Detection of early decay in peaches by structured-illumination reflectance imaging

Abstract

Peaches are susceptible to fungal infection after harvest, and detection of the symptom at early stage is critical to reducing economic loss for the industry, but is challenging because the symptom is not visible at the surface of infected fruit. This research was therefore aimed to develop a non-destructive and accurate method, based on structured-illumination reflectance imaging (SIRI), for detection of early fungal infection in peaches. Patterned spectral images for seven wavelengths between 690 nm and 810 nm were acquired from 600 peaches of different decayed levels, using a multispectral SIRI system, under sinusoidally-modulated illumination at the spatial frequencies of 60, 100 and 150 m−1. The resultant direct component (DC) images, which are equivalent to images acquired under uniform, diffuse illumination, could not reveal the slightly diseased symptom for peaches, but the symptom was visible from the alternating component (AC) images and ratio images calculated from the AC and DC images for the three frequencies. Watershed algorithm and partial least squares discriminant analysis were used for classification of diseased peaches based on the AC and ratio images, which achieved detection rates in the range of 65%–87%. Consistently better detections of diseased peaches were achieved with the AC images at the wavelength of 730 nm and the spatial frequency of 100 m−1. The pixel-based convolutional neural network for the AC images of 730 nm and 100 m−1 frequency achieved an excellent detection rate of 98.6% for all peach samples, and it also demonstrated superior performance for detecting early decayed peaches with non-visible disease infection symptom at a detection rate of 97.6%. For comparison, detection rates of diseased peaches by the three classification methods for the DC images were consistently lower. This study has shown that SIRI, coupled with an appropriate image classification method, can be effective for early disease detection of peaches.