Abstract
Maturity degree and quality evaluation are important for strawberry harvest, trade, and consumption. Deep learning has been an efficient artificial intelligence tool for food and agro-products. Hyperspectral imaging coupled with deep learning was applied to determine the maturity degree and soluble solids content (SSC) of strawberries with four maturity degrees. Hyperspectral image of each strawberry was obtained and preprocessed, and the spectra were extracted from the images. One-dimension residual neural network (1D ResNet) and three-dimension (3D) ResNet were built using 1D spectra and 3D hyperspectral image as inputs for maturity degree evaluation. Good performances were obtained for maturity identification, with the classification accuracy over 84% for both 1D ResNet and 3D ResNet. The corresponding saliency maps showed that the pigments related wavelengths and image regions contributed more to the maturity identification. For SSC determination, 1D ResNet model was also built, with the determination of coefficient (R2) over 0.55 of the training, validation, and testing sets. The saliency maps of 1D ResNet for the SSC determination were also explored. The overall results showed that deep learning could be used to identify strawberry maturity degree and determine SSC. More efforts were needed to explore the use of 3D deep learning methods for the SSC determination. The close results of 1D ResNet and 3D ResNet for classification indicated that more samples might be used to improve the performances of 3D ResNet. The results in this study would help to develop 1D and 3D deep learning models for fruit quality inspection and other researches using hyperspectral imaging, providing efficient analysis approaches of fruit quality inspection using hyperspectral imaging.
Highlights
Strawberry, a kind of fruit cultivated worldwide, is favored by consumers due to the unique characteristics, such as characteristic aroma, sweetness, and rich in nutrition
The results showed that the performances of 1D Residual Network (ResNet) for solids content (SSC) determination were not good enough compared with previous studies (Amodio et al, 2017; Chen et al, 2017; Shen et al, 2018; Mancini et al, 2020; Weng et al, 2020)
Both 1D spectra and 3D hyperspectral images were used to establish the ResNet models for maturity degree identification, and 1D spectra were used for the SSC estimation
Summary
Strawberry, a kind of fruit cultivated worldwide, is favored by consumers due to the unique characteristics, such as characteristic aroma, sweetness, and rich in nutrition. Maturity is an important quality index of fruits, related to eating quality, harvest, storage, and trade. It has short shelf-life, and strawberries with different maturity degree have different shelf-life. Matured strawberries have the shortest shelf-life (Rahman et al, 2014). Immature strawberries can become mature and over-mature quickly. They are vulnerable to physical damage, especially for the matured ones (Aliasgarian et al, 2015). Nearly-mature strawberries are harvested and stored for trade. Exploring the appropriate maturity degrees for harvest is of importance for the growth management, storage, and trade
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