Coconut shows unique physiological characteristics and application value at different developmental stages, and accurate identification and classification of its internal developmental stages are important for research and planting management. With the rapid development of CT nondestructive testing and artificial intelligence technology, developmental monitoring based on the internal morphological characteristics of coconuts has become possible. However, limited by the limited number of coconut CT samples, the recognition accuracy of existing classification models for internal developmental stages is significantly restricted. In this study, a method for classifying multiple developmental stages of coconuts based on few-shot image generation is proposed. Firstly, an improved few-shot image generation model FastGAN-Pro is proposed, which is capable of generating higher-quality CT images of coconuts at different developmental stages with a small amount of training data. On this basis, a multi-developmental stage classification method based on migration learning is proposed based on two pre-trained models, VGG16 and ResNet18. The experimental results show that compared with the baseline model, the FID values of FastGAN-Pro are reduced by about 21.08% on average, and the generated images are more visually. • FastGAN-Pro boosts coconut CT image realism and quality in few-shot scenarios. • Classification accuracy improves by up to 14.16% with best-sized augmentation. • Method enables reliable multi-stage coconut development monitoring via CT.
Memhmood et al. (Sun,) studied this question.