Shin, Jaemyung2020-04-162020-04-162020-04-16http://hdl.handle.net/10222/78584This research proposed the algorithm, that can detect powdery mildew and give the highest classification accuracy (CA). Three image processing and two machine learning algorithms (artificial neural network; ANN and support vector machine) were used to find the optimal combination for different image resolutions. Also, data augmentation by using a rotation technique was carried out to simulate the real-world field-situation. The results after data augmentation tended to be underfitting due to the added directional parameter. The results showed the highest CA of 94.34% for the combination of speeded-up robust features and ANN at 908×908 image size. To get better performance, six convolutional neural network algorithms were compared after data augmentation. ResNet-50 as the highest CA, AlexNet as the shortest computation time, and SqueezeNet-MOD2 as the smallest memory would be recommended in the conclusion.enpowdery mildewmachine learningdeep learningdisease detection