Abstract: In order to provide a theoretical basis for the online, rapid and nondestructive detection of apple diseases, hyperspectral imaging was adopted to study the nondestructive detection of diseases (mainly anthracnose, bitter pox disease, black fruit rot and leaf spot disease) in fruits of the apple cultivar ‘Hanfu’, which is widely planted in north China. The acquired hyperspectral images were used for segmentation of regions of interest and extraction of spectral information. Then, 10 feature wavelengths (502, 573, 589, 655, 681, 727, 867, 904, 942 and 967 nm) were extracted in the full wavelength range of 500–970 nm by successive projection algorithm (SPA1). Furthermore, three wavelengths (681, 867 and 942 nm) were selected out of these feature wavelengths by using this algorithm again (SPA2). Finally, the spectral data in the full wavelength range and at the feature wavelengths obtained after each selection step were used as input vector to build a linear discriminant analysis (LDA) model, a support vector machine (SVM) model and a BP artificial neural network (BPANN) model for the detection of diseases in apple. Analysis of the test results revealed that SPA2-BPANN was finally chosen as the best detection method, providing a correct detection rate of 100% for both training validation sets. Our results show that hyperspectral imaging allows effective detection of diseases in apples, and the characteristic wavelength obtained can provide a reference for the development of multispectral imaging for apple quality detection and classification system.

Key words: hyperspectral imaging, successive projections algorithm, BP artificial neural network, apple disease, nondestructive detection