College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 310058, China.
Key Laboratory of Spectroscopy Sensing, Ministry of Agriculture, Hangzhou 310058, China.
Sensors (Basel). 2018 Jun 15;18(6):1944. doi: 10.3390/s18061944.
Mildew damage is a major reason for chestnut poor quality and yield loss. In this study, a near-infrared hyperspectral imaging system in the 874⁻1734 nm spectral range was applied to detect the mildew damage to chestnuts caused by blue mold. Principal component analysis (PCA) scored images were firstly employed to qualitatively and intuitively distinguish moldy chestnuts from healthy chestnuts. Spectral data were extracted from the hyperspectral images. A successive projections algorithm (SPA) was used to select 12 optimal wavelengths. Artificial neural networks, including back propagation neural network (BPNN), evolutionary neural network (ENN), extreme learning machine (ELM), general regression neural network (GRNN) and radial basis neural network (RBNN) were used to build models using the full spectra and optimal wavelengths to distinguish moldy chestnuts. BPNN and ENN models using full spectra and optimal wavelengths obtained satisfactory performances, with classification accuracies all surpassing 99%. The results indicate the potential for the rapid and non-destructive detection of moldy chestnuts by hyperspectral imaging, which would help to develop online detection system for healthy and blue mold infected chestnuts.
霉变是导致板栗品质下降和产量损失的主要原因。本研究采用近红外高光谱成像系统在 874-1734nm 光谱范围内检测板栗青霉病引起的霉变。首先采用主成分分析（PCA）得分图像定性直观地区分霉变板栗和健康板栗。从高光谱图像中提取光谱数据。采用连续投影算法（SPA）选择 12 个最佳波长。采用人工神经网络，包括反向传播神经网络（BPNN）、进化神经网络（ENN）、极限学习机（ELM）、广义回归神经网络（GRNN）和径向基神经网络（RBNN），使用全光谱和最佳波长建立模型来区分霉变板栗。使用全光谱和最佳波长的 BPNN 和 ENN 模型获得了令人满意的性能，分类准确率均超过 99%。结果表明，高光谱成像技术具有快速无损检测霉变板栗的潜力，有助于开发健康和青霉病感染板栗的在线检测系统。
