外文摘要：This paper presents a novel approach for predicting the ripeness parameters of Mandarin oranges (Citrus reticulata Blanco cv. Batu 55) using a Deep Convolutional Neural Network (DCNN) regression model. The proposed method leverages the combined reflectance-fluorescence images of the fruit. The use of reflectancefluorescence imaging enables the extraction of more features and information, which contributes to the accurate assessment of fruit ripeness. A comparison between the prediction model using a single Red -Green -Blue (RGB) image from both measurement modes and a combination image from both modes has been made. CNN regression model images with XSE_ResNet50 architecture to predict soluble solid content (SSC), acidity, firmness, and brix-acid ratio. Experimental results demonstrate that the fluorescence image (3 channels input) and the combined reflectance-fluorescence image (6 channels as input) accurately predict maturity parameters. The fluorescence image combined with DCNN performed well in predicting acidity and brix-acid ratio with R2 training/testing of 0.83/0.78 and 0.94/0.88 consecutively. For the SSC and firmness prediction, the reflectancefluorescence (6 channels) images provide the best accuracy with R2 testing of 0.86/0.81 and 0.91/0.84. This accuracy is higher compared to the conventional image features-based model. Overall, these results show that reflectance-fluorescence images in a visible wavelength regime combined with the DCNN model could enhance the prediction accuracy of citrus maturity parameters.
外文关键词：Machine vision;Reflectance-fluorescence;Six-channels;CNN regression;Ripeness
作者：Al Riza, Dimas Firmanda;Ikrom, Abdul Mulukul;Tulsi, Ahmad Avatar;Hendrawan, Yusuf
作者单位：Univ Brawijaya
期刊名称：SCIENTIA HORTICULTURAE
期刊影响因子：0.0
出版年份：2024
出版刊次：331
原文传递申请：江苏省科技资源（工程技术文献）统筹服务平台