This paper presents a computer vision application of transfer learning in the detection of â€˜Tungroâ€™ among rice plants, using pre-trained deep convolutional neural networks. An AlexNet network, consisting of 5 convolution layers and 3 fully connected layers of neurons, was customized and fine-tuned to accommodate as inputs, images of rice plants representing two (2) classes: those afflicted with Tungro, and those that are healthy. The fine-tuned network was trained on five hundred twenty (520) images of rice plants, three hundred sixty-eight (368) of which belong to the group without infestation, and one hundred fifty-two (152) are infested with Tungro. Both the training and testing dataset-mages were captured from rice fields around the district and validated by technicians in the field of agriculture. Applying stochastic gradient descent as the learning algorithm, the two-class classifier achieved a very high accuracy of 98.17% at mini batch size of twenty (20) and learning rate of 0.0001.