Wheat Hardness Prediction Research Based on NIR Hyperspectral Analysis Combined with Ant Colony Optimization Algorithm

• Published in: Procedia engineering Vol. 174; pp. 648 - 656
• Main Authors: Zhang, Hongtao, Gu, Bo, Mu, Jianru, Ruan, Pengju, Li, Dewei
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 2017
• Subjects: Ant Colony Optimization(ACO); hardness prediction; the near infrared (NIR) hyperspectral; the optimized selection of wave band; hardness prediction; the near infrared (NIR) hyperspectral; Ant Colony Optimization(ACO); the optimized selection of wave band
• ISSN: 1877-7058; 1877-7058
• DOI: 10.1016/j.proeng.2017.01.202

This paper presents a new and improved method that ant colony optimization (ACO) algorithm is combined with the support vector regression for band selection. The method is applied to the prediction research of wheat grain hardness, and tries to detect the feasibility of the forecasting ability. The optimized selection of characteristic wave band is the key link of the near infrared (NIR) hyperspectral analysis technology of wheat hardness. Experimental results showed that eleven characteristic wave band sub-intervals were selected from thirty spectral intervals by the algorithm, including 86 wave points. The selected wave band sub-interval were respectively 902.1 to 931.8 nm, 968.7 to 1027.5 nm, 1119.0 to 1143.4 nm, 1174.1 to 1275.5 nm, 1174.1 to 1275.5 nm, 1626.0 to 1647.6 nm and 1626.0 to 1647.6 nm. After using the optimized parameter in the spectral information forecasts and analyzes by the support vector regression. Prediction performances of regression models are assessed by calculating the estimated root mean square errors of cross-validation(RMSECV) the root mean square errors of prediction (RMSEP) and the correlation coefficient(R). The results showed that the estimated RMSECV and Rcv values were respectively 0.0382, and 0.9810 for the training set, the estimated RMSEP and RP values were respectively 0.0590, and 0.9544 for the validation set. Compared with the full spectrum of partial least squares (PLS), interval partial least squares (IPLS) algorithm, it simultaneously reduces the number of certain variables used in the model and increases in the prediction ability and the precision, and it can better reflect optimization model of the wave band. It is confirmed that the ACO method applied to the prediction research of the grain kernels is feasible.