Diseases and pests cause serious damage in crop production, reducing yield and fruit quality. Their identification is often time-consuming and requires trained personnel. New sensing technologies and artificial intelligence could be used for automatic identification of disease and pest symptoms on grapevine in precision viticulture. The aim of this work was to apply deep learning modelling and computer vision for the detection and differentiation of downy mildew and spider mite symptoms in grapevine leaves under field conditions. RGB images of grapevine canopy leaves with downy mildew symptoms, with spider mite symptoms and without symptoms were taken under field conditions in a commercial vineyard. The images were prepared using computer vision techniques to increase disease visual features. Finally, deep learning was used to train a model capable of differentiating leaf images of the three classes. An accuracy up to 0.94 (F1-score of 0.94) was obtained by classifying leaves with downy mildew, spider mite and without symptoms at the same time, using a hold-out validation. Additionally, accuracies between 0.89 and 0.91 (F1-scores between 0.89 and 0.91) were obtained in the binary classification of the disease and pest, obtaining the best results in differentiating downy mildew from spider mite symptoms. This high accuracy demonstrates the effectiveness of deep learning and computer vision techniques for the classification of grapevine leaf images taken under field conditions, automatically finding complex features capable of differentiating leaves with spider mite symptoms, with downy mildew symptoms and without any. These results prove the potential of these non-invasive techniques in the detection and differentiation of pests and diseases in commercial crop production.

病虫害严重损害了作物产量，降低了产量和果实品质。识别它们通常很耗时，需要训练有素的人员。新的传感技术和人工智能可用于在精密葡萄栽培中自动识别葡萄上的病虫害症状。这项工作的目的是应用深度学习建模和计算机视觉技术来检测和区分田间条件下葡萄叶中的霜霉病和红蜘蛛症状。在野外条件下，在商业葡萄园中拍摄了带有霜霉病症状，有红蜘蛛症状和无症状的葡萄冠层RGB图像。使用计算机视觉技术制备图像以增加疾病的视觉特征。最后，深度学习用于训练能够区分这三个类别的叶子图像的模型。通过使用保留验证，同时对具有霜霉病，红蜘蛛和无症状的叶子进行分类，可获得高达0.94的精度（F1分数为0.94）。此外，在病虫害的二元分类中，获得的准确度在0.89至0.91之间（F1得分在0.89至0.91之间），在区分霜霉病和红蜘蛛症状方面获得了最佳结果。如此高的准确性证明了深度学习和计算机视觉技术对田间条件下拍摄的葡萄叶片图像进行分类，自动发现能够区分具有红蜘蛛症状，霜霉病症状和没有任何症状的叶片的复杂特征的有效性。