流行病学中发挥着重要作用——引起火疫病的细菌。休眠树木的图像是通过安装在三脚架上的数码单反相机拍摄的，而为了检测秋季开花，图像是使用在树木上方几米处飞行的小型无人机拍摄的。在这两种情况下，都使用数年在不同地点获取的多个数据集来训练和测试多个 Faster R-CNN。总体而言，用于溃疡病检测的 CNN 实现了超过 90% 的精确度和召回率，而对于秋季开花检测，除了一种情况外，所有情况下的精确度和召回率均超过 80%。这些经过训练的 CNN 用于自动分析地理参考图像，从而生成感染/病毒爆发图。此类地图可能是种植者用于管理果园的信息层之一，