A comprehensive recognition of crop diseases is still quite challenging in numerous parts of the world because of inadequate facilities. However, computer vision-based deep learning strategies show a promising way for smartphone-assisted crop disease determination. This study focuses on the classification of specific mushroom diseases. Mushroom is a demandable crop, contains very high food value, and is extensively used in cooking various cuisines. This chapter develops a deep learning strategy to correctly classify the specific mushroom diseases. Data of raw images have been collected from different mushroom farms, and afterward, potential factors are purified via a computer vision approach. Three different types of deep learning architectures, AlexNet, ResNet15, and GoogLeNet, have been used to investigate the system accuracy, precision, recall, and F1-score using a dataset containing four different diseases of mushrooms. The dataset contains about 2536 images which were divided into 80% of the training set and 20% of the testing set. It has been found that ResNet15 provides the highest accuracy of 0.9% in detecting mushroom diseases.