Radish Growth Stage Recognition Based on GAN and Deep Transfer Learning

Abstract

Image recognition of plant growth states provides technical support for crop monitoring; this reduces labor costs and promotes efficient planting. However, difficulties in data collection, the required high levels of algorithm efficiency, and the lack of computing power resources create challenges to the development of intelligent agriculture. As a result, a deep transfer learning algorithm is proposed in this paper. The main motivation for this study was the lack of a dataset of plant growth stages. The key idea was to collect radish growth stage images in an experimental field using standardized equipment and to generate more images using DCGAN. By improving the deep transfer learning model, radish growth stages can be identified much more accurately. In this study, five different deep migration models were selected, namely, Inception-v3, MobileNet, Xception, VGG-16, and VGG-19. Our experiment demonstrated that Inception-v3 was the most suitable model for the recognition of plant growth states. Based on Inception-v3, we propose three improved models. The test accuracies for the radish and Oxford Flower datasets were 99.5% and 99.3%, respectively. Additionally, the accuracy of the pest and disease dataset also achieved excellent performance, with an accuracy of 94.7%, 2.4% higher than previously. These results demonstrate the wide applicability of our model and the rationality of constructing a radish growth stage dataset.

Keywords

• plant growth stage
• deep transfer learning
• convolutional neural network
• DCGAN