In:
Journal of Food Process Engineering, Wiley, Vol. 45, No. 3 ( 2022-03)
Abstract:
The quality of the strawberry appearance is the most important indicator for consumers and modern fruit process engineering, which is closely associated with the ripeness and diseases. The continuous development of profound deep learning has greatly helped the recognition of strawberry appearance quality, granting a vigorous tool of relative precise outcomes, but the better performance of deep learning requires more time and more computation for training. This paper analysis the efficiency of different combination models that utilize deep feature plus classifiers for recognizing strawberry. The six convolutional neural networks are used to extract the deep feature and then the feature is imported into eight classifiers for classification. The average accuracy of six combination models is 1.53% higher than transfer learning. Besides, a method combining ResNet101 plus linear discriminant analysis (LDA) is proposed. The results show that the accuracy of the proposed method is 96.55%, which is superior to the transfer learning. Moreover, the training time of the method is 57.70 s faster than transfer learning. Therefore, this method has positive significance for the development of recognizing strawberry appearance quality. Practical Applications The goal of our study is to make it easier for farmers to accurately classify the appearance of strawberries in real situations. This paper analysis the efficiency of different combination models that utilize deep feature plus classifiers for recognizing strawberry. Meanwhile, we have proposed a method combining ResNet101 with linear discriminant analysis (LDA), which obtain the classification task more accurate and efficient. This method has an important role in recognizing strawberry, which will facilitate the development of efficient sorting equipment and provide a viable strategy for future smart sorting methods. Therefore, this work has practical application value.
Type of Medium:
Online Resource
ISSN:
0145-8876
,
1745-4530
Language:
English
Publisher:
Wiley
Publication Date:
2022
detail.hit.zdb_id:
2175259-X
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