Measurement of Early Disease Blueberries Based on Vis/NIR Hyperspectral Imaging System

Abstract

Blueberries, which are rich in nutrition, are susceptible to fungal infection during postharvest or storage. However, early detection of diseases in blueberry is challenging because of their opaque appearance and the inconspicuousness of spots in the early stage of disease. The goal of this study was to investigate the potential of hyperspectral imaging over the spectral range of 400–1000 nm to discriminate early disease in blueberries. Scanning electron microscope observation verified that fungal damage to the cellular structure takes place during the early stages. A total of 400 hyperspectral images, 200 samples each of healthy and early disease groups, were collected to obtain mean spectra of each blueberry samples. Spectral correlation analysis was performed to select an effective spectral range. Partial least square discrimination analysis (PLSDA) models were developed using two types of spectral range (i.e., full wavelength range of 400–1000 nm and effective spectral range of 685–1000 nm). The results showed that the effective spectral range made it possible to provide better classification results due to the elimination of the influence of irrelevant variables. Moreover, the effective spectral range combined with an autoscale preprocessing method was able to obtain optimal classification accuracies, with recognition rates of 100% and 99% for healthy and early disease blueberries. This study demonstrated that it is feasible to use hyperspectral imaging to measure early disease blueberries.

Maturity Stage Discrimination of Camellia oleifera Fruit Using Visible and Near-Infrared Hyperspectral Imaging

“…Hyperspectral imaging (HSI) has been a powerful tool for the comprehensive analysis of agricultural and food production that integrates spectroscopy and machine vision to collect both spectral and spatial information from one target [ 15 , 16 , 17 , 18 ]. HSI represents the spectra at each pixel of an image, utilizing the advantages of near-infrared spectroscopy, conventional imaging, and even multispectral imaging techniques.…”

Classification and Identification of Pinecone Mulching in Blueberry Cultivation Based on Crop Leaf Characteristics and Hyperspectral Data

“…In this study, the spectral data obtained through contact-based methods allowed for rapid and bulk data collection compared to conventional physiological measurement methods. In the future, there is a need to utilize non-contact methods such as hyperspectral imaging to improve and support blueberry cultivation [73][74][75][76]. Currently, the high cost and difficulty in portability of equipment present limitations, and individual measurements in the field are challenging.…”