Maturity Grading and Identification of Camellia oleifera Fruit Based on Unsupervised Image Clustering

Maturity grading and identification of are prerequisites to determining proper harvest maturity windows and safeguarding the yield and quality of Camellia oil. One problem in production and research is the worldwide confusion regarding the grading and identification of fruit maturity. To solve this problem, a fruit maturity grading and identification model based on the unsupervised image clustering model DeepCluster has been developed in the current study. The proposed model includes the following two branches: a maturity grading branch and a maturity identification branch. The proposed model jointly learns the parameters of the maturity grading branch and maturity identification branch and used the maturity clustering assigned from the maturity grading branch as pseudo-labels to update the parameters of the maturity identification branch. The maturity grading experiment was conducted using a training set consisting of 160 fruit samples and 2628 fruit digital images collected using a smartphone. The proposed model for grading fruit samples and images in training set into the following three maturity levels: unripe (47 samples and 883 images), ripe (62 samples and 1005 images), and overripe (51 samples and 740 images). Results suggest that there was a significant difference among the maturity stages graded by the proposed method with respect to seed oil content, seed soluble protein content, seed soluble sugar content, seed starch content, dry seed weight, and moisture content. The maturity identification experiment was conducted using a testing set consisting of 160 fruit digital images (50 unripe, 60 ripe, and 50 overripe) collected using a smartphone. According to the results, the overall accuracy of maturity identification for fruit was 91.25%. Moreover, a Gradient-weighted Class Activation Mapping (Grad-CAM) visualization analysis reveals that the peel regions, crack regions, and seed regions were the critical regions for fruit maturity identification. Our results corroborate a maturity grading and identification application of unsupervised image clustering techniques and are supported by additional physical and quality properties of maturity. The current findings may facilitate the harvesting process of fruits, which is especially critical for the improvement of Camellia oil production and quality.