Moisture content assessment of dried Hami jujube using image colour analysis
2022
Benxue Ma | Cong Li | Yujie Li | Wenxia Wang | Guowei Yu | Wancheng Dong | Yuanjia Zhang
To investigate the feasibility of image colour information in predicting the moisture content of dried Hami jujube, the images were obtained under different colour space models, and the colour model component mean and chromaticity frequency sequences of R, G, B, H, S, V, L*, a* and b* were extracted through image analysis. After optimising the colour model component mean and chromaticity frequency sequence, the model was established and compared. The results showed that the GA-ELM (genetic algorithm - extreme learning machine) model established by CARS (competitive adaptive reweighted sampling) method to optimise 12 chromaticity features of S chromaticity frequency sequence had the best prediction effect, with Rc of 0.917, Rp of 0.934 and residual predictive deviation (RPD) of 2.507. Therefore, the colour image information can accurately predict the moisture content of dried Hami jujube.
Afficher plus [+] Moins [-]Informations bibliographiques
Cette notice bibliographique a été fournie par Directory of Open Access Journals
Découvrez la collection de ce fournisseur de données dans AGRIS