Flower Classification with Convolutional Neural Networks

Computer software that is able to identify the plant type given the image can be very beneficial tool in the area of botany, horticulture and agriculture. Besides improving the research possibilities in the mentioned fields, it can be used as entertaining learning tool or it can be applied to other similar domains. Flower classification can be quite challenging task, since the majority of flowers have highly similar main features. This paper proposes using convolutional neural networks for flower classification. The first step in this research was preparing the dataset for network training. Numerous of network models were implemented during this research, but the main focus is on LeNet and AlexNet models. AlexNet model with Sigmoid Uniform function for allocating initial weights provided the best classification results in this research. Network training, as well as the data preprocessing was conducted in deeplearning4j Java library.