The prediction of soluble solids content of fuji apples based on feature fusion
To address the problems of high cost and portability of traditional apple nondestructive testing methods,different features of RGB images of Fuji apples are used to predict their soluble solids content.The color features,texture features and local features of apple images are extracted by statistical methods and convolutional neural networks,and the prediction results are obtained by splicing the above features and training regression models using fused features.The results show that the prediction coefficient of determina-tion R2p=0.6557 for the model based on fused features is better than that of the model based on single fea-tures.
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