Convolutional neural network application to plant detection, based on synthetic imagery

Deep convolutional neural networks (DCNN's) have shown great value in approaching highly challenging problems in image classification. Based on the successes of DCNNs in scene classification and object detection and localization it is natural to consider whether they would be effective for much simpler computer vision tasks. Our work involves the application of a DCNN to the relatively simple task of detecting weeds in lawn grass. We looked at the effects of the choice of CNN hyper-parameters on accuracy and training convergence behavior. In order to obtain a large labeled set of interesting data we generated realistic synthetic imagery. Since our problem is somewhat constrained we were able to run thousands of training experiments and do accurate estimation of the probability density function of the convergence rate. Our results suggest that the use of realistic synthetic imagery is an effective approach for training DCNNs, and that very small DCNNs can be effective for simple image recognition tasks.