In this study, Deep Learning (DL) was used to detect powdery mildew (PM), persistent fungal disease in strawberries to reduce the amount of unnecessary fungicide use, and the need for field scouts. This study optimised and evaluated several well-established learners, including AlexNet, SqueezeNet, GoogLeNet, ResNet-50, SqueezeNet-MOD1, and SqueezeNet-MOD2. Data augmentation was carried out from among 1450 healthy and infected leaf images to prevent overfitting and to consider the various shapes and direction of the leaves in the field. A total of eight clockwise rotations (0°; the original data, 45°, 90°, 135°, 180°, 225°, 270°, and 315°) was performed to generate 11,600 data points. Overall, the six DL algorithms that were used in this study showed on average of >92% in classification accuracy (CA). ResNet-50 gave the highest CA of 98.11% in classifying the healthy and infected leaves; however, considering the computation time, AlexNet had the fastest processing time, at 40.73 s, to process 2320 images with a CA of 95.59%.When considering the memory requirements for hardware deployment, SqueezeNet-MOD2 would be recommended for PM detection on strawberry leaves with a CA of 92.61%.

在这项研究中，深度学习（DL）用于检测白粉病（PM），草莓中的持久性真菌病，以减少不必要的杀真菌剂使用量以及对田间侦察员的需求。这项研究优化和评估了一些知名的学习者，包括AlexNet，SqueezeNet，GoogLeNet，ResNet-50，SqueezeNet-MOD1和SqueezeNet-MOD2。从1450张健康和受感染的叶片图像中进行了数据增强，以防止过度拟合并考虑田间叶片的各种形状和方向。总共进行了八次顺时针旋转（0°；原始数据分别为45°，90°，135°，180°，225°，270°和315°），以生成11,600个数据点。总体而言，本研究中使用的六种DL算法显示出的分类准确率（CA）平均超过92％。ResNet-50给出了98的最高CA。对健康叶片和受感染叶片进行分类的比例为11％；但是，考虑到计算时间，AlexNet能够以40.73 s的最快处理时间处理2320张图像，CA为95.59％。考虑到硬件部署的内存要求，建议使用SqueezeNet-MOD2来检测草莓叶上的PM。具有92.61％的CA。