Age Prediction of Komatsuna using Hu Moments with Neural Networks for Small Datasets

Moeri Okuda, Shinsaku Hiura

Abstract: The use of large-scale models such as neural networks has been increasing in recent years. When using NN, it is generally necessary to prepare a large amount of data. However, collecting a large number of images for plants poses a challenge. Therefore, we demonstrate the effectiveness of combining a geometric feature with NN, even with a small dataset. This paper proposes a method for predicting the age of komatsuna leaves using a combination of a Neural Network model and Hu moments that have traditionally been used as geometric features in computer vision. We used ViT and ResNet as Neural Network models. The results show that Hu moments with NN consistently achieve higher accuracy than only NN across different dataset sizes. Specifically, Hu moments with ViT attained 96.3% accuracy, exceeding the 84.2% of the ResNet-based model relying solely on image features. As shown in Figure 1, the advantage of incorporating Hu moments with NN remains evident regardless of the dataset size.

External IDs:dblp:conf/mva/OkudaH25