An innovative fusion feature method of spectrum and visual image for diagnosing ‘Akizuki’ pear cork spot disorder

Abstract

Cork spot disorder is a common physiological disease that severely affects the quality and yield of the ‘Akizuki’ pear. In this study, we propose a novel method, Near-infrared spectra and Visual images with Neural Architecture Search (NV-NAS), to achieve a highly accurate diagnosis of cork spot disorder. This method automatically generates fusion features by combining near-infrared spectral and visual image features and then searches for the best-performing model structure. The best result was obtained with near-infrared spectral features preprocessed by first derivatives (FD) and the visual image features extracted by DenseNet121 in the NV-NAS method. On the validation set, the accuracy was 95.5%, and on the test set, the accuracy was 88.2%. Compared to the modeling results of a single feature, the accuracy improved by 26.3% over spectra and by 11.2% over images on the test set. Additionally, in experiments with models of different depths, deep image features were more conducive to feature fusion, with accuracy improvements of 21.7% and 30.1% compared to features from the middle or bottom layers, respectively. The results indicate that the model built on the fusion feature shows high generalization performance and holds significant reference value for the more accurate diagnosis of the ‘Akizuki’ pear cork spot disorder.